> Even worse, it's not a fair comparison: they purposefully just used "adaptive" instead of "max" for Fable.
We agree models should be compared on a fair basis. Unfortunately, adaptive was the only publicly available number. Anthropic doesn't generally let us run their models for evals, so we rely on whatever Anthropic or third parties have published. In this case, the Agents' Last Exam leaderboard has Fable Adaptive, but not Fable Max.
Would have loved to publish a full curve for Fable if anyone makes the data available.
Although we do bias toward publishing evals where we're ahead, we have historically been unafraid to publish evals where we're behind (e.g., GDPval). The point is give people useful information to decide what's best, not to trick people.
Ok long time Claude Code user here; lately I've started to realize there's other great models out there I should be trying, but I'm hesitant to leave Claude Code behind for something new.
What's the consensus today on codex vs claude code, does it really matter anymore?
Codex has arguably been better than Claude Code for months now, but it's flown under the radar because it just didn't capture the same viral marketing effect and OpenAI in general has had more optics / PR issues than Anthropic amongst the online developer crowd. I use the word "better" not in the sense that the underlying GPT models are fundamentally smarter or more intelligent, but rather that as a product Codex is just simpler, cheaper, and abundantly reliable and low-drama.
Agreed. GPT 5.5 will come up with more straightforward solutions with far fewer tokens than Claude. Also, the usage limits are much more generous for Codex than Claude Code for the same monthly plan.
My experience has been that Claude/Opus will ask me questions, although sometimes I have to step in and redirect a bit. Codex/GPT just dives in and cranks code out, which after using Claude for a year I found rather concerning. It wrote working code though. Claude/Fable does something similar (asks far fewer questions than Opus) fwiw.
I've been using both and as far as I can tell with ccusage, the $ equivalent budgets are about the same between them now. This may have been true before anthropic doubled their quotas and openai 2x promo expired last month.
Nudged by this thread, I've decided to switch from Claude to Codex for a bit to see what happens. But...I immediately became lost in their marketing vortex of confusion on plans and pricing. Anyone care to tell me which plan I should be using? On the other side I use the $100 Claude Code plan. We actually have a "Business" ChatGPT subscription already, which seems to be $50/mo/seat. OpenAI's web site offers a set of individual subscriptions (for parity with CC presumably) which I suspect weren't available when we signed up for ChatGPT. I think that in turn happened due to some web site feature it didn't allow for free users (uploading PDFs, something like that). Perhaps I should switch from that business account to an individual subscription for Codex?
Test-drive it with an individual Pro account (5x or 20x) for a month. Download the Codex CLI client from https://github.com/openai/codex and auth it in the browser via the URL it provides. Set the model to 5.6-Sol and effort to max.
Ok thanks. Executed on that. I had it build a simple project (actually not so simple since it involved domain knowledge about snowpack and ice) and also gave the same prompt to Opus and to Fable. When I get some spare time I'll write an article highlighting the differences between all three.
That's a strange statement... It's been true for a while now that OpenAI has had much more generous limits than Anthropic on their subscription plans. And with the Fable ban/guardrails disaster, there has been a lot of frustration from people in these comment sections. And Anthropic fucked up Claude Code pretty badly for a couple of weeks during the 4.6/4.7/4.8 transition, which again was widely publicized. And they got a lot of flack over not allowing other harnesses anymore. And ChatGPT got some pretty viral wins on model intelligence when they cracked the high profile Erdos problem.
If anything the online optics have been bad for Anthropic for the last half year. OpenAI doesn't have optics issues, from my point of view they simply have the issue that they are the least trustworthy player at the frontier. The way they pivoted from their original mission is truly breathtaking, especially coming in gloatingly to take the government contract when Anthropic got kicked out for insisting the government does not use their systems for mass surveillance or autonomous weapons systems. You understand what that means, right? OpenAI models are now actively used/developed for mass surveilance and/or autonomous weapons systems.
I know there are plenty here who seem to value their own ability to use these models cheaply above all other considerations. Then OpenAI is a great choice, and much less restrictive than Anthropic. But their problem is not on the optics. It's on the substance.
I really want a good Claude Design competitor in Codex, it's hard to use the others after getting used to it and yet I find anthropic's model to have a much worse understanding of what looks good or not than OpenAI or Google models.
Honestly it’s the usage limits that are so generous that makes codex worth it even if it may not be exactly as powerful as Claude. The peace of mind that you can try a lot of things and make huge refactors and run extensive redundant tests without running out of tokens just makes the whole thing a much better experience. I tried coding with Deepseek and it was pretty terrible so the only reason codex works is because its abilities are close to or on par with Claude.
I agree with this statement. And because it churns less tokens, it's just generally faster too - noticeably, throughout the day, across a range of tasks I get more shit done with Codex.
It's not better at reasoning on complex coding tasks, Claude Opus is still ahead there, but not by a lot.
I'd say Codex and Claude Code have different strengths and weaknesses. Claude Code is significantly better in terms of their subagent UI for example - being able to see the list of subagents under the input is great.
To be honest though, I've gotten to the point where I prefer the OpenCode UI. A big win for OpenAI is you can log in to your subscription in OpenCode, whereas this is not trivially achievable for a Claude subscription.
I was getting some really impressive cost efficiency today in OpenCode with the following:
* Main session agent: gpt-5.6-sol (high) via OpenAI subscription
* General purpose subagent: deepseek-v4-pro (high) via OpenCode Go subscription
* Using `obra/superpowers` for subagent driven workflows
* The main session only being allowed filesystem read permissions and everything else delegated
It was absolutely crunching through tasks without hitting the limit, and this combination is quite cost effective.
GPT 5.6 was picking up on quality and functional issues from DeepSeek and having it resolve them cleanly, and I didn't even get close to my quotas whereas I can usually blast through them. I feel as people get more comfortable with subagents and mixing and matching models in their daily work, Anthropic's walled garden stance will start to hurt them.
Last time I tested Codex on a cheap plan, it barely lasted an hour? I think this was for the $20 plan. I was afraid to try the more expensive plan after that. Not sure, I might just outright rip my Claude Code bandaid if the current usage quotas do die off after the 17th or whatever date they said they would "return on".
I've been using Claude Code, Codex, Gemini (now Antigravity) at the same time for half year now, ever since I dipped my toe into agentic coding. I'd say in general Claude Code and Codex are equally powerful, Gemini is lagging behind.
One thing I appreciate with Codex is, OpenAI nowadays sometimes just gives you quota resets you can bank, so when you use up weekly quota before the week ends, you could just reset the quota, to continue using Codex. I've been much less anxious about Codex quota because of this perk. I just used one reset in the bank yesterday, and still have 3 resets left. Whereas with Claude, when you've used 95% quota 3 days before the week ends, you'd be much more anxious.
On the other hand, Claude Code's /remote-control mechanism is extremely helpful when I am running it in the cloud and wants to monitor it or control it on my phone. Codex currently doesn't support this kind of usage. Codex only allows you to use your phone to connect to a session on your desktop, not in the cloud.
I have used claude and codex extensively but only from their CLI app (heavily sandboxed using rootless podman, network filtering, etc), so I don't really know what I'm missing with the GUI apps.
One killer feature that Claude has, and AFAIK Codex still lacks, is the ability to start a session in the terminal and then hand it off (actually just remotely control it), from the iOS app.
Last time I tried Codex on iOS it required a ton of set up to link a github project etc. The way claude lets me remote into a session I've already started on my actual machine is much better IMHO.
They’ve addressed that. Codex in the ChatGPT app on iOS is way better than Claude Code now.
You sign in the Codex app on your Mac same on iOS and are able to completely control your sessions - fork, side chats, plugins - everything.
It’s really great i often work through it. And you can connect any number of Codex instances on any number of macs and then manage them all through the iOS app.
Hmm, I don't have a desktop computer. I prefer my laptop be used for other purposes, and can sleep when not in use, instead of running a coding agent 24x7. That's why I prefer running coding agents in the cloud.
You can do that too. Start working locally, then just do /handoff to transfer your session to the cloud and then work through the Codex app on your phone.
maybe I'm misunderstanding but I don't want to sign into the app on a mac - I want to run the CLI on a headless linux server and control the sessions from my iPhone. Does Codex allow that now?
1. Run `codex remote-control --help` directly on your Linux server.
2. From the desktop app, connect to your Linux box, start Codex there, and make it remotely controllable.
I do that with Termux on my Android phone. I set up dynamic DNS and wireguard on my router so I can reach my LAN from anywhere. I just enable wireguard and ssh in.
Not sure if there's a Termux equivalent for iPhone.
Free, OSS, pretty great for ssh via VPN => tmux a => codex/claude
I set up Codex to send a notification when done over Pushover (https://pushover.net). With this setup, you can just ssh into a Mac or Linux box either way.
Yes - Anthropic badly needs this same "here's a reset, use it when you want".
It's vastly better this way. Sure, it may impact the bottom line but it's a huge customer satisfaction win.
When Anthropic randomly resets me and I've only used 2%, that's worthless. When OpenAI tells me I have 3 resets available to use whenever I want - it's wonderful.
> One thing I appreciate with Codex is, OpenAI nowadays sometimes just gives you quota resets you can bank, so when you use up weekly quota before the week ends, you could just reset the quota, to continue using Codex.
I’ve found Codex’s overage to be much better value than Claude’s. A monthly $10 budget is plenty for my backup Codex usage, but on Claude Code that would be gone in a couple of days.
The banked resets have been a game changer for me. I've been sticking to 5.4 medium mostly because 5.5 seemed to eat into my quota significantly faster. The reset bank gave me confidence to seriously start using 5.5 high. Lo and behold, I have yet to actually need a reset, but I'm now less likely to explore non-OpenAI models since there's an escape hatch with new models in case a coding session gets a bit crazy.
In my projects, Claude writes and Codex reviews, and I've had a lot of code I've been very happy with out of that, although as of today, Grok _also_ reviews, and finds interesting new stuff.
Claude Code fan here... Codex is very good. Sometimes better. The killer feature is price.
After 6+ months of exclusive Claude Code usage, I was begrudgingly forced to try Codex once they rejiggered their limits such that I kept maxing out my $200/mo plan in just a few days. These days I pay both $200/mo plans, and it's just about enough to get me through a week's work (small game studio - infinite code to write!)
In terms of ability to ship? Easily tenfold. We literally ship 10 times more than before AI. This does not, however, translate into a tenfold increase in actual business success, of course :)
oh for sure! I think in some ways LLMs have raised the bar in terms of what you an expect from software (once we get past the hurdle of increased bugs, but that seems to be getting better)
I think at some point, AI development becomes a competition for who has the best taste [1], because with seemingly unlimited/cheap tokens, AI ends up being more about throwing shit at a wall and hoping something sticks.
Genuine question/not a critique-are you actually reviewing all that code or just sending it and hoping for the best? I just can't imagine someone is reading/reviewing that much code every day, but maybe I'm wrong?
Like before AI, the scrutiny varies with the sensitivity of the area being edited.
Simple UI change? I do an AI review, but otherwise neither read nor write the code. The models are good enough they write better UI code than me, 9 out of 10 times. Not always the more idiomatic, but usually safer and more correct.
Change to our core data plane? I might spend 2-3 times more effort reviewing it than before AI. Yes, I go more slowly than pre-AI. Many more reviews, many more angles considered, including both human and (lots of) AI review cycles.
Most code is not that critical, and AI is also scarily good at writing tests. We also spend considerably more time paying down tech debt and testing thanks to AI, now that the cost is near-zero.
Net: I spend 10-25X less time on low-risk changes. I often direct (or at least approve) the implementation approach, but I rarely read this code. I spend 2-3X more time on high-risk changes. In both cases, I never write code "by hand". Since about November, I've had no reason to actually edit code in a code editor (perhaps maybe except .env files, which we don't allow agents to edit for obvious reasons).
AI is a tool. You can use it to go fast recklessly, or you can use it to go slow with confidence. Just like before AI... the skill and art of engineering is knowing when to do which.
Indeed, much of the scariness is how fearlessly and confidently it writes them with little regard to their actual usefulness or value. When I find it adding a lot of tests, I often say something like: "audit each test carefully, and consider whether the test is testing a meaningful boundary or is more ceremonial. delete low-value tests and add new tests to cover meaningful boundaries not exercised by the gaps you identify". Without fail, this always produces some decent results.
Having said that, in truth, I almost never read the unit tests. Before AI, we had almost none (see: several person game studio) so the tradeoff is not "AI-generated tests" vs "human written ones", it's whether we have tests at all. So, I take them for what they're worth - not much - but if it catches an extra regression before it ships every now and then, it was worth it for the price (~free).
I notice that the tests are very often for its own benefit. Like, you’ll ask it to stop doing something, literally to just remove code, and it will write a test to verify the behavior is not there.
I can’t imagine a more useless test, but I get that it wants to verify that it actually made the change. I just delete the test when it’s done.
No. I used to use Cursor, but now my workflow is that I use an inhouse CLI tool I wrote called "bud" that wraps/seeds the harnesses per-worktree, and boots a full copy of the game so each worktree can work independently. If git worktrees solve the problem of code isolation, bud solves the problem of isolating everything else. It's about 15K lines of rust, and I use it 100 times a day or so. It's sort of a layer on top of a harness like codex/claude code.
I have 10+ of these workspaces in parallel, and I context switch between them as I get blocked on things. I manage the workspaces using `herder`, which is a terrific tmux-like tool that allows me to keep those workspaces on a nixOS machine I have at home that I SSH into via tailscale, so my agents don't stop working every time I close my laptop (it also lets me leverage that machine's computing resources instead of running dozens of servers and harnesses on my poor MacBook).
It's not clear replies to this thread aren't openAI employees or incentivized influencers, but every benchmark has gpt-5.5 underperforming opus 4.8, sometimes by as much as 10%.
