Yes -> every 18 months they've gotten 90% more efficient for the same level of quality for about 5 years. There's little sign that trend is slowing. If anything, there's reason to believe that System 1 models (plus potentially 1-2-3 workflows) may increase that over the next 3-5 years.
You'll know when the trend stops -> when the intelligence differential between smaller models like 7B starts to grow instead of shrink from 32B models -> that means 7B is getting about as smart as it can get. Then, 32B will follow next, then 70B, etc etc.
Because models are only getting better at a rate of 10% per year, people always want the best quality possible. You can get SotA performance from a year ago for a fraction of the cost, but why would you use Opus 4.5 when you can use Opus 5.5?
Reddit is full of people complaining how they burn their 200$ sub in half an hour by starting ten Max sub agents. That’s to say, many people just don’t know what they’re doing.
To pay for the training of the models which are getting bigger and more expensive. So intelligence is getting cheaper overall but the need for ever-increasing intelligence can't be sated.'
Input
$0.10 / MTok for prompts up to 100,000 tokens
$0.50 / MTok for prompts over 100,000 tokens
Output
$0.50 / MTok for prompts up to 100,000 tokens
$2.50 / MTok for prompts over 100,000 tokens
100k tokens is an absurdly low cutoff and it is only applicable to Haiku and not Sonnet or Opus. It's a low enough cutoff that it will be quickly exceeded if you are doing anything with Agents; for typical generation or Jev-like classifiers, it's a good value and as noted in this article, that is apparently the vast majority of Haiku use.
In both cases, still much cheaper than Haiku 4.5's $1 input / $5 output and these prices better compete with GPT-6 Luna. ($0.10 input / $0.50 output, but with no token threshold [EDIT: the threshold for Luna is apparently 272k])
I mostly use Haiku for really, really basic stuff, never for actual engaging work. I've used it for first-pass analysis to triage bugs, for example - all it does is related N bugs together to see if any potentially relate. Then I have Sonnet investigate further.
Haiku 5.5 is noticeably smarter than GPT-6 Luna, so I can see their pricing strategy here.
For a while Anthropic has lacked a cost effective “cheap” LLM for summarisation, compacting, RAG helpers, etc.
These ‘ephemeral’ workloads are often under 100k tokens, or can be structured to be under 100k.
In some coding benchmarks, Haiku 5.5 beats Sonnet 5! (Especially implementation; do a well defined Jira ticket; etc), it’s really impressive how much intelligence per dollar has grown in just a few short months.
noticeably smarter remains to be seen in practice. For now, Haiku is a bit more expensive than Luna on < 100k token, but I just don't have any agentic work below 100k, so this is going to be 5x more expensive than shown on these charts. It's hardly competitive ...
No? Don't use these lower end models to work on small well defined tasks with a frontier model orchestrating? This approach works very well for me and don't have any issue staying under 100k. I have no idea if it's cheaper but it does seem to be much faster for tasks like QA.
The benchmarks are very long form logic, knowledge, and coding tasks though. I'm very interested in Haiku 5.5's performance on ObviousBench where Luna 6 is currently SotA.
It's not that weird. Most companies considering paying Anthropic are probably not considering Chinese models as alternatives. Many don't even realize they exist.
The thing is: availability of near-SOTA cheap Chinese models is forcing OAI and Anthropic to bring prices down and offer more efficient models, instead of simply focusing on super expensive SOTA LLMs.
If you want to make it about 99% of real world companies, they are all on Gemini or Copilot anyway, nobody is going through legal and procurement to get models from dubious silicon valley startups when you have relations with Microsoft or Google or Amazon from ages because some benchmark is showing some minor digit benefit when vibe coding GTA 6.
I said "most companies considering paying Anthropic", which is not the same as "most companies". I also don't agree that "nobody" is doing this; I have lots of anecdata suggesting otherwise. Maybe the majority of companies are using the easy option of Copilot or Gemini like you said, but it's nowhere near 99%.
Yes it is. Literally every company Ive spoken too about this only has MS teams co-pilot lol. Like all of them are using the most basic form of AI.
They dont even know about Anthropic and think its just "AI". By the way this includes one of the largest power systems design firms in the world, who helps build many datacenters... Uses only MS copilot.
Umm, just go look up the whole of software revenues globally and compare that to OpenAI. Its very small. Nobody is paying for SOTA models outside software engineering and ancillaries, except for some outliers fields like consulting. Then there's techies in every field who use it, but not companies themselves.
Companies as a whole, do not care about this technology, except tech companies and its adoption cant even be compared to CRMs. A company might buy a SaaS product with AI but most of them are not purchasing Anthropic subscriptions lol.
I have been settling on using Mimo 2.6 flash for discovery/research and glm 5.3 flash for implementation. So far I've been pretty happy with the results, but mimo can be quite slow. If speed is an issue, I just switch to glm 5.3 flash.
I haven't really tried it that much. I use GLM 5.3 a lot, especially with Coralbricks where the cache reads are free so I can let it think a lot without breaking the bank in the following turns. It's really good for bughunt, planning, and security work.
I said this in another comment, but Artificial Analysis has the cost per task of Haiku on max roughly equal to that of Sol on medium, and the latter is significantly more intelligent. (And I'd wager that Sol probably finishes tasks more quickly, even with Haiku inference being faster.) So Haiku really only makes sense on lower reasoning levels, and only if you care about intelligence and speed more than you do about cost effectiveness (where Luna currently dominates). And that's without even bringing Chinese models into the mix.
Some people/organizations are ideologically opposed to using Chinese models. Not me, I use GLM-5.3-Flash almost everything (the subscription-subsidized pricing on a legacy Z.ai plan makes it the best value model by a wide margin), along with some MiMo and DeepSeek. Still, I use Luna for certain tasks where speed is more valuable than performance; I can see this new Haiku displacing Luna for those. If you mean Haiku 4.5 though I agree, that model was a waste of time and money.
Luna is not really the fastest. You need to use it in high/max to get the good output for what it is good for: summarizing. And that is already close to two minutes per task...
That's not what any benchmarks that look at cost per task or similar says in terms of cost. The Chinese models, generally speaking, might be cheaper per token but need a lot more tokens to get there.
Except for the new MiMo V2.6 models, which appear to give some of the best value right now, at least on paper. (I haven't tried them so I can't speak from experience.)
Well, unless you're using OpenCode Go, it's per-token costs (even if already super low), while Haiku falls under the Claude sub. It's just more straight forward and you aren't feeling a "loss" with the sub.
Most people are on subscription pricing. A $20 Anthropic subscription gives >$500 equivalent tokens, which is not so different, and you get smarter models. Their API pricing has decent margins.
They say it in the announcement: Haiku is basically useful to be called as a subagent. So you use Opus, and you want to investigate your production logs, instead of throwing them right away which is going to use a massive amount of token for mostly noise data, Opus asks Haiku to determine patterns to extract only the relevant logs and feed it back in Opus. The models are meant to be used together.
If that was the case, then Claude Code would use smaller models for subagents. It doesn't. The subagent always inherits the parent model unless you tell it specifically not to.
>> 100k tokens is an absurdly low cutoff and it is only applicable to Haiku and not Sonnet or Opus. It's a low enough cutoff that it will be quickly exceeded if you are doing anything with Agents
Your vibes don't appear to be supported by facts. From the announcement:
>> Claude Haiku 5.5 is priced 90% lower than Claude Haiku 4.5 for requests up to 100,000 tokens, and 50% lower for requests over 100,000 tokens. On Haiku 4.5, 90% of requests fell into the former category.
Not really. We use Haiku 4.5 to turn users' natural language queries and requests into fairly complex structured specs for interior design and construction. It has near perfect accuracy.
There's also a tokenizer efficiency difference: modern Claude's 100K tokens are about ~60-65K modern GPT tokens, so in reality the Luna cutoff is much further away than the Haiku one.
Notable that one suggested use case for Haiku is "classification requests", i.e. Jev competitor, and the pricing matches GPT-6 Luna which is behind OpenAI's "Decisions API" Jev competitor.
For this application 100K token input is plenty.
Of course Anthropic and OpenAI at $0.10/M are still 2.5x the cost of Jev's $0.04/M.
They are targeting businesses/API use for fast decision making and agent integration. Plus they now need to be competitive with Jev-type models in that space.