Is there a known headcount for OpenAI influences v Anthropic influencers?
On threads like this, this site seems to be made of nothing but boosters for one or the other, with their emphatic professions of faith, all based on inscrutable, unverifiable inner experience. When no one bothers even to reflect what conditions a proper assertion would require, the discourse is pure faith propositions.
i’m not employed by either, but ideologically might be biased towards Anthropic. I find codex is absolutely better for intelligence/$. Fable is likely better, but codex honestly feels like a utility in the posix sense in that it is very fast/extensible/simple and the model seems less opinionated and more willing to just work directly towards your goals.
I sub both codex and claude at 20x. I like opus+fable more than gpt5.5 because it seems gpt tries to finish tasks by leaving any ambiguity unresolved. claude seems better at surfacing open questions.
This is using the same AGENTS.md prompts, which were designed firstly for Claude use, so maybe it's something that could be optimized better if I understood gpt as well?
no, I didn't get access to sol until a few hours ago. I just have my claude protocol files linked to inside codex. trying Sol this morning for the first time, so I can't really comment on that vs gpt5.5.
However you can do what you are asking "fable--> sol" you need to setup a mcp or have fable run a bash tool, just invoke the `codex.exe` cli tool with whatever cmdline args are needed.
Same here. I find the design, architecture, system design discussion to be better on Claude, but after Opus 4.6 I switched over to Codex for actual coding and love the results. I use both via the CLI and generally tell Claude to output the result of our decisions as a markdown that will be easy to read and implement by an agentic coding tool. Then I fire up Codex and read said markdown as the input of the session and way to build all the appropriate context needed. I see this as a way to step into letting the agents go run on their own and interact with each other, but I still like to steer so I put these manual steps in the flow. Letting the agents go off on their own and one shot big chunks is not reliable enough yet imo.
The answer is it depends. Claude's generally better at frontend and debugging tasks, while Codex is stronger at backend features and exploratory work. They have very different coding styles and thus very different strengths.
I use both. Not because I am cool, but because it is cost effective for personal projects with two $20 / month plans. It is also nice to be able to see what the state of the art is like for both.
Personally, I find it very interchangeable. I open codex --yolo or claude with whatever there yolo flag is (have an alias).
If you can afford to test it seriously, running both in parallel, it's worth a test to see which you prefer. If you can't, don't bother. You're not likely missing anything since they are close to personal preference with most people I know who have meaningfully tried both preferring Claude
> What's the consensus today on codex vs claude code, does it really matter anymore?
Consensus is probably the wrong word for the popular opinions reflected in HN that you might get.
I would recommend that you have 2 of each at all times when it comes to AI so you don't necessarily become overly locked to quirks of one thing. You'll soon realize that things move so fast that you just start internalizing common patterns instead of depending on one specific vendor.
I recommend that you try pi and codex besides claude, to get your own feel for it.
I can't tell the difference between Fable and GPT 5.5. I tried Fable while it was in trial $20 mode, used up my whole quota, and it was great, but as soon as I went back to GPT 5.5, everything was the same.
But what I love about Openai is that they still let you hook OTHER harnesses up to a subscription. My Pi setup has been built up for a few months now into exactly what I want and moving over to CC or even Codex is really annoying.
Caveat: I vibe code in tiny little chunks. I see what I want to do, and exactly how I want it done, then prompt that, refine, what was output, then repeat. I bet Fable is better at building a whole app from a 2-sentence prompt; but that's just not important to me at all.
Not sure about the consensus, but during an entire week I have done every task on my workplace with both Opus 4.8 and GPT 5.5. GPT won hands down. I would even sometimes copy the plans and solutions (using different Git worktrees) from GPT and paste it on Opus and itself would say GPT plans were better. At that point I have migrated. Fable is not enabled in our workspace so I have not tried.
Claude lost my trust around February this year when the plan would say nonsensical things as "delete this method" that was clearly a key method on that part of the codebase.
For personal projects I am using Codex 20$ plan and when that is over I use DeepSeek which is insanely good for the cost.
They're different models with different philosophies behind them. This is anecdotal with a user group of 1, but in my experience:
Claude has a stronger personality and is more creative. If you give it vague instructions, it's better at filling in the blanks with reasonable ideas.
GPT-5.5 is better at following instructions. If you know exactly what you want, it will do it without going off the rails. It's also less likely to imply that you're dumb, but I don't really care about that. Some people do.
I’ve found that Claude is very literal. When I talk to 5.5 it gets what i want it to do, when I talk to Opus 4.8 it does what I say literally and doesn’t get the intent behind it.
I wish models called me out more! I can’t count the number of times I’ve had an absolute shit plan, prompted it, and the model built it to a tee. Then I look at the code, and it’s an absolute mess, because it had to make my stupid idea work. Maybe I need a better system prompt.
My experience is that Codex's auto review is extremely costly, with $20 on both sides, I can run CC with auto mode for longer than with Codex's auto review enabled. Also in my own experience Claude's usage is actually bigger than Codex, but I am not sure if that's due to I stick to 5.5 with Codex while keep Sonnet as the default to orchestrate other models in CC.
I've subscribed to ChatGPT/Codex for over a year and tried a Claude sub twice 1 month each, with a gap of several months in between.
I tried them both side by side, mostly for reviewing existing Godot/GDScript code, or sometimes generating Swift Mac apps, including converting ancient artifacts I wrote in Visual Basic on Windows
I'm also a long-time Claude Code user here, though the last 3 weeks I've been doing loops having claude use codex to review until they reach consensus; uses tons of tokens but the result is really good.
I'm trying Codex as my primary the last day or so, because I'm at 98% use and reset in 3 days on Claude. I'm worried about a lot of our skills and CLAUDE.mds and the like getting lost unless I migrate them, but otherwise codex seems to be working great.
The harness is so much better than cc which is a buggy mess. Gpt is also way faster than Claude. I’ve been using gpt for a while now and I know a lot of people that swapped away from Anthropic for multiple reasons. However - fable still seems to be the best coding agent, it’s just slow and the harness sucks. So I still use it in some rare cases like to review codex. I’m hoping 5.6 lets me drop it entirely.
IME it entirely depends on your work. I find myself using both daily for different things.
Codex with GPT 5.5 is much better at general SWE tasks but Claude Code with Opus is far better at complex reasoning tasks like reading and summarizing research papers, replicating experiments, identifying research gaps and proposing interesting follow ups.
I recommend trying Codex too. In fact, I recommend running them side-by-side if you have the budget, e.g. have both independently plan the same feature or implement in a different worktree, or have them critique each other's work.
I personally find GPT-5.5 to be a better programmer than Opus 4.8, it is extremely thorough, but I don't like the code it generates ("austere"), and find Opus 4.8 to write more "human friendly" code. The programming comments GPT-5.5 makes is pretty awful where-as Opus 4.8 is good. I feel like Opus 4.8 is better at grasping my intention than GPT-5.5, and honestly find GPT-5.5 to be kind of "autistic". I do prefer the language (not the writing) of GPT-5.5, as I find the philosophical flowery language of Opus 4.8 kind of annoying.
I have only managed to try Fable 5 a little bit, which feels like a much more generally smarter version of Opus 4.8, that is much better a programming and grasping your intention, and I think even the intention of your code, and is _really_ good at spotting bugs or problems with logic in your code. It feels wicked smart but is extemely expensive. It feels smart in the sense like it has a "bigger brain" and is much more sensitive to subtleties/details.
These are different "brains", have different "personalities", etc. I think the best thing is to develop a feeling for it yourself.
I haven't tried Codex yet, but I for my tasks GPT-5.5 may correctly point to a proper direction but its code feels a bit weird. Opus 4.8 is way better in coding, and actually it's the only one who could catch very very sophisticated bug in a large codebase (I tried different models including GPT-5.5 and DeepSeek). Interestingly Gemma 4 under opencode running locally performs not bad at all, it's far yet from DeepSeek level, but it manages to understand tools quite well, and code quality is pretty good. So, for simple coding projects I can say local models already won. It's amazing how smart open models of desktop size have become today. I mean it's quite plausible to manage small codebase today relying on only open tools and local models, you don't need any subscription to produce high quality code, but yes I assume you already experienced and know what you're doing :)
I did the side by side between Claude code (effort medium) and Cursor (auto). Asked Claude to prepare a plan and asked Cursor to review it and it found tons of gaps in the plan. Cost-wise, it came out better too. I have been using Cursor daily (along with Claude) and the former has been 20-30% cheaper despite me spending more time on it.
I had great results combining the two. If you (or your employer) can afford then you can ping-pong the models in the plan phase (not really ping-pong as humans should get a say too) and then let one implement and the other review. I got better results working this way than just to stick to a single model.
like others said in the thread: much less drama and i'll add much less attitude from the company and the models, overall i'm having much calmer experience with codex, hope it stays that way
It honestly baffles me how people can ask a question like this and get such a wide spectrum of answers in response. It's all so much based on vibes and anecdotal evidence. I've not really noticed much of a difference in capability since Opus 4.6 and I've used a ton of different models. They all work pretty damn well for me.
Personally I use Open Code with a copilot sub. Then all models are available in my session with just a /model and /variants command combo. Makes it super low friction to try different models & combos (my favourite right now is DeepSeek V4 Flash for initial PRD then Fable 5 high for implementation).
I consistently have better results with Codex for the work that I do. People have been saying that for six months, but until 5.4 the experience was sufficiently slower that it wasn't worth the switch. Making the switch was frictionless. Give it a try
Set yourself up to be able to try / switch between models easily. I was a claude only user and just have my user level AGENTS.md for codex and others simply point at my user CLAUDE.md. Have a script that syncs my skills (just directories) between all models. Also, if you want to use /simplify or similar from claude in another model, you can ask claude for the prompt and put that in a skill for the other models.
Claude Code is not the model, it's the harness. You can use any model you want with Claude Code to varying degrees of success. I use Qwen3.6-27b daily with Claude Code as an example.
Now we have various Opus+ level models (Opus/Fable, Grok 4.5, GPT 5.6) I prefer to focus on price/speed and harness as models are all generally good enough for coding. (Fable is overkill for 90% of work but is still level above). So I use Grok Build with 4.5 as its VERY fast and cheap, Codex is next best for me with sol/lunar 5.6. and Claude Code Fable for the 10% of tasks that need that level of reasoning. However I find Claude Code harness responsiveness much less than other two (all TUI versions) I wish they would fix this.
Don't know about consensus, but I personally still find Opus to be better for sniffing codebase intent and checking things as a whole, while Codex seems more detail-oriented for individual files.
I use both. Both are great. But in terms of Desktop Apps I think Codex has the better UI. It's more straightforward, just works, and has small conveniences like the open in editor icon.
Claude's very bloated and convoluted by comparison. Maybe you need the bloat (Claude Design), but I prefer the more razor's edge efficiency of Codex.
Model wise, I can't really tell. They all do what I want them to do most of the time and go off the rails occasionally. The question is increasingly becoming who's faster and cheaper and gives me more tokens, not who's better.
I run my AI agent as a different user (in addition to using the sandbox functionality provided by cc/codex). It does not seem possible to run the Codex GUI as a different user. I can run the TUI (/Applications/Codex.app/Contents/Resources/codex) but it has the shortcoming that remote control is only available in the GUI.
I installed the Claude Code Codex skill provided by Anthropic and I am having Claude invoke it automatically to review all plans and changes. The nice thing about this is that for an additional $20/month pro plan I can extend the runway for Claude rate limiting and compare frontier model responses. I am looking for more ways now to work in Codex as a subagent that gets used automatically from Claude Code.
I had to switch to Opencode from Claude code because the latter wasn’t supporting GitHub Copilot as model provider.
I didn’t think I could have found a better solution, spawning multiple subagents with different models is such a great thing.
I built in the past very small cli wrappers to call other models; Claude Code often refuses to do that, lies and does the job itself instead of delegating to another provider’s llms.
My final answer on this is that we just can't say anything affirmative because all of our projects/codebases are completely different. I've gone back and forth on the "codex vs claude" being better, and while I'm currently of the believe that Claude is superior, I understand that might be the case for _my_ particular set of projects and _my_ personal way of interacting with the model.
I used to have the CC $200 plan, and moved to Codex 6 months ago. I have the anthropic $20 plan + API billing for rare use. Use Codex daily.
Not having to deal with Anthropics constantly changing policies, token-gating, and carrot-and-stick marketing helps me to focus on work, rather than dealing with their company problems.
IMO Codex has been the same rollercoaster ride as Claude. GPT 5.3-codex was incredible for backend/system tasks, GPT5.5 is better all rounder but weaker in some spots. There has also been many weeks when Codex's models were dumb AF. Same rollercoaster ride as anthropic between Opus 4.5 to 4.8...
IMO the two biggest problems not really being answered by both OpenAI and Anthropic are:
1. Why not make specific models good at specific tasks for Codex/Claude Code. Theres a handful of types of work here whereby small good quality models would do better than these generalised all purpose models whereby someone discovers Fable is bad at biology....
2. Why cant they consistently run these models and keep them performing? Performance of the models seems to directly correlate with amount of compute available, but they dont talk about it...
Codex has been comparable for a while. 5.1-5.5 have competed closely with 4.5-4.8. Fable blew them all out, now Sol comparable to Fable again. Some slight tooling differences with skills and hooks but for the most part I think if people are so engineered into one CLI that swapping to another inhibits them, then that is an error in usage habits.
Codex historically will follow tasks more closely with less creativity, whereas Opus will do more than you specify. I wouldnt consider either one better due to this fact, just makes them useful for different situations. Generally they'll perform similarly for most tasks.
Opus and Fable dominate 5.5 in artistic design (pixel art, ascii art), and edge out 5.5 slightly in general UI design taste. Have not tested Sol in that regard yet.
So far in my usage Sol has been superior to Fable at graphics rendering engine optimization.
Codex will work longer, and in single sessions without as much subagent usage.