You could also use it as a subagent prompted eg by Sonnet/Opus orchestrator agent and for many agentic workflows significant part of the dispatched tasks might be under 100k budget.
Isn't it less than a year since Claude models went from 100k token limit to 1M limit? Don't get me wrong - my main agent normally gets to 25% or so before I clear it these days, but as a subagent, doing research or summarisation, I don't think 100k is "absurdly low".
If you look at how different reasoning levels can easily exceed task cost of sonnet 5.5 you will see that you will basically never fall into that under 100,000 token threshold. I mean maybe you can choose low and do a basic summary task, but then you could choose something much cheaper instead. I don't know what Anthropic is thinking with its dumber models.
If you can't get any coding done with 100K context that is either a broken model, a broken harness or a skill issue. I would mostly use Haiku in task or explorer subagents. I'm not saying I stay under that on every task, but I do have quite a few sessions that cap out well below that, so that price difference would be very meaningful.
I use Luna for this day in and out and its excellent - if Haiku is that much better I will be changing things up.
>If you can't get any coding done with 100K context that is either a broken model, a broken harness or a skill issue.
"less context is better and if you can't get stuff done with less yur bad" is the worst argument ever.
it might be pure luxury to your eyes, but it's great to not require the use of a special custom harness that transcribes everything into emoji and compresses everything into barcode images.
it's great to have a million token context to throw a large project into. If I need 100k just about any current gen consumer GPU in the world has very good models that I can self host for 100k context, limiting myself to 100k on someone elses machine seems to be missing a lot of the point unless the model itself is extraordinary.
It isn't an argument, and I never said it is better. It is an explanation for why the pricing break is relevant. Anyone can do the same things to take advantage of that pricing. Would you prefer I not explain a basic fact to someone who may not know what is possible?
The services are priced this way because larger context has significantly higher costs. That is a fact about the technology and it is true for every provider. So moaning about it isn't useful.
So what's your setup? Speaking as a "open terminal in repo, open Claude, say 'do this thing please '" kinda guy I'm interested in learning about these more advanced techniques.
I use oh-my-pi (omp.sh) - mostly with stock settings and skills but its a "fully loaded" harness that you don't really have to add anything to. I do change a couple of things: I set it to prefer subagents, and I enable rewind. It is crazy that rewind is not a default, it saves a TON of context - if the agent goes down a crazy path that burns a lot of tokens it can rewind to an earlier checkpoint with the exploration or bug hunting summary.
Presently I'm using sol-high for the default agent which does orchestration and a lot of smaller investigation and coding tasks itself. sol-max for planning and review. Luna-max for planned coding and general tasks. I also have a $10 minimax plan and use M3 for exploration and library roles, but I could probably be using Luna for that just as well and still only very rarely run into usage issues.
I don't use any plugins or skill libraries apart from Caveman and I'm not sure how useful that really is anymore so I'd start without it so you have a baseline to compare. I do think it reduces context usage a bit but I haven't measured it recently. Caveman also includes some team, agent & investigation skills - again they might be helping but I haven't re-evaluated since like 90 days ago.
Haiku doesn't seem most cost effective solution. Jev like classifier can work in a fairly smaller model which are 1/10 of the cost. Opus is SOTA so I get the use case for one being restricted to Anthropic use case. I think their use case is mainly for users using on their chat for pro and free users to maximize their quota.
The monthly API credits for Max plan seems fantastic, especially considering Haiku pricing. Being able to actually use my Claude plan for other harnesses and use-cases on top of regular CC usage is everything I wanted.
Anthropic has really been doing all the right things in the past few weeks, while OpenAI continues to fumble the bag.
yeah totally agree. esp how efficient it can be to have a subscription quota-paid orch spin up a bunch of API agents, this is kind of like free money to encourage what was already an easy way to save money (via batch pricing)
Note that this is Anthropic Trojan-Horsing the previously announced June change in with a model release, where the Claude Agent SDK can no longer be used with Claude subscriptions and is now billed with API credits only.
You could always use Claude models on other harnesses via API... just not via subscription. Now they give you $100 worth of API tokens to use on opencode or Pi. Which is better, but still not the same as OpenAI were you can use the subscription on Pi without problems.
It's great at parsing documents inexpensively. For the few skills/plugins I've made, I usually instruct Claude to use Haiku for low-reasoning grunt work.