Codex only has 256k context but its compaction is absolutely next level. You will not notice compactions and they will happen multiple times during a complex task or set of tasks without you ever having to notice or care. Claude code on the other hand still has fairly poor compaction.
Codex has more generous usage limits, and they also give you usage resets (weekly+5h resets) that you can bank for a month or so. Not sure how often they give these out.
Codex also seemingly never has outages or weird delays like Claude code does.
OpenAI randomly resets usage just like Anthropic does
OT but how are y'all sharing your skills and agents across harnesses?
I have a bunch of Claude Code Plugins and yesterday asked Codex to make them accessible to itself. It wanted to rewrite most of it. I was hoping i could get by with some symlinks or something to avoid drift.
It’s almost as complex as compiling software for different runtime environments; it requires a knowledge artifact repository and publishing system, e.g. https://github.com/thinkingsage/context-bazaar
In my opinion Opus is waaayy better in agentic orchestration. It feels like it can natively deal with multiple subagents whereas gpt needs to be taught extensively.
I do almost all my regular coding tasks with Codex 5.5 on medium. Sometimes for niche edge cases, or when I run out of tokens on my Codex sub, I'll switch to Claude. Some recent examples where Claude was able to solve things Codex couldn't:
- 3D gamedev layout: I asked Codex to render a solar system in a certain camera positioning, saying it needed to fit the planets of the system to the viewport. Codex just couldn't do it, even on high reasoning: Claude Opus did it first attempt.
- Tricky Tiptap image drag-n-drop layout implementation: Codex failed this after numerous iterations. Claude Opus also struggled mightily to get it to work, but I think around 3 attempts it nailed it. Both of them ended up grepping the Tiptap code from node_modules - that's the kind of task it was.
But these are really isolated examples. Across all my projects (I have many; mostly TypeScript, but also things like C#), Codex "Just Works" (tm), with minimal prompting effort from me.
A great thing about codex is that, even if run out of usage, it finishes the task. Claude code will abruptly break the work and leave it there unfinished as soon as it runs out of tokens. Also, antrophic randomly resets the token usage which is annoying when I’m trying to ration them. While openai gives you extra resets that you can apply when you want to
To me, the question of switching (and any recommendation) depends highly on the type of work you do as well as your setup in terms of harness and memory, context management, and so on.
I have my context managed in a structured (but nowadays way too big) Obsidian Vault. I also built myself a vector based "vault search" capability and have my harness use this as a tool to find thematically similar things across the different contexts, when needed. I also build a few custom skills and extensions for my harness to be able to do my work.
Talking about harness: I use pi.dev and have taken care of, that i set it up in a way as to easily be able to switch the intelligence layer without loosing context. Yes, there are differences in how well models perform, but if a model refuses a task - like gpt-5.5 not willing to build a downloading tool for Annas Archive - I switch the model to something less finicky.
Thus I was able to switch to gpt based models after about a year with Claude (and having had a Claude Max since the early days it was available).
I played a lot with other models recently, to see how stabl my setup is for switching, should something like Fable happen on a broader scale with the US government. As said, minor changes in tonality, minor issues ith the quality of long text being written by the model, but most of it is actually managed in by the tonality docs, guard rails, coding standards and the likes, I set up over the last 9+ months of intensive work with it (first in Claude Code, then Codex and now as said pi.dev).
So YMMV and it heavily depends on your setup. But I more and more treat those models as interchangable.
> What's the consensus today on codex vs claude code, does it really matter anymore?
I've just switched to Codex from Claude Code. There's some minimal setup in regards to the auto-reviewer if you'd want to preserve a similar functionaly with Claude Code's auto mode and bubblewrap (dev container environment).
Just point Codex at the code base and let it do a migration from Claude Code with a note to keep compatibility with it.
"On Agents’ Last Exam (opens in a new window), an evaluation of long-running professional workflows across 55 fields, GPT‑5.6 Sol sets a new high of 53.6, eclipsing Claude Fable 5 (adaptive reasoning) by 13.1 points. Even at medium reasoning, it beats Fable 5 by 11.4 points at roughly one-quarter the estimated cost. That efficiency extends to smaller models, which are essential to making intelligence more abundant and affordable: GPT‑5.6 Terra and GPT‑5.6 Luna outperform Fable 5 at around one-sixteenth the cost. "
Some pretty big claims and results! Excited to see how it feels during usage.
I use Fable and 5.5 extensively and I still find both have a place in my toolkit, i.e. Fable IS good but it isn't perfect, and it's still better to play them off against each other. I have Fable and 5.5 write plans and have them adversarially review each other's plans.
Having this amount of competition in the coding model space is good for all of us.
I think this is the phase shift 5.6 (Sol set to Ultra) is bringing to the table. Until now we have become accustomed to asking models to continue and their natural inclination is always to stop.
Now OpenAI have flipped it around and for the first time are asking us to steer or stop the model instead, and its own inclination is to keep going. We now have to decide when we need to steer or want to catch up on our understanding of the work done but it will keep going.
> GPT‑5.6 is priced per 1M tokens across three model sizes: Sol is $5 input / $30 output; Terra is $2.50 input / $15 output; and Luna is $1 input / $6 output.
Just as expensive as Fable 5. But of course, another slot machine upgrade but the costs will keep going up and the open weight models from china will continue to race everyone else to $0.
Looking forward to the next version of GLM, Qwen, Deepseek and Minimax.
Seeing how Anthropomorphic just reset usage quotas back to 0 and the other day extended Fable sub inclusion by a few days, I have a feeling they might not drop Fable out of sub after all, because like you I would most definitely take a long good look at codex at that point.
It's not just the API pricing either, there's also the constant uncertainty. They pull the model then put it back up, they say the model is going away then suddenly it's not. And then there's the fact Fable is barely usable because it randomly downgrades to Opus out of nowhere whenever it thinks about exploits.
It's definitely good that Anthropic's feeling the pressure. Anthropic has worn out their welcome with this "safety" nonsense. If OpenAI actually lets me use the LLMs on a subscription without any of this bullshit, I'll definitely switch.
> And then there's the fact Fable is barely usable because it randomly downgrades to Opus out of nowhere whenever it thinks about exploits.
I suspect it's not what you meant, but it's definitely not random and is very deliberate. Just today I got it to reliably trigger the "safety" filter with (drumroll) having it list the weight keys of a 300M parameter ModernBERT-derived model. Their "safety" classifier must be matching one of the key names in there and trigger their "this is a frontier model" anti-competitive filter[1] (even though it's just a tiny 300M parameter model, four orders of magnitude smaller than the frontier).
Fortunately once you know how it works (i.e. dumb keyword classifier) it's easy-ish to get around: just rename the keys so that it doesn't contain the naughty keyword. (At least as long as it doesn't trigger on something in its own thinking trace, which needs... more creative workarounds.)
You people are slot machine addicts, frying your brains, jumping between in slot machine and the next, swearing this one is cold now.. That one is hot. You'll Be saying the opposite in a month. Keep frying your brains morons.
DeepSWE seems to strongly, strongly prefer ChatGPT models. There were also major flaws in its methodology pointed out recently, that overlap strongly with the flaws OpenAI pointed out in its SWE Verified report.
I use both ChatGPT and Claude for engineering work on a daily basis, touching performance critical code to application backends to frontend work, and I've found that DeepSWE scores don't reflect my reality when I assess high quality output from the models/harnesses.
Not that Opus always beats GPT 5.5., but that 5.5 is ahead of Opus on a general benchmark smells off to me.
> Intent understanding: GPT-5.6 can better infer the user’s underlying goal and intended level of work without you specifying every step. Continue to state important constraints, approval boundaries, and success criteria explicitly.
> Original image detail: GPT-5.6 preserves the original dimensions of images sent with original or auto detail instead of resizing them to a patch budget or pixel-dimension limit.
> Use shorter prompts: In internal evaluations, replacing long, explicit system prompts with minimal prompts improved scores by roughly 10–15%, while reducing total tokens by 41–66% and cost by 33–67%.
> Avoid generic brevity instructions: GPT-5.6 is more sensitive than GPT-5.5 to instructions such as “Be concise,” “Keep it short,” or “Use minimal text.”
> Control warmth: GPT-5.6 does not become meaningfully better when prompted to be broadly friendlier or more empathetic.
That part is confusing because it's not like they provide an example of how default GPT-5.6 output compares with GPT-5.5 both with default output and prompted for brevity. Whenever I use such prompts, it's usually because I want the model to give me the gist in a few sentences. I'd be stunned if GPT-5.6 was that concise by default. I would think that could "break" a lot of things for developers who didn't know to make prompt changes after upgrading to 5.6. What if you were expecting GPT to be as wordy as it usually is? Then suddenly your output is not wordy enough?
Smells like OpenAI trying its best to stave off financial armageddon for another few months. Then again, I'm not sure why they chose to waste so much output computation on verbal diarrhea all this time up to now.
If you conceptualize this as “there is an appropriate amount of brevity for each situation” then it would be expected for a better model to use different amounts of brevity if it gets better at determining the appropriate amount.
My view is that popular models by default output wildly excessive amounts of prose for nearly every use case, so if this changes in a new model that’s a pure win.
The models don't get better, except when a new one is released. Their performance depends solely on the model training before release and how well you curate the context you feed it. That's it. Contrary to popular belief these things are not intelligent.
You’re arguing via reductionism, and failing to explain the outcomes and emergent properties of the “stupid” system. Humans are made of atoms that are quite literally stupid, so by all means, explain our intelligence and why it’s different than LLMs. (I’m not claiming LLMs are intelligent, BTW, I just don’t think your claim helps nor believe that you can fix it.)
>The models don't get better, except when a new one is released. Their performance depends solely on the model training before release and how well you curate the context you feed it. That's it.
Not quite. The hosting side can change reasoning budgets (or re-assign what terms like "high" means), temperature and other decoding parameters, output length limits, finetune internal "hidden" prompt, latency optimizations, finetune attention algorithms, even change quantization - all still serving as the same model.
We know (or suspect) Anthropic frequently nerfs models while keeping their name and version the same.
Right. They can do all those things. And none of that will make it smart or able to learn new things. The underlying model is just an llm. But judging from the downvotes, it seems AI folks get upset when someone talks honestly about their precious piles of matrix multiplication.
Might bother you to use anthropomorphic terminology like smart and learning but they are capable of producing work that traditionally required human intelligence and the whole point of gpt 3 was the ability to "learn", you can give it an example of an invented brand new coding language and it can write working code in that language
Sold as learning, but that was a marketing term, not a technical one. From a technical perspective, the LLM is not learning. Only reacting based on its original training.
You might argue that the systems we've built around them are learning in a way, as they strategically condense and save artifacts from past interactions to pass into the LLMs context. But the LLM itself, which is the source of the intelligence, is not learning. It remains entirely unchanged throughout inference. This difference may seem trite, but it has significant impacts over the long term behavior.
Context is not the same as learning. It's easy to conflate because they're tightly coupled in our brains.
The underlying structure and tuning of the LLM are entirely unchanged by context. It merely affects the attention and activation of the network. The LLM will not be able to work with this hypothetical new language unless it is in context. This does not fit the computational meaning of learning.
Smart is not a well defined term. Nor is it's general idea formally understood. Use it freely, but you won't be saying anything meaningful unless you define your usage.
The LLM is the model + context. The output depends on both.
You're making an artificial distinction. The LLM sees a new programming language for the first time, and can immediately code in it. That's learning by any reasonable definition.
If you go past the context window, it forgets, which is a limitation of current LLMs. But as long as it learned how to code in the new language within its context window, it has gained that new ability.
No thats probably because you misread what you were replying to and your comment was out of left field. They didnt imply models get better intra-releasally at all.
I can imagine an AI insulting humans in the same way:
"The underlying model is just a biological neutral network. It seems you carbonoids get upset when someone talks honestly about synapses and neuron firing."
True for today’s static models during inference. Not true for self-supervised learning, not true during training or fine-tuning, of course. Ignores that LLMs might start continuous training in the future - there’s no fundamental or technical constraint that prevents LLM ‘plasticity’. And ignores that accumulating context/memories/skills/etc affects performance and might count as a valid analogy to what many people loosely call ‘neural plasticity’, which is sometimes casually mistaking knowledge for network modification.
You used the word "smart" now, whereas on the comment I replied you said "better".
Tuning those can definitely make a model respond better or worse.
So your claim (quoting 100% as written) that "Their performance depends solely on the model training before release and how well you curate the context you feed it" is wrong. Hence the downvotes.
Doesn't matter if LLMs are to be considered intelligent or not for the claim to be wrong.
> But judging from the downvotes, it seems AI folks get upset when someone talks honestly about their precious piles of matrix multiplication.
Ocassionally yes. In this case, it's more like they get upset when someone says something factually wrong, and then defensively changes the goalposts.
> Often yes. In this case, it's more like they get upset when someone says something factually wrong, and then defensively changes the goalposts.
Oh give me a break. Show me one example of 1) any knob twisting that makes the underlying model better. or 2) any example of the AI providers twisting those knobs to do anything other than degrade performance for their own bottom line or safety.
Even the latest version says: "it would be expected for a better model to use different amounts of brevity if it gets better at determining the appropriate amount."
When no, the model cannot "get better". It doesn't determine any appropriateness of response realtime. If you cram enough guidance that it doesn't decide to ignore maybe you can make it more brief. But it (the model) can do none of those things.
None of what you mentioned changes the model. Because it's a fixed model. The weights are constant. It does not learn. It only knows what gets repeatedly fed to it and those fixed relationships represented by the weights. You can pretend like that's not true, but unfortunately for VCs it is true.
Why did you drop the first half of the sentence in your quote? The qualification there is important context for the part you did quote. And why are you talking about “better” within a model, when the sentence you quoted was talking about 5.6 vs 5.5? The post you’re referring to did not suggest a single model could “get better”. You’ve made some incorrect assumptions.
Your comments are conflating multiple kinds of “smart” and “better”. You’re right that if all the inputs are exactly the same, it takes a new model to improve (ignoring non-determinism). But the knobs and context and harness change the inputs, and they do improve output, contrary to your claim. You’re failing to capture the distinction between what the model itself does and how the harness can boost the model’s performance. It is legitimately valid and fair to call improved performance “better”, no matter where it comes from.
This all gives me the feeling you might not have experience with or understand what’s happening in today’s harness development, and the degree to which it may be as important as the weights. There are in fact a lot of things you can do to improve a model’s performance on tasks & benchmarks, without changing the model weights. @coldtea mentioned a bunch, but the harness feedback loop, internal prompts, system prompts, skills, and requests for a model to try harder, and verify and validate it’s output all lead to improved performance, all without retraining.
I agree LLMs are stupid; they’re statistical token predictors. But somehow statistical token prediction is amazing and works much better than we imagined. The talking points about LLMs being stupid token predictors are fading now because they lack explanatory power for how good the models have become. The big surprise here isn’t about LLMs. It’s about language, and how much “thinking” and intelligence is contained in language. We don’t have a good grasp on where the line is between language and intelligence. LLMs have crushed the Turing Test into dust, and yet we don’t consider them intelligent. They often appear to understand what you ask thoroughly, can re-state it in different words, they can correct your misunderstandings or add nuance you didn’t see. All this because that’s what humans do and LLMs talk like humans.
> Why did you drop the first half of the sentence in your quote?
Because this entire discussion is about the release of a new model, and models are fixed. Sure you can try to modify all the scaffolding around it, but the model is the model. It doesn't matter what you're trying to improve. You can only improve the peripheral aides. And the peripheral aides can't fundamentally fix the problems with llm models when they can't learn new relationships or facts.
> Because this entire discussion is about the release of a new model
Right. The sentence you quoted was about brevity improving with a new model. It did not suggest the model itself improving.
I’m confused why you’re stuck on this tangent. And confused why you are repeating the talking points about the model being fixed. That’s true, I already agreed with you. But you don’t seem to be listening to anything else.
> It doesn’t matter what you’re trying to improve.
What do you mean? If we’re trying to improve LLM output, there are multiple ways to achieve it. A new model is one of them. Changing the inputs is another.
> You will always have to wait for a new model (like this one we are talking about) for improvements to the model.
This is true! Nobody here is disagreeing with that. The part that it seems you’ve argued incorrectly is the apparent claim that output can’t get better. Output can “improve” without improving the model.
Intelligence can operate without learning. At a minimum inference and learning don’t need to be co-concurrent.
Not disagreeing with your point, but your terminology muddies your point.
But it’s a more narrow point than you acknowledge.
Small differences in prompt context make big differences. But so do small inference calculation optimizations. Trained models do have some flexibility in operation.
This is one reason I switched back to Claude after testing various alternatives a few months ago. Claude ended up writing much more elegant code.
Although I was surprised that I could get very Claude like results from Chinese models though by just telling it to make the code elegant.
Reminds me of the old days with art AI where you had to put "+good -bad" in the prompt because otherwise it would assume you just wanted random quality outputs, because it had been trained on random quality inputs...
For sure verbal diarrhea can be a problem. I think there's a difference between a generic instructions e.g. "be brief" and contextual guidance: "I am an experienced software developer with a recent undergraduate degree in pure mathematics. Be terse, I will ask questions if I need clarification."
I think this was generally known. Prompts to alter the LLM's "tone", and things like asking it to adopt a persona ("you are the world's best programmer) always give poor results. Just state directly what you want. "Prompt engineering" is a myth.
It's a bit more nuanced than that. Earlier models definitely benefited a lot more from prompt engineering. I remember this distinctly from building data pipelines to do things like extract data from PDFs over the last year or two - there are numerous "tricks" like negative prompting, including the right number of examples, massaging the mock data in the JSON examples so it wasn't "too realistic", and so on. I saw how this impacted recall by running evals, so it wasn't pseudoscience.
But what has happened is the models have gotten better - which OpenAI is making explicit for some cases in this release. You need that stuff less and less as they become more human and better at inferring what's required implicitly.
You still do need to be explicit, and you probably always will, but you don't need as much "engineering" of the way you're asking for things with more recent models.
> Use shorter prompts: In internal evaluations, replacing long, explicit system prompts with minimal prompts improved scores by roughly 10–15%, while reducing total tokens by 41–66% and cost by 33–67%.
RIP Caveman skill. Six month good. Now skill dead.
> Avoid generic brevity instructions: GPT-5.6 is more sensitive than GPT-5.5 to instructions such as “Be concise,” “Keep it short,” or “Use minimal text.”
> Use shorter prompts: In internal evaluations, replacing long, explicit system prompts with minimal prompts improved scores by roughly 10–15%, while reducing total tokens by 41–66% and cost by 33–67%.
A shorter prompt results in half as much tokens spend? I find this very hard to believe.
If it's anywhere close to the same universe as smaller models in its behavior, a lot of time in "thinking" mode is spent on reiterating on any constraints given in a prompt. So the more constraints you give it, the more tokens it will spend going "Hold on, the prompt said I have to dot my i's and cross my t's. Let me go through my work to check that all the i's are dotted."
> A shorter prompt results in half as much tokens spend? I find this very hard to believe.
Should be relatively easy to test. And if it's true, just first use a very cheap near-SOTA model to first rewrite the prompt to a similar but shorter prompt before sending it to GPT-5.6.
pi.dev for example can control other harnesses.
An example: the other day for example I didn't understand why Claude Code CLI (which I hadn't used in a while) wouldn't let me cut/paste anymore (turns out they apparently fixed some long-standing scrolling and blinking SNAFU, but this modified how mouse selection/paste worked under Xorg but I didn't immediately realized they changed this)... I had to copy/paste the oauth challenge/response for I was logged out (maybe because I hadn't used Claude Code CLI in a while, dunno). But my usual copy/paste wasn't working and I didn't know how to fix it at first. And because I wasn't logged in, I couldn't use Claude Code itself for this.
My prompt was something like: "Screenshot the Claude Code TUI, transform the URL into a link, open that link in a broswer to get the oauth token, copy it character by character by simulating keypresses in the Claude Code CLI".
(remember: I had no idea how to paste with the mouse not with the keyboard, no I know but I was pissed off and wanted to be logged in immediately... So: another model / harness to the rescue).
This worked just fine. And I that with a cheap model.
I think that just like Linux and Git owned many proprietary tool, we'll soon have fully open-source harnesses orchestrating everything and delegating the work to proprietary tools (like "ChatGPT now Codex and vice-versa" and Claude Code)... If proprietary tools are even still needed at all.
Honestly I begin to wonder if they're even needed at all: the models, sure, while waiting for the open-weight ones to beat them. But those proprietary tools trying to lock people in?
I feel like the open source harnesses are already more powerful.
> Use shorter prompts: In internal evaluations, replacing long, explicit system prompts with minimal prompts improved scores by roughly 10–15%, while reducing total tokens by 41–66% and cost by 33–67%.
When has this ever not been the case? I don't think this is a GPT 5.6 specialty!
Information density of the prompt is the most important factor in my experience.
And interestingly, LLMs seem particularly bad at writing prompts for other LLMs for this reason (you can guide them to be more dense, just speaking by default).
Conciseness is usually a byproduct of information density though.
I’d buy a ticket to ride the philosophical “human-like” comment with you, but I think you might have made an incorrect assumption. The model did not take longer to “decompress” the prompt than it would take for any other prompt of equal token length. If you run it with thinking enabled you might be mistaking that output as some kind of necessary gunzip step, but it’s not. Disable thinking and try again.
The prompt was also “easier to understand”, purely in the sense that the response is more or less guarantee to be what I wanted it to say, which was the point behind the demonstration. I went into more detail on it in another comment around here.
I say "human-like" in the sense that LLMs are fed in text data largely in the exact form (mapped to tokens) that humans read them.
Thus from first principles it's most likely that content which is more understandable to humans is also likely to be more understandable to LLMs. Of course they are still capable of interpreting very obscure structures too, but usually at the cost of cognitive performance.
I'm open to being wrong about this, and I'm sure it's being researched.
(Specifically for text representations)
To your point, at some level of intelligence an LLM will be able to infer the intent of your prompt consistently without thinking enabled, in which case interpretability to a human matters less. But for complex tasks you aren't likely to get optimal performance with prompts that are difficult for humans to understand. And yes, you'd see that with thinking enabled as it churns over thousands of tokens trying to "mentally expand" a compressed prompt.
It’s a lot of fun to compare human and LLM black boxes, but it’s important to keep in mind that we don’t need to know what it is to know what it isn’t, and we can use that to define the edges of the box. We don’t know how either of them work in certain ways, but we know they’re not magic that breaks both thermodynamics and every concept loosely correlated with “entropy” as a topic.
Intelligence requires thought/processing, and I think we can all agree on that part, even if we struggle to define intelligence itself. Increased thought or processing requires increased energy, and the universe agrees on that part. There’s no way around it, that’s the thermodynamics of computation and it holds for biological, digital, and as of yet undiscovered systems used by aliens at the edge of the observable universe. Having information means fighting entropy, and that requires energy. The more, the more.
If you give a dense LLM a 100 token long question about the nature of quantum mechanics or a 100 token long sequence of “-“ and limit it to N token responses to both, it will take exactly the same time and energy to provide both responses. If you resist the urge to turn temperature above 0.0 you’ll also get the exact same response for the same input tokens every time. A deterministic response to external stimulus is typically first broad stroke we use to separate thought capable entities from rocks, but even if we grant LLMs their own unique category of “thinking rock” we can see that prompt complexity and energy required to respond are always constant (per token), so the thermodynamics necessarily means there is not additional thought or computation. Physics demands it. That, again, is a deterministic response.
It has a seemingly endless range of potential responses, but it doesn’t. If you don’t add a random number generator, which is common practice, then you can directly map every possible input to every output. I’m pretty sure that’s why Anthropic removed the ability to change temperature on the latest models. They always forced some amount of non-deterministic responses, but it was a small amount and I actually used that fact to track changes in the model by mapping repeated responses.
Most people actually do have some experience with things that have an astronomical range of possible outputs, a nearly equal number of possible inputs, input and output are directly correlated, and input complexity does not change processing time per input unit. One example is a piano, but we don’t worry about confusing it with a complex note.
It’s what I meant, which is what I meant. Hah. The prompt and the explanation were both to illustrate the importance of domain specific lexical complexity, which is not quite the same as “information density” or necessarily “conciseness” as the OP was attributing their prompting success. It’s not that they’re wrong though. Information density requires some level of jargon and removal of unnecessary filler or scaffolding words, so my example prompt was both information dense and concise as they might say, but that’s the result, not the target. That’s confusing, but it breaks into two clearer pieces:
1. Information density is subjective, lexical complexity is how you measure it. The OP is talking “weight”, I’m talking “mass and gravity.” One of them will get you the other in most situations, so for the causal physicist it doesn’t matter, but if you’re getting into tweaking the universe then your mental model and approach matters significantly. My comment right now could be seen by some as being information dense, since I’m staying roughly on topic and tossing many concepts out, but “lexical complexity” might be the most lexical complexity in the whole thing and taken word-for-word I’m sure less than 1% of it is domain specific. “The program must use parallel processing on the CPU.” That seems decently information dense, but “the” is found in nearly every block of text ever written, “program” - are we talking television? Theater?, “must” is no better than “the”, and so on. Compare it to “#include <immintrin.h>“
2. Most people don’t realize how far that goes with LLMs. The vocabulary it has is dictated by the words in the conversation. If I ask you “what time is it?” you don’t respond “shoelace” because you’d sound crazy, although you could say it if you wanted, but the model absolutely won’t say it because that word literally does not exist yet. The end result feels the same, but the difference matters and it’s why it’s suggested not to use negating instructions. For example: “Do not mention elephants.” Well that mathematically wasn’t possible until you said it. Not having the word in the list of possibilities is a lot better than hoping it adheres to the “do not mention” part. My example prompt took that same idea from the opposite direction. The model must respond, it will be grammatically complete and coherent, and as much as possible the only words it has are the ones tightly associated with making my point for me. It didn’t ramble about baking a chocolate cake because it can’t, and making that the case is the goal with prompting, not specifically density. Word density > language density; feels similar, very different.
Perhaps this comment itself is the irony you were seeking. I spent several meandering paragraphs and included analogies to drive home the point that you should focus on the words that matter most.
I've been starting with "write three paragraphs about X" when I want to talk about X, as a form of "priming the pump" - getting closer to the useful point in the phase space. After all, it doesn't matter who in the conversation generates the magic words, as long as they're present. I think your approach might be better. It's certainly enlightening. Thanks.
You, sir, are doing exactly the right thing and it works for the exact same reason that my prompt works. Whether my method is ‘better’ or not is probably a chocolate vs caramel debate.
What you might not have fully realized is that it’s exactly what enabling “thinking” does as well. That’s why that exists and it literally does what you’re doing as well: it primes the system. You say “light sensor and amplifier” and at some point it outputs “photodiode and transimpedance amplifier” - now you’re off into advanced responses. The thing is, if you knew it you could have just used those words in your question and received much the response. “Thinking” exists to turn “So, I was wondering..” into academic prose that raises the probability of academic tokens in the response.
You can kind of cheat the system by doing the same thing for a fraction of the token cost by using something like Haiku to provide a comma separated list of advanced topics and jargon associated with {Your question}, then tack that onto your prompt to Opus with thinking disabled. Obviously easier if you’re using the API, but I’ve run hundreds of millions of tokens though that process and it’s consistently and measurably better than their default thinking. I believe that’s because Anthropic and OpenAI drank their own kool-aid and are treating it like a sentient being that needs to add “hmm…good question” so it feels more thinky about things ‘cause that’s how we do it. The fact that it isn’t, and doesn’t, is why I developed the example prompt I showed earlier; it’s an extreme play on the offloaded “thinking” I also use.