I have a zsh functions that calls claude code with haiku to suggest commit messages, is faster and the instructions are two lines.
I also have an "ask" script that I use daily to ask simple stuff, it can access websearch and webfetch, it's more than enough to parse logs, ask for commands, quick research on the internet, small stuff. https://github.com/mariocesar/dotfiles/blob/main/common/.loc...
I use haiku for things that needs to be quick, have really clear instructions.
with claude -p seemingly now using api credits I guess this approach will have to change unfortunately :/ I wonder what will be the best cmdline way to do things like these
I've been using GPT-6 Luna in some capacity for nearly all my agent workflows. It's just a really good model, and the pricing is cheap. If Haiku 5.5 is better, and the same price (under 100k context... which is a big caveat) i'd probably swap it.
not haiku, but luna - last week i used it for things like "read this historical dump of 15k support tickets and break them into categories that make sense, then propose help docs that i could write to handle the most frequent queries in each category"
> Haiku 5.5’s cybersecurity safeguards are more restrictive than Haiku 4.5’s, but somewhat less restrictive than those we’ve applied to other recent models. In cybersecurity, they permit a wider range of defensive tasks than our safeguards for Sonnet 5.5, but they still block penetration testing and other techniques more likely to be used by attackers.
If you block pentest or "other techniques more likely to be used by attackers", then what does "permit a wider range of defensive tasks" even mean?
Any defensive task that's meaningful is almost indistinguishable from legitimate red-teaming that then falls under 'likely to be used by attackers". If only they would just stop nerfing these models, that'd be great. No APT is waiting around for Anthropic's permission, so might as well let us have some cool stuff.
> Second, this week, we’ll roll out a new monthly API credit to all Max and Team subscribers for use on the Claude Platform. Max 5x users will get $100 in credits per month, Max 20x users will get $200, and Team subscribers will receive up to $500, pooled across their users. These credits are designed to allow our users to experiment with building tools, apps, and agents that call our API. They can be used on any of our models. For more information, see our Help Center article.
Did anyone read this? We get free API credits on some plans now
To correct myself. The price was not lower. It was up about 20% for the tokens. But, the big price hike was that it used a lot more tokens for the same tasks.
> Second, this week, we’ll roll out a new monthly API credit to all Max and Team subscribers for use on the Claude Platform. Max 5x users will get $100 in credits per month, Max 20x users will get $200, and Team subscribers will receive up to $500, pooled across their users
This is a very big benefit for me. I can now ship actual ai enhanced features behind my subscription without paying extra or fully relying on on-device models. I do worry that this is to soften the blow for user-unfriendly changes
This is them sneaking in taking the Claude Agent SDK off of subscription plans through the back door along with a model release. They previously wanted to do this in June, but backpedaled after huge backlash:
Even after the June changes there was some allowance to use agent SDK on the pro plan. This will move me to codex tomorrow if agent SDK is really blocked on pro
Those mfers. I'm using this for work! I use my work teams plan with pi so I can do all kinds of custom workflows that I can't in Claude Code. Time to convince management I need OpenAI instead.
How credits work with `claude -p` is a common question we're seeing. we're updating the faq now to make sure it's more clear. Will share updated docs soon
I hope people notice again that this is happening this time around.
Being forced through the non-OSS Claude Code with all of its quirks and issues is... such an exhausting use of force by Anthropic.
To the extent that you _can_ choose to disable telemetry and training on your traces in CC, it's not all that obvious what they gain by crippling your ability to use the subscription with other – better – tools.
It's also remarkable that it's coincident with OpenAI adding "Sign in with OpenAI", so that you can use your tokens with other tools.
Just to be clear - claude -p use will now consume credits rather than count against your normal claude code usage? This seems like a significant downgrade.
The update they added here[1] yesterday makes it appear that usage is covered by your subscription by granting you credits. That's the way I'm reconciling the above comment with their update on the site.
Sorry that that help center article was misleading; we’ve updated it to clarify that `claude -p` has not been removed from subscriptions as part of this change!
I've been using `claude -p` for personal use for a long time without issues. I do mean actual irregular, personal use though. Not using it to max out my quota, using it as main rather than sub or trying to be cheeky with it.