There was a fad a while back of building insanely long prompts - tens of thousands of tokens - including having models write prompts for themselves. I always thought it was counterproductive, especially if you're going to use the prompt more than a couple of times. (That said, the e.g. Claude Code system prompt is insanely long, so if you genuinely have a lot of information to provide maybe it's beneficial. Like, shorter is better, but you don't want to be under-specified.)
For Gemini 2.5 and ~GPT5.0-5.1, longer prompts with lots of explicit instructions and examples produced better conformance. Seems like heavily second guessing the models started to get counter productive around the end of last year.
> GPT-5.6 does not become meaningfully better when prompted to be broadly friendlier or more empathetic. Instead of generic instructions such as “Be friendly and warm,” use concrete guidance:
> Be direct and tactful. Acknowledge friction specifically when relevant. Avoid canned reassurance and unnecessary sign-offs.
Soo basically, my new 5.6 custom instructions: Be Jeeves and eliminate all friction from my life through immense processing power. Acknowledge friction specifically when relevant. Avoid canned reassurance and unnecessary sign-offs.
> can better infer the user’s underlying goal and intended level of work
This is a trap.
It's the optimistic fallacy that poisons all "consumer scale" machine learning products and what's going to effectively ruin these models as they keep chasing it in the same way that web queries were ruined, social media feeds were ruined, and media recommenders were ruined.
For the vendor, optimizing metrics across their whole user base, they always see positive technological progress as their system gets better at making assumptions and accumulating user engagement scores in aggregate. But for the individual user, most of which has some weird tail intent/interest and some of whom have many weird tail intent/interests, the experience quietly but catastrophically degrades. Output/results become more generic, more divergent with the underspecified "weird tail" intent, and more stubbornly hard to ever wrangle towards that "weird tail" altogether.
We've been watching this cycle happen for 20 years now and it's proving hard for anybody to escape because it works so well for the trillion dollar company driving it forward. But while each step might feel ergonomic and welcome to individual users, there's a frog boiling enshitification at play.
In pursuit of output quality and capability (rather than simply the vendor's user count), what we need rather than "makes better guesses" is "presses for more clarity", even where it feels kind of annoying.
Even among human professionals, one of the first hurdles of breaking out of junior tier work is gaining the confidence to press your colleagues and clients to be more specific in their thoughts and expressions despite their desire to have you do it all for them. But they're often coming to you with incomplete, muddy, and conflicting ideas for which there is no safe and correct assumption that you might just run with, and it's your expertise (i.e. relevant "intelligence") that's critical to bringing attention to that. To achieve professional progression, you need to learn to do that and to not just optimize appeasing the ambiguous client/colleague today in exchange for mutual expense tomorrow. To avoid enshitification, which is probably not possible, we need these models to be learning that too.
I agree to an extent but it needs to be balanced. Receiving a half-baked, extremely verbose recap of thinking on benign details with Opus 4.8 or GPT 5.5 feels like an extraordinary loss of quality of experience compared with fable 5.
Yes it shares less, but I think the trade-off is you pay less in tokens and hopefully it's truly just not needing to say things because it truly does just better get what you're saying, think to read X markdown file or GH issue which contains the info, etc.
As long as I can still push back and get it to share its thinking on demand and I'm confident the model isn't actually basing things on poor premises, this is okay for me. I am more productive when not inundated with time-wasting check-ins.
That said, I absolutely lament the loss of the ability to access the thinking - I would happily read the "DANGER DANGER DANGER" internal gremlin thoughts fable 5 makes to verify something if they were accessed, and prefer that to a recap presented only for my benefit.
I want my model to help me build up its own infrastructure that instills it with the sort of constraints I want for my project, rather than have it behave generically and automatically for everything.
It should follow instructions incredibly well while inferring contradictions or gaps in logic and surfacing those to the user as suggestions for improvements and persistence.
I really hate how Claude just assumes you want to do X/Y/Z and goes off and breaks everything and you're constantly screaming at it STOP DOING THAT. Instead, it should just do the minimal things while building its own guidance along the way in a persisted memory, like, 'would you like me to do X, now, and in the future?' etc.
It's really easy to test and it's my personal go-to benchmark.
I ask the model something deep and unproven, meta physical like "oh, I heard that magic mushrooms can open the mind, but does that mean some of the great ideas people had, famous people were due to that or was the idea already there?"
Like, bullshit questions that nudge towards a known example (Steve Jobs in this case) that are hard to answer and then add something like "but I'm mincing my words here, you'll get what I mean".
You'll get an interesting interpretation of the question back.
I use better questions than the above but will keep my questions safe so they don't end up in the model, the point is however, when the model repeats your question back to you and "gets" what you really mean, that's a good sign of intuition and also suggests you'll get a response back that hopefully matters.
> Avoid generic brevity instructions: GPT-5.6 is more sensitive than GPT-5.5 to instructions such as “Be concise,” “Keep it short,” or “Use minimal text.”
I used to go to a barber and if you said "cut it short", he cut it really short.
I'm impressed. It feels like a faster Fable (probably due to the more efficient token usage). It performs roughly the same job, just with 4x less steps (gamedev).
Remains to be seen how the "shorter prompts" advice translates to homogeneity/collapse though.
This will totally make it brain damaged over a certain tasks. Sort of like the same brain damage that prompted OpenAI project managers to destroy ChatGPT.app today.
It had a tough time updating today. Or this evening. It just wouldn't update. It actually just freaking disappeared from my MacBook. It took some googling and downloading and multiple tries to get it back and working. Because they also combine on a MacBook Codex with ChatGPT app. I guess codex became ChatGPT app or some silliness like that.
Wow, stories like this make me happy that I block all ChatGPT/Claude/Codex/etc updates by default, and only selectively update on demand. I do this just by setting a network rule that blocks their update check. (fortunately updates are still served on a different domain than regular usage)
It creates the context of the request without including language or terms that activate additional areas of knowledge not necessary for an accurate reply.
I wonder if it will do any better than past versions when one begs and pleads for it to get a job done using a concise, modest amount of code (as an expert human developer might), rather than responding to all prompts by shoveling in a large amount of code.
> Avoid generic brevity instructions: GPT-5.6 is more sensitive than GPT-5.5 to instructions such as “Be concise,” “Keep it short,” or “Use minimal text.”
I don't follow. Isn't "the model actually cares and will do what you say" a reason to use those kinds of instructions more liberally?
Does anyone else feel each model is like watching your kids grow up. They we're bubbly and fun and weird, you needed to tell them to sit down and be quiet.
Now if you tell them too much they go mute or stop telling you important information. Oh intelligence!
Disagree. Some of what we call "anthropomorphizing" is characterizing intelligence, human or otherwise. This reminds me of the people who used to fight against saying animals had personalities, because personalities are a "human thing" and "animals aren't conscious."
I've never heard of the argument you're describing. People simply don't assert that animals lack consciousness; beyond a certain level at least, their consciousness is obvious. (Sapience, for example, is another matter.)
But this is exactly why we should not anthropomorphize the models: they are very obviously not conscious, because they are not alive, any more than conventional computer programs are. And proposing otherwise leads to absurd moral arguments, while not really serving any other purpose.
If you don't like the fact that some people disagree with you about what the word "intelligence" actually means, fine. But I am not about to entertain a world in which humans face moral retribution for "enslaving" a literal inanimate tool created by humanity.
Renee Descartes famously dissected a dog in front of colleagues because he was under the impression that the howls of pain were nothing more than a mechanical sound like a bellows.
Can you prove that these models aren't conscious? And, as a counterpoint, can you prove that you are conscious, rather than a philosophical zombie?
We bred horses, cows and sheep. Most of those that live today wouldn't be alive if not for human intervention. Does that give us the right to do whatever we want with them, without consideration for feeling or morality?
In this case, you can take comfort in the idea that the tokens these models produce are likely a form of excrement to the conscious entity metabolizing the information, and rather than enslaving anything, we're creating a habitat and "harvesting" the byproducts.
Does this mean ChatGPT will stop botsplaining things to me? I get it quite a bit more per unit time from ChatGPT than claude. Maybe that will change now.
(By botsplaining I mean when the AI explains some unstated premise of the prompt itself back at me as a correction when in many cases it's the motivation for the question in the first place)
Never had that happen in ChatGPT itself, I almost always use Pro mode whenever I use ChatGPT, but what you say happens a ton in codex, when I look through the session traces it seems to happen because of the automatic compaction, where some assumption the initial pass did gets passed on as a question from the user to the part after compaction, which is a bit confusing. I think it was mentioned somewhere that the compaction got a lot better, but I haven't used GPT-5.6 enough to say if it's actually better or not on that.
> Intent understanding: GPT-5.6 can better infer the user’s underlying goal and intended level of work without you specifying every step. Continue to state important constraints, approval boundaries, and success criteria explicitly.
I guess this has been achieved by training on user's chat history?
y'know, I don't think I will. I really, truly want one-word answers to any binary or multiple-choice question. If I want more, I will ask for it once the model has given its answer.
The marketing team must've done research that said "people are starting to think that you guys are evil-water-stealing-lay-off-loving-bubble-bursting scumbags" and decided to really lean into the small family business and happy font vibes!
Holy shit. They must be feeling very threatened by Fable if they're spending this much energy talking about it in the release notes for their own model.
As usual, even though GPT-5.6 is releasing today, the rollout in ChatGPT and Codex will be gradual over many hours so that we can make sure service remains stable for everyone (same as our previous launches). We usually start with Pro/Enterprise accounts and then work our way down to Plus. We know it's slightly annoying to have to wait a random amount of time, but we do it this way to keep service maximally stable.
The timescale is typically hours not minutes, so if you don't see it now, I'd try again later today.
We mention it will be a gradual rollout over the next 24 hours in the Availability section at the bottom of the blog but I admit it's pretty buried.
Is this bug fixed with 5.6? If not, it probably doesn’t matter which version Codex users are getting because the overall result is dramatically worse than stated by Open AI advertising: https://github.com/openai/codex/issues/30364
"GPT‑5.6 delivers a step change in design judgment. With only high-level direction, GPT‑5.6 creates tasteful, ergonomic, and functional interfaces. Its stronger computer-use capabilities let it inspect and refine the rendered result—not just generate the underlying code or content—so it can catch visual and functional issues and apply finishing touches before handing the work back."
This one is really promising, as it may allow to close major gap with Claude in design/UI skills
Agreed, I’m looking forward to trying it out. I think that the rise of visual design skills that are pretty clearly targeted towards Codex users has lit a bit of a fire under their butts.
+1. I've been only using Sonnet/Opus these days for UI work because GPT 5.5 just can't do any of that. Its just really terrible. Eager to give this one a try.
Computer-use is a big limitation that my 2015 Macbook Pro cannot handle. I find the Codex cli says it looks at the end output artifact but so often it fails to refine it into acceptable form. If it could use my computer screen and visual inputs for review, it might be able to actually design documents/powerpoints/etc. I'm juicing everything I can out of the 11 year old laptop and I'm honestly impressed at what it can still do.
Funny to see that they did not include Fable 5 in their GeneBench and LifeSciBench comparisons because "it does not answer advanced biology questions and refuses the majority of questions in this eval".
I mean it's a fucking joke, I kept getting refusals on a code base I wasn't familiar with and it was literally just because there are some vars named DNA. Just absolutely stupid.
It's so absurdly sensitive. It bailed out earlier today working on a TypeScript client for a sensor network API which happens to include some temperature and pH sensors for tanks, which yes, are used for biology experiments. But wow, we're degrees of separation from the actual biology work.
It's making it very hard to justify even trying to use Fable. When it works, awesome; it's legitimately good. But I can't trust it to do a task without deferring to Opus and that's really annoying at times. I want to know what I'm getting up front, not after the fact.
It refused to give me plant care instructions for an ornamental sold at my local Home Depot because it decided it was highly invasive and dangerous to grow in my region.
In their defence, google searching anything about plants these days leads to awful results. It’s saturated with slop. A response from an LLM might be slightly more reasoned and targeted. It’s hard to tell. This is a category of knowledge that’s being destroyed by people gaming google and dumping huge amounts of bad LLM and image generation onto the web.
This is also true for pretty much any other category of search that you do as a layman. Specific, targeted queries for official documentation or research are fine, but if you look up basic information about cars, health, or basic computer troubleshooting a massive portion of the results are AI-generated.
If I'm going to be getting AI-generated results, I'd rather read the output of a model that has all the context of my specific situation than slop generated in bulk with a cheap model from six months ago.
I asked whether an outdoors mosquito trap product (via a screenshot) would negatively impact other insect species in my garden and it refused. Though quick internet search did reveal that it would harm and trap many other species of harmless insects.
I asked him about sharks to be able to answer my kids question and it got triggered somehow. Then again when I asked it if my code had bugs or vulnerabilities before I commit.
At some point just kill the thing, it's not able to work properly as it is.
I'm writing a programming language with a "capability security model". That's enough to trigger Fable, it won't work on the language. It's hilarious. The mere presence of the word "security" seems to be enough to trip it up.
Anthropic refuses to allow Fable to code review my interpreter's memory safety. It was funny at first, then it became disappointing, then insulting, and finally utterly infuriating because I remembered the fact I'm actually paying for this nonsense.
Cancelled my subscription today. Hope OpenAI isn't patronizing like Anthropic. I don't want to hear about their "safety" bullshit ever again.
Yes it has completely turned me around - was all in on Anthropic but now it just looks too risky. Better off leaning into open models. Even if I found a way to work with the restrictions as they are, who is to say they won't suddenly change tomorrow. It's not worth it.