Are you able to speak about the degree to which non-Claude harness use is discouraged? Has Anthropic's disdain towards non-Claude Code harnesses settled down since the compute crunch + OpenClaw heydey?
More importantly, will my personal company account get banned for using a non-Claude Code harness?
Not sure if you're able to answer these questions, but I would appreciate it if you can.
I've been operating under the pretense that claude -p was removed ages ago in the OpenClaw era, it's back now? What a mess of contradictory information, hope yall can figure it out
This is massive. So on top of the regular usage, we now have USD 200,- to freely use via the API however we please, even resell? That is a statement, even knowing that inference does not cost them nearly as much as they charge, this is very developer-friendly. Does some minor de-risking for testing concepts. Terms seem to be reasonable [0].
Of course, they don't do this out of pure kindness, but I really struggle to see a negative for subscribers, especially given changing to another model is essentially frictionless, so once the monthly allowance is used up, you can still just decide not to use Anthropic models for the remainder.
The previous Haiku release (4.5) is a year old at this point. But this new one is pretty good on price/performance, so I assume it will be popular for at least a while.
> Claude Haiku 5.5 is our fastest model to date at each model’s standard speed, although it runs less quickly than our Opus models in Fast Mode.
Opus 5.5 runs 117 tps average on Openrouter, so it must be at least 10-20 tps slower for them to mention. IDK why they mention this as it does not help for marketing though.
https://openrouter.ai/anthropic/claude-opus-5.5
About time Anthropic released a competitive cheap model. Haiku 4.5 has been too expensive compared to its performance for months now (in fact I don't remember being too impressed even when it was released). This one actually looks worth using in some scenarios. If it's really as much of a step up from Luna as the benchmarks they've shown indicate, it'll probably replace Luna in my workflows. 100k tokens is a pretty low threshold before the price goes up, but I tend to use these smaller models for smaller tasks anyway.
One interesting thing is it took a look at the job at hand, and immediately delegated it to Opus 5.5. It at least knows what it isn't good at. Very fast though, and likely best used for small subagent tasks / tightly scoped work.
Neither of these look "good" to me. There is so much visual noise on the page, like someone turned the "AI Slop" dial to 11. In fact I prefer the simpler design Haiku made.
It's not really about whether the design looks good. It's about if the model can take the design given to it and replicate it in code. Opus 5.5 matches the designs almost to the pixel. Haiku built something else entirely.
Totally fair, but I'd encourage you not to look at the design so much as the task. This was a design that's part of a benchmark test suite specifically for image->html conversion. The dense visual noise / complexity / flowing svg shapes are things that most LLMs have trouble with.
Interesting that you have gpt-6-luna at $0.01 vs. claude-haiku-5-5 at $0.16 for this task. I see the score disparity though and I played them briefly. My takeaway from this is that the choice between Luna and Haiku 5.5 may remain nuanced. Luna may be a lot cheaper still and good enough for some jobs. Is that your read of the results?
Actually, I misspoke. At least as far as Pac-Man Bench, Luna does about as good of a job. The ghost logic's not quite as good, but it also makes a map that doesn't have nonsensical sections in it. So maybe call it a wash.
Sure, but presumably Haiku was distilled from the same training data. Part of this is seeing how much the capabilities degrade as their model size goes down.
This is great! Been using GPT 6 Luna for decompiling my childhood favorite game (Age of Mythology) and this means I can throw Haiku into the mix as well. 17352/21965 functions matched so far...
I want to get the original (Age of Mythology Gold Edition) running natively on macOS and then port it to WASM to run it on the web so I can easily play it with friends
Last time I gave that a try (without LLM assistance though) it was really hard as games DirectX calls cannot simply be glued to WebGL so performance was bad.
Probably. There have been dozens of examples of taking old games (Crazy Taxi, Super Monkey Ball, Quake, etc) and making WASM browser equivalents using AI to decompile them just on "Show HN" alone.
They often ship the original assets in a somewhat brazen disregard for basic copyright law even when the games are still for sale on places like GOG though.
Definitely, TW can "cheat" a lot with using sprites/very low LOD at distance, only streaming the corresponding units on battle load, and it's turn-based so performance isn't critical. Although obviously you won't be doing those 50000 unit battles on the web.
Off-topic sidenote: what's with all these new projects targeting WASM instead of native, even if packaged for desktop anyway?