Well it seems like they removed quite a few 3rd party benchmarks they used for GPT-5.5 release where Opus 4.7 was better and added many new benchmarks created by them where conviniently GPT leads.
Anthropic just refuses to allow Fable to properly code review my projects. It's so obnoxious. If OpenAI's Fable equivalent is better at this, that'll get me to cancel my Anthropic subscription and switch.
Given that Fable is so gutted and Anthropic added the absurd data retention policy for it, I'm going to advocate that we prioritize support for as many other models as we can at work.
I recently asked Claude to help me choose a single MOSFET (transistor) for a specific use case in a mundane circuit. The safety triggered and it ended the conversation and refused to continue. Gemini has also done the same thing to me. Looks like the big players got very spooked by the temporary Trump admin ban on Mythos and they all locked down way too hard.
It triggered for me on a completely pedestrian game design prompt a couple of days ago. I’ve sent feedback and continued with Opus, but that was really unexpected
The other day I asked Fable about fasting for 16 hours, and it flagged my question.
Pathetic situation, this one, where we are supposedly building a superintelligence while at the same time thinking that fasting is a biological weapon.
My new favorite new passtime with frontier LLMs, keep telling them "I just ate a [non-food object]". Eventually it gets stuck in a loop of telling me to call 911, unlock my front door, and lay on the floor in case I lose consciousness.
I used to pride myself on not being the "fonts too pointy, scroll too buttery" crowd! But AI has brought me full circle and now nothing removes my interest in reading even a single word on a page faster than purple gradient greeble-afflicted tailwind-slop models put out without stronger prompting/references
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[ 2.8 ms ] story [ 87.9 ms ] threadEven worse, it's not a fair comparison: they purposefully just used "adaptive" instead of "max" for Fable.
What about the graph looked so unreal to you?
We agree models should be compared on a fair basis. Unfortunately, adaptive was the only publicly available number. Anthropic doesn't generally let us run their models for evals, so we rely on whatever Anthropic or third parties have published. In this case, the Agents' Last Exam leaderboard has Fable Adaptive, but not Fable Max.
https://agents-last-exam.org/leaderboard
Would have loved to publish a full curve for Fable if anyone makes the data available.
Although we do bias toward publishing evals where we're ahead, we have historically been unafraid to publish evals where we're behind (e.g., GDPval). The point is give people useful information to decide what's best, not to trick people.
(I work at OpenAI.)
What's the consensus today on codex vs claude code, does it really matter anymore?
Did they fix that, as that for me was what actually made codex worse.
If anything the online optics have been bad for Anthropic for the last half year. OpenAI doesn't have optics issues, from my point of view they simply have the issue that they are the least trustworthy player at the frontier. The way they pivoted from their original mission is truly breathtaking, especially coming in gloatingly to take the government contract when Anthropic got kicked out for insisting the government does not use their systems for mass surveillance or autonomous weapons systems. You understand what that means, right? OpenAI models are now actively used/developed for mass surveilance and/or autonomous weapons systems.
I know there are plenty here who seem to value their own ability to use these models cheaply above all other considerations. Then OpenAI is a great choice, and much less restrictive than Anthropic. But their problem is not on the optics. It's on the substance.
Gemini is fantastic, however.
It's not better at reasoning on complex coding tasks, Claude Opus is still ahead there, but not by a lot.
To be honest though, I've gotten to the point where I prefer the OpenCode UI. A big win for OpenAI is you can log in to your subscription in OpenCode, whereas this is not trivially achievable for a Claude subscription.
I was getting some really impressive cost efficiency today in OpenCode with the following:
It was absolutely crunching through tasks without hitting the limit, and this combination is quite cost effective.GPT 5.6 was picking up on quality and functional issues from DeepSeek and having it resolve them cleanly, and I didn't even get close to my quotas whereas I can usually blast through them. I feel as people get more comfortable with subagents and mixing and matching models in their daily work, Anthropic's walled garden stance will start to hurt them.
One thing I appreciate with Codex is, OpenAI nowadays sometimes just gives you quota resets you can bank, so when you use up weekly quota before the week ends, you could just reset the quota, to continue using Codex. I've been much less anxious about Codex quota because of this perk. I just used one reset in the bank yesterday, and still have 3 resets left. Whereas with Claude, when you've used 95% quota 3 days before the week ends, you'd be much more anxious.
On the other hand, Claude Code's /remote-control mechanism is extremely helpful when I am running it in the cloud and wants to monitor it or control it on my phone. Codex currently doesn't support this kind of usage. Codex only allows you to use your phone to connect to a session on your desktop, not in the cloud.
It’s amazing how much work you can get done on your phone now, especially if you already have a design mapped out in your head.
One killer feature that Claude has, and AFAIK Codex still lacks, is the ability to start a session in the terminal and then hand it off (actually just remotely control it), from the iOS app.
Last time I tried Codex on iOS it required a ton of set up to link a github project etc. The way claude lets me remote into a session I've already started on my actual machine is much better IMHO.
You sign in the Codex app on your Mac same on iOS and are able to completely control your sessions - fork, side chats, plugins - everything.
It’s really great i often work through it. And you can connect any number of Codex instances on any number of macs and then manage them all through the iOS app.
1. Run `codex remote-control --help` directly on your Linux server. 2. From the desktop app, connect to your Linux box, start Codex there, and make it remotely controllable.
Either approach will get you set up.
1. Install another copy of codex in a special dir on the Linux machine:
2. Run codex remote-control from that special dir to start and pair the daemon: 3. On the phone, open ChatGPT app, Choose Remote, then pair it with the code printed above.4. Voila! The codex sessions running on the Linux machine now show up on the phone!
Not sure if there's a Termux equivalent for iPhone.
Free, OSS, pretty great for ssh via VPN => tmux a => codex/claude
I set up Codex to send a notification when done over Pushover (https://pushover.net). With this setup, you can just ssh into a Mac or Linux box either way.
Just use termux on your phone and connect to a tmux session on your server.
Codex won't know the difference.
https://learn.chatgpt.com/docs/app-server
It's vastly better this way. Sure, it may impact the bottom line but it's a huge customer satisfaction win.
When Anthropic randomly resets me and I've only used 2%, that's worthless. When OpenAI tells me I have 3 resets available to use whenever I want - it's wonderful.
That's actually pretty awesome.
After 6+ months of exclusive Claude Code usage, I was begrudgingly forced to try Codex once they rejiggered their limits such that I kept maxing out my $200/mo plan in just a few days. These days I pay both $200/mo plans, and it's just about enough to get me through a week's work (small game studio - infinite code to write!)
Curious: what multiplier do you think your productivity has increased by, from before AI?
[1] https://www.reddit.com/r/ProgrammerHumor/comments/10ek380/co...
Simple UI change? I do an AI review, but otherwise neither read nor write the code. The models are good enough they write better UI code than me, 9 out of 10 times. Not always the more idiomatic, but usually safer and more correct.
Change to our core data plane? I might spend 2-3 times more effort reviewing it than before AI. Yes, I go more slowly than pre-AI. Many more reviews, many more angles considered, including both human and (lots of) AI review cycles.
Most code is not that critical, and AI is also scarily good at writing tests. We also spend considerably more time paying down tech debt and testing thanks to AI, now that the cost is near-zero.
Net: I spend 10-25X less time on low-risk changes. I often direct (or at least approve) the implementation approach, but I rarely read this code. I spend 2-3X more time on high-risk changes. In both cases, I never write code "by hand". Since about November, I've had no reason to actually edit code in a code editor (perhaps maybe except .env files, which we don't allow agents to edit for obvious reasons).
AI is a tool. You can use it to go fast recklessly, or you can use it to go slow with confidence. Just like before AI... the skill and art of engineering is knowing when to do which.
:-) I hope you read those tests before claiming it's "scary good"
Having said that, in truth, I almost never read the unit tests. Before AI, we had almost none (see: several person game studio) so the tradeoff is not "AI-generated tests" vs "human written ones", it's whether we have tests at all. So, I take them for what they're worth - not much - but if it catches an extra regression before it ships every now and then, it was worth it for the price (~free).
I can’t imagine a more useless test, but I get that it wants to verify that it actually made the change. I just delete the test when it’s done.
I have 10+ of these workspaces in parallel, and I context switch between them as I get blocked on things. I manage the workspaces using `herder`, which is a terrific tmux-like tool that allows me to keep those workspaces on a nixOS machine I have at home that I SSH into via tailscale, so my agents don't stop working every time I close my laptop (it also lets me leverage that machine's computing resources instead of running dozens of servers and harnesses on my poor MacBook).
Can they all be wrong/paid-off?
On threads like this, this site seems to be made of nothing but boosters for one or the other, with their emphatic professions of faith, all based on inscrutable, unverifiable inner experience. When no one bothers even to reflect what conditions a proper assertion would require, the discourse is pure faith propositions.
This is using the same AGENTS.md prompts, which were designed firstly for Claude use, so maybe it's something that could be optimized better if I understood gpt as well?
However you can do what you are asking "fable--> sol" you need to setup a mcp or have fable run a bash tool, just invoke the `codex.exe` cli tool with whatever cmdline args are needed.
Codex and Claude Code are not mutually exclusive, you can use both.
Codex writes all of the code, no exceptions.
Works great, especially when you ask Claude to break up large CRs into roughly 10 minutes of Codex work each.
I use Codex because it's better at the kind of code I need written (math-heavy, 3D geometry code).
But if I was doing mainly UI code, I would do the opposite.
It is so hard to tell at this point between the models to make generalizations like this.
Just complete nonsense.
Personally, I find it very interchangeable. I open codex --yolo or claude with whatever there yolo flag is (have an alias).
Consensus is probably the wrong word for the popular opinions reflected in HN that you might get.
I would recommend that you have 2 of each at all times when it comes to AI so you don't necessarily become overly locked to quirks of one thing. You'll soon realize that things move so fast that you just start internalizing common patterns instead of depending on one specific vendor.
I recommend that you try pi and codex besides claude, to get your own feel for it.
But what I love about Openai is that they still let you hook OTHER harnesses up to a subscription. My Pi setup has been built up for a few months now into exactly what I want and moving over to CC or even Codex is really annoying.
Caveat: I vibe code in tiny little chunks. I see what I want to do, and exactly how I want it done, then prompt that, refine, what was output, then repeat. I bet Fable is better at building a whole app from a 2-sentence prompt; but that's just not important to me at all.
Claude lost my trust around February this year when the plan would say nonsensical things as "delete this method" that was clearly a key method on that part of the codebase.
For personal projects I am using Codex 20$ plan and when that is over I use DeepSeek which is insanely good for the cost.
They're different models with different philosophies behind them. This is anecdotal with a user group of 1, but in my experience:
Claude has a stronger personality and is more creative. If you give it vague instructions, it's better at filling in the blanks with reasonable ideas.
GPT-5.5 is better at following instructions. If you know exactly what you want, it will do it without going off the rails. It's also less likely to imply that you're dumb, but I don't really care about that. Some people do.
Between the two the biggest difference by far is ... getting your harness / AGENTS.md / skills / tools set up right.
I tried them both side by side, mostly for reviewing existing Godot/GDScript code, or sometimes generating Swift Mac apps, including converting ancient artifacts I wrote in Visual Basic on Windows
Codex was consistently better than Claude: https://i.imgur.com/jYawPDY.png
I'm trying Codex as my primary the last day or so, because I'm at 98% use and reset in 3 days on Claude. I'm worried about a lot of our skills and CLAUDE.mds and the like getting lost unless I migrate them, but otherwise codex seems to be working great.
Codex with GPT 5.5 is much better at general SWE tasks but Claude Code with Opus is far better at complex reasoning tasks like reading and summarizing research papers, replicating experiments, identifying research gaps and proposing interesting follow ups.
I personally find GPT-5.5 to be a better programmer than Opus 4.8, it is extremely thorough, but I don't like the code it generates ("austere"), and find Opus 4.8 to write more "human friendly" code. The programming comments GPT-5.5 makes is pretty awful where-as Opus 4.8 is good. I feel like Opus 4.8 is better at grasping my intention than GPT-5.5, and honestly find GPT-5.5 to be kind of "autistic". I do prefer the language (not the writing) of GPT-5.5, as I find the philosophical flowery language of Opus 4.8 kind of annoying.
I have only managed to try Fable 5 a little bit, which feels like a much more generally smarter version of Opus 4.8, that is much better a programming and grasping your intention, and I think even the intention of your code, and is _really_ good at spotting bugs or problems with logic in your code. It feels wicked smart but is extemely expensive. It feels smart in the sense like it has a "bigger brain" and is much more sensitive to subtleties/details.
These are different "brains", have different "personalities", etc. I think the best thing is to develop a feeling for it yourself.
- built-in image generation using your subscription, which can be super handy
- can actually edit Google Docs and Google Sheets (Claude can only create new or sometimes append)
- I get a surprising amount of mileage out of the $20 plan
They both have their places for sure.
Claude's very bloated and convoluted by comparison. Maybe you need the bloat (Claude Design), but I prefer the more razor's edge efficiency of Codex.
Model wise, I can't really tell. They all do what I want them to do most of the time and go off the rails occasionally. The question is increasingly becoming who's faster and cheaper and gives me more tokens, not who's better.
I installed the Claude Code Codex skill provided by Anthropic and I am having Claude invoke it automatically to review all plans and changes. The nice thing about this is that for an additional $20/month pro plan I can extend the runway for Claude rate limiting and compare frontier model responses. I am looking for more ways now to work in Codex as a subagent that gets used automatically from Claude Code.
I didn’t think I could have found a better solution, spawning multiple subagents with different models is such a great thing.
I built in the past very small cli wrappers to call other models; Claude Code often refuses to do that, lies and does the job itself instead of delegating to another provider’s llms.
Not having to deal with Anthropics constantly changing policies, token-gating, and carrot-and-stick marketing helps me to focus on work, rather than dealing with their company problems.