Probably would work although if you release/distribute/allow others to use it, you're treading into copyright infringement. Afaik personal use if you own a properly licensed copy is usually okay.
How do you validate the functions are correct? I did something similar, letting it (mostly deepseek 4.1) translate from assembly to C but it commonly made mistakes, some really hard to discover and fix.
If it's like any other matching (game) decomp, you compare the output byte-for-byte to the retail binary (e.g. any of the projects on https://decomp.dev/). Hard part to getting started is making sure you have a comparable compiler, flags, & toolchain
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[ 3.1 ms ] story [ 98.1 ms ] threadYou'll know when the trend stops -> when the intelligence differential between smaller models like 7B starts to grow instead of shrink from 32B models -> that means 7B is getting about as smart as it can get. Then, 32B will follow next, then 70B, etc etc.
We haven't yet seen that at any size AFAIK.
> The cost of achieving a given level of AI performance has fallen about 47% per quarter since 2023, or 13× per year. [0]
0. https://epoch.ai/publications/the-plunging-price-of-thought
AI gets cheaper, people use it everywhere. Google searches, for example. Now we want to crack math problems and spend weeks with unreleased models.
If you used GPT-2, it'd be incredibly cheap. You basically can't use it for anything and it's simple to serve.
In both cases, still much cheaper than Haiku 4.5's $1 input / $5 output and these prices better compete with GPT-6 Luna. ($0.10 input / $0.50 output, but with no token threshold [EDIT: the threshold for Luna is apparently 272k])
For a while Anthropic has lacked a cost effective “cheap” LLM for summarisation, compacting, RAG helpers, etc.
These ‘ephemeral’ workloads are often under 100k tokens, or can be structured to be under 100k.
In some coding benchmarks, Haiku 5.5 beats Sonnet 5! (Especially implementation; do a well defined Jira ticket; etc), it’s really impressive how much intelligence per dollar has grown in just a few short months.
Open weights models giving a distant salute from afar
If you want to make it about 99% of real world companies, they are all on Gemini or Copilot anyway, nobody is going through legal and procurement to get models from dubious silicon valley startups when you have relations with Microsoft or Google or Amazon from ages because some benchmark is showing some minor digit benefit when vibe coding GTA 6.
They dont even know about Anthropic and think its just "AI". By the way this includes one of the largest power systems design firms in the world, who helps build many datacenters... Uses only MS copilot.
Companies as a whole, do not care about this technology, except tech companies and its adoption cant even be compared to CRMs. A company might buy a SaaS product with AI but most of them are not purchasing Anthropic subscriptions lol.
Maybe I should try the flash...
Even for simple tasks why use X if I know "Y Max" is available and on paper, better?
And why use something else when your favorite company releases something. Surely it must always be the best one to use.
AAI Index // Input // Output
Haiku 5.5: 43 // $0.10 // $0.50
Mimo 2.6 Pro: 46 // $0.43 // $0.87
Mimo 2.6 Flash: 38 // $0.10 // $0.28
Seems competitive to me? Plus then I don't have to manage multiple providers
If that was the case, then Claude Code would use smaller models for subagents. It doesn't. The subagent always inherits the parent model unless you tell it specifically not to.
There are plenty of workflows like translations where you'd easily be under the cap.
Your vibes don't appear to be supported by facts. From the announcement:
>> Claude Haiku 5.5 is priced 90% lower than Claude Haiku 4.5 for requests up to 100,000 tokens, and 50% lower for requests over 100,000 tokens. On Haiku 4.5, 90% of requests fell into the former category.
I'm asking to learn for a similar project, not to discount anything you're saying.
You can test with Anthropic's count_tokens endpoint or with https://crates.io/crates/tokwc
So, it is might be even worse.
For this application 100K token input is plenty.
Of course Anthropic and OpenAI at $0.10/M are still 2.5x the cost of Jev's $0.04/M.
I use Luna for this day in and out and its excellent - if Haiku is that much better I will be changing things up.
"less context is better and if you can't get stuff done with less yur bad" is the worst argument ever.
it might be pure luxury to your eyes, but it's great to not require the use of a special custom harness that transcribes everything into emoji and compresses everything into barcode images.
it's great to have a million token context to throw a large project into. If I need 100k just about any current gen consumer GPU in the world has very good models that I can self host for 100k context, limiting myself to 100k on someone elses machine seems to be missing a lot of the point unless the model itself is extraordinary.