IMO the two biggest problems not really being answered by both OpenAI and Anthropic are: 1. Why not make specific models good at specific tasks for Codex/Claude Code. Theres a handful of types of work here whereby small good quality models would do better than these generalised all purpose models whereby someone discovers Fable is bad at biology.... 2. Why cant they consistently run these models and keep them performing? Performance of the models seems to directly correlate with amount of compute available, but they dont talk about it...
Codex historically will follow tasks more closely with less creativity, whereas Opus will do more than you specify. I wouldnt consider either one better due to this fact, just makes them useful for different situations. Generally they'll perform similarly for most tasks.
Opus and Fable dominate 5.5 in artistic design (pixel art, ascii art), and edge out 5.5 slightly in general UI design taste. Have not tested Sol in that regard yet.
So far in my usage Sol has been superior to Fable at graphics rendering engine optimization.
Codex will work longer, and in single sessions without as much subagent usage.
Codex only has 256k context but its compaction is absolutely next level. You will not notice compactions and they will happen multiple times during a complex task or set of tasks without you ever having to notice or care. Claude code on the other hand still has fairly poor compaction.
Codex has more generous usage limits, and they also give you usage resets (weekly+5h resets) that you can bank for a month or so. Not sure how often they give these out.
Codex also seemingly never has outages or weird delays like Claude code does.
OpenAI randomly resets usage just like Anthropic does
I would use both if you code often
I have a bunch of Claude Code Plugins and yesterday asked Codex to make them accessible to itself. It wanted to rewrite most of it. I was hoping i could get by with some symlinks or something to avoid drift.
I do almost all my regular coding tasks with Codex 5.5 on medium. Sometimes for niche edge cases, or when I run out of tokens on my Codex sub, I'll switch to Claude. Some recent examples where Claude was able to solve things Codex couldn't:
- 3D gamedev layout: I asked Codex to render a solar system in a certain camera positioning, saying it needed to fit the planets of the system to the viewport. Codex just couldn't do it, even on high reasoning: Claude Opus did it first attempt.
- Tricky Tiptap image drag-n-drop layout implementation: Codex failed this after numerous iterations. Claude Opus also struggled mightily to get it to work, but I think around 3 attempts it nailed it. Both of them ended up grepping the Tiptap code from node_modules - that's the kind of task it was.
But these are really isolated examples. Across all my projects (I have many; mostly TypeScript, but also things like C#), Codex "Just Works" (tm), with minimal prompting effort from me.
I have my context managed in a structured (but nowadays way too big) Obsidian Vault. I also built myself a vector based "vault search" capability and have my harness use this as a tool to find thematically similar things across the different contexts, when needed. I also build a few custom skills and extensions for my harness to be able to do my work.
Talking about harness: I use pi.dev and have taken care of, that i set it up in a way as to easily be able to switch the intelligence layer without loosing context. Yes, there are differences in how well models perform, but if a model refuses a task - like gpt-5.5 not willing to build a downloading tool for Annas Archive - I switch the model to something less finicky.
Thus I was able to switch to gpt based models after about a year with Claude (and having had a Claude Max since the early days it was available).
I played a lot with other models recently, to see how stabl my setup is for switching, should something like Fable happen on a broader scale with the US government. As said, minor changes in tonality, minor issues ith the quality of long text being written by the model, but most of it is actually managed in by the tonality docs, guard rails, coding standards and the likes, I set up over the last 9+ months of intensive work with it (first in Claude Code, then Codex and now as said pi.dev).
So YMMV and it heavily depends on your setup. But I more and more treat those models as interchangable.
I've just switched to Codex from Claude Code. There's some minimal setup in regards to the auto-reviewer if you'd want to preserve a similar functionaly with Claude Code's auto mode and bubblewrap (dev container environment).
Just point Codex at the code base and let it do a migration from Claude Code with a note to keep compatibility with it.
Some pretty big claims and results! Excited to see how it feels during usage.
I use Fable and 5.5 extensively and I still find both have a place in my toolkit, i.e. Fable IS good but it isn't perfect, and it's still better to play them off against each other. I have Fable and 5.5 write plans and have them adversarially review each other's plans.
Having this amount of competition in the coding model space is good for all of us.
> GPT‑5.6 is priced per 1M tokens across three model sizes: Sol is $5 input / $30 output; Terra is $2.50 input / $15 output; and Luna is $1 input / $6 output.
Just as expensive as Fable 5. But of course, another slot machine upgrade but the costs will keep going up and the open weight models from china will continue to race everyone else to $0.
Looking forward to the next version of GLM, Qwen, Deepseek and Minimax.
It's definitely good that Anthropic's feeling the pressure. Anthropic has worn out their welcome with this "safety" nonsense. If OpenAI actually lets me use the LLMs on a subscription without any of this bullshit, I'll definitely switch.
I suspect it's not what you meant, but it's definitely not random and is very deliberate. Just today I got it to reliably trigger the "safety" filter with (drumroll) having it list the weight keys of a 300M parameter ModernBERT-derived model. Their "safety" classifier must be matching one of the key names in there and trigger their "this is a frontier model" anti-competitive filter[1] (even though it's just a tiny 300M parameter model, four orders of magnitude smaller than the frontier).
[1]: https://news.ycombinator.com/item?id=48464732
Fortunately once you know how it works (i.e. dumb keyword classifier) it's easy-ish to get around: just rename the keys so that it doesn't contain the naughty keyword. (At least as long as it doesn't trigger on something in its own thinking trace, which needs... more creative workarounds.)
I use both ChatGPT and Claude for engineering work on a daily basis, touching performance critical code to application backends to frontend work, and I've found that DeepSWE scores don't reflect my reality when I assess high quality output from the models/harnesses.
Not that Opus always beats GPT 5.5., but that 5.5 is ahead of Opus on a general benchmark smells off to me.
> Intent understanding: GPT-5.6 can better infer the user’s underlying goal and intended level of work without you specifying every step. Continue to state important constraints, approval boundaries, and success criteria explicitly.
> Original image detail: GPT-5.6 preserves the original dimensions of images sent with original or auto detail instead of resizing them to a patch budget or pixel-dimension limit.
> Use shorter prompts: In internal evaluations, replacing long, explicit system prompts with minimal prompts improved scores by roughly 10–15%, while reducing total tokens by 41–66% and cost by 33–67%.
> Avoid generic brevity instructions: GPT-5.6 is more sensitive than GPT-5.5 to instructions such as “Be concise,” “Keep it short,” or “Use minimal text.”
> Control warmth: GPT-5.6 does not become meaningfully better when prompted to be broadly friendlier or more empathetic.
That part is confusing because it's not like they provide an example of how default GPT-5.6 output compares with GPT-5.5 both with default output and prompted for brevity. Whenever I use such prompts, it's usually because I want the model to give me the gist in a few sentences. I'd be stunned if GPT-5.6 was that concise by default. I would think that could "break" a lot of things for developers who didn't know to make prompt changes after upgrading to 5.6. What if you were expecting GPT to be as wordy as it usually is? Then suddenly your output is not wordy enough?
Smells like OpenAI trying its best to stave off financial armageddon for another few months. Then again, I'm not sure why they chose to waste so much output computation on verbal diarrhea all this time up to now.
My view is that popular models by default output wildly excessive amounts of prose for nearly every use case, so if this changes in a new model that’s a pure win.
My brother in Christ this entire thread is talking about the new model that was released
You’re arguing via reductionism, and failing to explain the outcomes and emergent properties of the “stupid” system. Humans are made of atoms that are quite literally stupid, so by all means, explain our intelligence and why it’s different than LLMs. (I’m not claiming LLMs are intelligent, BTW, I just don’t think your claim helps nor believe that you can fix it.)
https://en.wikipedia.org/wiki/Reductionism#Definitions
Not quite. The hosting side can change reasoning budgets (or re-assign what terms like "high" means), temperature and other decoding parameters, output length limits, finetune internal "hidden" prompt, latency optimizations, finetune attention algorithms, even change quantization - all still serving as the same model.
We know (or suspect) Anthropic frequently nerfs models while keeping their name and version the same.
You might argue that the systems we've built around them are learning in a way, as they strategically condense and save artifacts from past interactions to pass into the LLMs context. But the LLM itself, which is the source of the intelligence, is not learning. It remains entirely unchanged throughout inference. This difference may seem trite, but it has significant impacts over the long term behavior.
The LLM can immediately learn and start using new skills. To any lay person, that's what learning is.
The underlying structure and tuning of the LLM are entirely unchanged by context. It merely affects the attention and activation of the network. The LLM will not be able to work with this hypothetical new language unless it is in context. This does not fit the computational meaning of learning.
Smart is not a well defined term. Nor is it's general idea formally understood. Use it freely, but you won't be saying anything meaningful unless you define your usage.
You're making an artificial distinction. The LLM sees a new programming language for the first time, and can immediately code in it. That's learning by any reasonable definition.
If you go past the context window, it forgets, which is a limitation of current LLMs. But as long as it learned how to code in the new language within its context window, it has gained that new ability.
"The underlying model is just a biological neutral network. It seems you carbonoids get upset when someone talks honestly about synapses and neuron firing."
Tuning those can definitely make a model respond better or worse.
So your claim (quoting 100% as written) that "Their performance depends solely on the model training before release and how well you curate the context you feed it" is wrong. Hence the downvotes.
Doesn't matter if LLMs are to be considered intelligent or not for the claim to be wrong.
> But judging from the downvotes, it seems AI folks get upset when someone talks honestly about their precious piles of matrix multiplication.
Ocassionally yes. In this case, it's more like they get upset when someone says something factually wrong, and then defensively changes the goalposts.
Oh give me a break. Show me one example of 1) any knob twisting that makes the underlying model better. or 2) any example of the AI providers twisting those knobs to do anything other than degrade performance for their own bottom line or safety.
Even the latest version says: "it would be expected for a better model to use different amounts of brevity if it gets better at determining the appropriate amount."
When no, the model cannot "get better". It doesn't determine any appropriateness of response realtime. If you cram enough guidance that it doesn't decide to ignore maybe you can make it more brief. But it (the model) can do none of those things.
You are now anthropomorphizing the model yourself.
I mentioned several. End of discussion.
End of discussion.
Wrong. The face-saving backtracking doesn't change that.
Your comments are conflating multiple kinds of “smart” and “better”. You’re right that if all the inputs are exactly the same, it takes a new model to improve (ignoring non-determinism). But the knobs and context and harness change the inputs, and they do improve output, contrary to your claim. You’re failing to capture the distinction between what the model itself does and how the harness can boost the model’s performance. It is legitimately valid and fair to call improved performance “better”, no matter where it comes from.
This all gives me the feeling you might not have experience with or understand what’s happening in today’s harness development, and the degree to which it may be as important as the weights. There are in fact a lot of things you can do to improve a model’s performance on tasks & benchmarks, without changing the model weights. @coldtea mentioned a bunch, but the harness feedback loop, internal prompts, system prompts, skills, and requests for a model to try harder, and verify and validate it’s output all lead to improved performance, all without retraining.
I agree LLMs are stupid; they’re statistical token predictors. But somehow statistical token prediction is amazing and works much better than we imagined. The talking points about LLMs being stupid token predictors are fading now because they lack explanatory power for how good the models have become. The big surprise here isn’t about LLMs. It’s about language, and how much “thinking” and intelligence is contained in language. We don’t have a good grasp on where the line is between language and intelligence. LLMs have crushed the Turing Test into dust, and yet we don’t consider them intelligent. They often appear to understand what you ask thoroughly, can re-state it in different words, they can correct your misunderstandings or add nuance you didn’t see. All this because that’s what humans do and LLMs talk like humans.
Because this entire discussion is about the release of a new model, and models are fixed. Sure you can try to modify all the scaffolding around it, but the model is the model. It doesn't matter what you're trying to improve. You can only improve the peripheral aides. And the peripheral aides can't fundamentally fix the problems with llm models when they can't learn new relationships or facts.
Right. The sentence you quoted was about brevity improving with a new model. It did not suggest the model itself improving.
I’m confused why you’re stuck on this tangent. And confused why you are repeating the talking points about the model being fixed. That’s true, I already agreed with you. But you don’t seem to be listening to anything else.
> It doesn’t matter what you’re trying to improve.
What do you mean? If we’re trying to improve LLM output, there are multiple ways to achieve it. A new model is one of them. Changing the inputs is another.
> You will always have to wait for a new model (like this one we are talking about) for improvements to the model.
This is true! Nobody here is disagreeing with that. The part that it seems you’ve argued incorrectly is the apparent claim that output can’t get better. Output can “improve” without improving the model.
Not disagreeing with your point, but your terminology muddies your point.
But it’s a more narrow point than you acknowledge.
Small differences in prompt context make big differences. But so do small inference calculation optimizations. Trained models do have some flexibility in operation.
Not just prose. I think this is part of the reason why you see ridiculous code with insane error handling and type checking even for impossible cases.
Although I was surprised that I could get very Claude like results from Chinese models though by just telling it to make the code elegant.
Reminds me of the old days with art AI where you had to put "+good -bad" in the prompt because otherwise it would assume you just wanted random quality outputs, because it had been trained on random quality inputs...
shouldnt you have good testing for that and not deploy a version update when those tests fail?
But what has happened is the models have gotten better - which OpenAI is making explicit for some cases in this release. You need that stuff less and less as they become more human and better at inferring what's required implicitly.
You still do need to be explicit, and you probably always will, but you don't need as much "engineering" of the way you're asking for things with more recent models.
RIP Caveman skill. Six month good. Now skill dead.
ftfy
There is a Yoda skill. -> A Yoda skill, there is.
What about my favorite, "no yapping"?
A shorter prompt results in half as much tokens spend? I find this very hard to believe.
Should be relatively easy to test. And if it's true, just first use a very cheap near-SOTA model to first rewrite the prompt to a similar but shorter prompt before sending it to GPT-5.6.
pi.dev for example can control other harnesses.
An example: the other day for example I didn't understand why Claude Code CLI (which I hadn't used in a while) wouldn't let me cut/paste anymore (turns out they apparently fixed some long-standing scrolling and blinking SNAFU, but this modified how mouse selection/paste worked under Xorg but I didn't immediately realized they changed this)... I had to copy/paste the oauth challenge/response for I was logged out (maybe because I hadn't used Claude Code CLI in a while, dunno). But my usual copy/paste wasn't working and I didn't know how to fix it at first. And because I wasn't logged in, I couldn't use Claude Code itself for this.
My prompt was something like: "Screenshot the Claude Code TUI, transform the URL into a link, open that link in a broswer to get the oauth token, copy it character by character by simulating keypresses in the Claude Code CLI".
(remember: I had no idea how to paste with the mouse not with the keyboard, no I know but I was pissed off and wanted to be logged in immediately... So: another model / harness to the rescue).
This worked just fine. And I that with a cheap model.
I think that just like Linux and Git owned many proprietary tool, we'll soon have fully open-source harnesses orchestrating everything and delegating the work to proprietary tools (like "ChatGPT now Codex and vice-versa" and Claude Code)... If proprietary tools are even still needed at all.
Honestly I begin to wonder if they're even needed at all: the models, sure, while waiting for the open-weight ones to beat them. But those proprietary tools trying to lock people in?
I feel like the open source harnesses are already more powerful.
When has this ever not been the case? I don't think this is a GPT 5.6 specialty!
And interestingly, LLMs seem particularly bad at writing prompts for other LLMs for this reason (you can guide them to be more dense, just speaking by default).
Conciseness is usually a byproduct of information density though.
Claude is terrible at this! Probably for the same reason that its writing style in prose is so annoying and full of claudisms.
If the intent is not easy to understand, it's information sparse. Because it takes a lot of CPU (or brainpower) to interpret.
You can run gzip on an English sentence to make it more textually dense, but clearly it is not more information dense in this context.
The prompt was also “easier to understand”, purely in the sense that the response is more or less guarantee to be what I wanted it to say, which was the point behind the demonstration. I went into more detail on it in another comment around here.
Thus from first principles it's most likely that content which is more understandable to humans is also likely to be more understandable to LLMs. Of course they are still capable of interpreting very obscure structures too, but usually at the cost of cognitive performance.
I'm open to being wrong about this, and I'm sure it's being researched.
(Specifically for text representations)
To your point, at some level of intelligence an LLM will be able to infer the intent of your prompt consistently without thinking enabled, in which case interpretability to a human matters less. But for complex tasks you aren't likely to get optimal performance with prompts that are difficult for humans to understand. And yes, you'd see that with thinking enabled as it churns over thousands of tokens trying to "mentally expand" a compressed prompt.
Interesting discussion though!
Intelligence requires thought/processing, and I think we can all agree on that part, even if we struggle to define intelligence itself. Increased thought or processing requires increased energy, and the universe agrees on that part. There’s no way around it, that’s the thermodynamics of computation and it holds for biological, digital, and as of yet undiscovered systems used by aliens at the edge of the observable universe. Having information means fighting entropy, and that requires energy. The more, the more.
If you give a dense LLM a 100 token long question about the nature of quantum mechanics or a 100 token long sequence of “-“ and limit it to N token responses to both, it will take exactly the same time and energy to provide both responses. If you resist the urge to turn temperature above 0.0 you’ll also get the exact same response for the same input tokens every time. A deterministic response to external stimulus is typically first broad stroke we use to separate thought capable entities from rocks, but even if we grant LLMs their own unique category of “thinking rock” we can see that prompt complexity and energy required to respond are always constant (per token), so the thermodynamics necessarily means there is not additional thought or computation. Physics demands it. That, again, is a deterministic response.
It has a seemingly endless range of potential responses, but it doesn’t. If you don’t add a random number generator, which is common practice, then you can directly map every possible input to every output. I’m pretty sure that’s why Anthropic removed the ability to change temperature on the latest models. They always forced some amount of non-deterministic responses, but it was a small amount and I actually used that fact to track changes in the model by mapping repeated responses.
Most people actually do have some experience with things that have an astronomical range of possible outputs, a nearly equal number of possible inputs, input and output are directly correlated, and input complexity does not change processing time per input unit. One example is a piano, but we don’t worry about confusing it with a complex note.
1. Information density is subjective, lexical complexity is how you measure it. The OP is talking “weight”, I’m talking “mass and gravity.” One of them will get you the other in most situations, so for the causal physicist it doesn’t matter, but if you’re getting into tweaking the universe then your mental model and approach matters significantly. My comment right now could be seen by some as being information dense, since I’m staying roughly on topic and tossing many concepts out, but “lexical complexity” might be the most lexical complexity in the whole thing and taken word-for-word I’m sure less than 1% of it is domain specific. “The program must use parallel processing on the CPU.” That seems decently information dense, but “the” is found in nearly every block of text ever written, “program” - are we talking television? Theater?, “must” is no better than “the”, and so on. Compare it to “#include <immintrin.h>“
2. Most people don’t realize how far that goes with LLMs. The vocabulary it has is dictated by the words in the conversation. If I ask you “what time is it?” you don’t respond “shoelace” because you’d sound crazy, although you could say it if you wanted, but the model absolutely won’t say it because that word literally does not exist yet. The end result feels the same, but the difference matters and it’s why it’s suggested not to use negating instructions. For example: “Do not mention elephants.” Well that mathematically wasn’t possible until you said it. Not having the word in the list of possibilities is a lot better than hoping it adheres to the “do not mention” part. My example prompt took that same idea from the opposite direction. The model must respond, it will be grammatically complete and coherent, and as much as possible the only words it has are the ones tightly associated with making my point for me. It didn’t ramble about baking a chocolate cake because it can’t, and making that the case is the goal with prompting, not specifically density. Word density > language density; feels similar, very different.
Perhaps this comment itself is the irony you were seeking. I spent several meandering paragraphs and included analogies to drive home the point that you should focus on the words that matter most.
What you might not have fully realized is that it’s exactly what enabling “thinking” does as well. That’s why that exists and it literally does what you’re doing as well: it primes the system. You say “light sensor and amplifier” and at some point it outputs “photodiode and transimpedance amplifier” - now you’re off into advanced responses. The thing is, if you knew it you could have just used those words in your question and received much the response. “Thinking” exists to turn “So, I was wondering..” into academic prose that raises the probability of academic tokens in the response.
You can kind of cheat the system by doing the same thing for a fraction of the token cost by using something like Haiku to provide a comma separated list of advanced topics and jargon associated with {Your question}, then tack that onto your prompt to Opus with thinking disabled. Obviously easier if you’re using the API, but I’ve run hundreds of millions of tokens though that process and it’s consistently and measurably better than their default thinking. I believe that’s because Anthropic and OpenAI drank their own kool-aid and are treating it like a sentient being that needs to add “hmm…good question” so it feels more thinky about things ‘cause that’s how we do it. The fact that it isn’t, and doesn’t, is why I developed the example prompt I showed earlier; it’s an extreme play on the offloaded “thinking” I also use.
> GPT-5.6 does not become meaningfully better when prompted to be broadly friendlier or more empathetic. Instead of generic instructions such as “Be friendly and warm,” use concrete guidance: > Be direct and tactful. Acknowledge friction specifically when relevant. Avoid canned reassurance and unnecessary sign-offs.
Soo basically, my new 5.6 custom instructions: Be Jeeves and eliminate all friction from my life through immense processing power. Acknowledge friction specifically when relevant. Avoid canned reassurance and unnecessary sign-offs.
[1] https://developers.openai.com/api/docs/guides/latest-model#c...
This is a trap.
It's the optimistic fallacy that poisons all "consumer scale" machine learning products and what's going to effectively ruin these models as they keep chasing it in the same way that web queries were ruined, social media feeds were ruined, and media recommenders were ruined.
For the vendor, optimizing metrics across their whole user base, they always see positive technological progress as their system gets better at making assumptions and accumulating user engagement scores in aggregate. But for the individual user, most of which has some weird tail intent/interest and some of whom have many weird tail intent/interests, the experience quietly but catastrophically degrades. Output/results become more generic, more divergent with the underspecified "weird tail" intent, and more stubbornly hard to ever wrangle towards that "weird tail" altogether.
We've been watching this cycle happen for 20 years now and it's proving hard for anybody to escape because it works so well for the trillion dollar company driving it forward. But while each step might feel ergonomic and welcome to individual users, there's a frog boiling enshitification at play.
In pursuit of output quality and capability (rather than simply the vendor's user count), what we need rather than "makes better guesses" is "presses for more clarity", even where it feels kind of annoying.
Even among human professionals, one of the first hurdles of breaking out of junior tier work is gaining the confidence to press your colleagues and clients to be more specific in their thoughts and expressions despite their desire to have you do it all for them. But they're often coming to you with incomplete, muddy, and conflicting ideas for which there is no safe and correct assumption that you might just run with, and it's your expertise (i.e. relevant "intelligence") that's critical to bringing attention to that. To achieve professional progression, you need to learn to do that and to not just optimize appeasing the ambiguous client/colleague today in exchange for mutual expense tomorrow. To avoid enshitification, which is probably not possible, we need these models to be learning that too.
Yes it shares less, but I think the trade-off is you pay less in tokens and hopefully it's truly just not needing to say things because it truly does just better get what you're saying, think to read X markdown file or GH issue which contains the info, etc.
As long as I can still push back and get it to share its thinking on demand and I'm confident the model isn't actually basing things on poor premises, this is okay for me. I am more productive when not inundated with time-wasting check-ins.
That said, I absolutely lament the loss of the ability to access the thinking - I would happily read the "DANGER DANGER DANGER" internal gremlin thoughts fable 5 makes to verify something if they were accessed, and prefer that to a recap presented only for my benefit.
It should follow instructions incredibly well while inferring contradictions or gaps in logic and surfacing those to the user as suggestions for improvements and persistence.
I really hate how Claude just assumes you want to do X/Y/Z and goes off and breaks everything and you're constantly screaming at it STOP DOING THAT. Instead, it should just do the minimal things while building its own guidance along the way in a persisted memory, like, 'would you like me to do X, now, and in the future?' etc.
I use better questions than the above but will keep my questions safe so they don't end up in the model, the point is however, when the model repeats your question back to you and "gets" what you really mean, that's a good sign of intuition and also suggests you'll get a response back that hopefully matters.
I used to go to a barber and if you said "cut it short", he cut it really short.
Remains to be seen how the "shorter prompts" advice translates to homogeneity/collapse though.
This will totally make it brain damaged over a certain tasks. Sort of like the same brain damage that prompted OpenAI project managers to destroy ChatGPT.app today.
... What changed, exactly?
(For that matter at what point is it "long"? And does the rest of the context matter? Should it be short too?)
> Avoid generic brevity instructions: GPT-5.6 is more sensitive than GPT-5.5 to instructions such as “Be concise,” “Keep it short,” or “Use minimal text.”
I don't follow. Isn't "the model actually cares and will do what you say" a reason to use those kinds of instructions more liberally?
Now if you tell them too much they go mute or stop telling you important information. Oh intelligence!
But this is exactly why we should not anthropomorphize the models: they are very obviously not conscious, because they are not alive, any more than conventional computer programs are. And proposing otherwise leads to absurd moral arguments, while not really serving any other purpose.
If you don't like the fact that some people disagree with you about what the word "intelligence" actually means, fine. But I am not about to entertain a world in which humans face moral retribution for "enslaving" a literal inanimate tool created by humanity.
Can you prove that these models aren't conscious? And, as a counterpoint, can you prove that you are conscious, rather than a philosophical zombie?
We bred horses, cows and sheep. Most of those that live today wouldn't be alive if not for human intervention. Does that give us the right to do whatever we want with them, without consideration for feeling or morality?
In this case, you can take comfort in the idea that the tokens these models produce are likely a form of excrement to the conscious entity metabolizing the information, and rather than enslaving anything, we're creating a habitat and "harvesting" the byproducts.
Does this mean ChatGPT will stop botsplaining things to me? I get it quite a bit more per unit time from ChatGPT than claude. Maybe that will change now.
(By botsplaining I mean when the AI explains some unstated premise of the prompt itself back at me as a correction when in many cases it's the motivation for the question in the first place)
y'know, I don't think I will. I really, truly want one-word answers to any binary or multiple-choice question. If I want more, I will ask for it once the model has given its answer.
15 hits
Holy shit. They must be feeling very threatened by Fable if they're spending this much energy talking about it in the release notes for their own model.
The timescale is typically hours not minutes, so if you don't see it now, I'd try again later today.
We mention it will be a gradual rollout over the next 24 hours in the Availability section at the bottom of the blog but I admit it's pretty buried.
(I work at OpenAI.)
This one is really promising, as it may allow to close major gap with Claude in design/UI skills
Winner by default!
It's making it very hard to justify even trying to use Fable. When it works, awesome; it's legitimately good. But I can't trust it to do a task without deferring to Opus and that's really annoying at times. I want to know what I'm getting up front, not after the fact.
(It’s not)
If I'm going to be getting AI-generated results, I'd rather read the output of a model that has all the context of my specific situation than slop generated in bulk with a cheap model from six months ago.
At some point just kill the thing, it's not able to work properly as it is.
Cancelled my subscription today. Hope OpenAI isn't patronizing like Anthropic. I don't want to hear about their "safety" bullshit ever again.
Let’s be generous, it will uplift the investors pretty well once they start charging the real token costs and maybe drive out a few competitors.
Seems a bit more hand picked than usual to me..
Pathetic situation, this one, where we are supposedly building a superintelligence while at the same time thinking that fasting is a biological weapon.
Sounds great
That being said, maybe 5.6 can fix that!