The services are priced this way because larger context has significantly higher costs. That is a fact about the technology and it is true for every provider. So moaning about it isn't useful.
Presently I'm using sol-high for the default agent which does orchestration and a lot of smaller investigation and coding tasks itself. sol-max for planning and review. Luna-max for planned coding and general tasks. I also have a $10 minimax plan and use M3 for exploration and library roles, but I could probably be using Luna for that just as well and still only very rarely run into usage issues.
I don't use any plugins or skill libraries apart from Caveman and I'm not sure how useful that really is anymore so I'd start without it so you have a baseline to compare. I do think it reduces context usage a bit but I haven't measured it recently. Caveman also includes some team, agent & investigation skills - again they might be helping but I haven't re-evaluated since like 90 days ago.
If there was no Luna you would see only the >100k pricing, but because we have Luna, they had to lower price for something.
GDPval-AA v2.1 as of now: 1620
GDPval-AA v2.1 for Haiku 4.5: 735
The 100k tokens pricing makes sense, looks to be a hedge against OpenAI's decisions API and Jev or its open source alternatives that are springing up.
Nice release, congrats to Anthropic.
She said she was using Haiku 4.5 because she was advised to be careful with the spending.
I hate that model so much lol.
Anthropic has really been doing all the right things in the past few weeks, while OpenAI continues to fumble the bag.
https://support.claude.com/en/articles/15036540-use-the-clau...
That's not a good sign for Conductor...
Wait what? This has gotten their blessing?
I also have an "ask" script that I use daily to ask simple stuff, it can access websearch and webfetch, it's more than enough to parse logs, ask for commands, quick research on the internet, small stuff. https://github.com/mariocesar/dotfiles/blob/main/common/.loc...
I use haiku for things that needs to be quick, have really clear instructions.
Planning on doing flash analyses of PRs that impact evals in some way, and then post comments on GitHub whenever there’s flaws in them
( https://evalship.com )
I've been using Luna, but I'll probably switch to Haiku.
used <10% of my 5hr limit on a $100 codex plan.
then a model named for both.
Same name, three lives.
Bigcorps re-using known names.
A worrying trend.
If you block pentest or "other techniques more likely to be used by attackers", then what does "permit a wider range of defensive tasks" even mean?
Any defensive task that's meaningful is almost indistinguishable from legitimate red-teaming that then falls under 'likely to be used by attackers". If only they would just stop nerfing these models, that'd be great. No APT is waiting around for Anthropic's permission, so might as well let us have some cool stuff.
(sadly Mistral Large 4 isn't up to par - but Mistral serves GLM at 130 tps!)
Did anyone read this? We get free API credits on some plans now
This is a very big benefit for me. I can now ship actual ai enhanced features behind my subscription without paying extra or fully relying on on-device models. I do worry that this is to soften the blow for user-unfriendly changes
https://support.claude.com/en/articles/15036540-use-the-clau...
https://code.claude.com/docs/en/headless
So, as written, yes.
Being forced through the non-OSS Claude Code with all of its quirks and issues is... such an exhausting use of force by Anthropic.
To the extent that you _can_ choose to disable telemetry and training on your traces in CC, it's not all that obvious what they gain by crippling your ability to use the subscription with other – better – tools.
It's also remarkable that it's coincident with OpenAI adding "Sign in with OpenAI", so that you can use your tokens with other tools.
You might be right and they will change this in the future, but that's speculative
[1]: https://news.ycombinator.com/item?id=49999702
[1]: https://support.claude.com/en/articles/15036540-use-the-clau...
Anthropic is among the worst, their linux kernel hack is a prime example, didn't even count the "CVEs" to see Mythos can't count either
https://www.youtube.com/watch?v=NnV_cWeoo5Q
https://www.cbc.ca/news/canada/british-columbia/mother-jones...
ANT annoys me more with their ai psychosis on steroids
More importantly, will my personal company account get banned for using a non-Claude Code harness?
Not sure if you're able to answer these questions, but I would appreciate it if you can.
The change was "paused" pretty quietly.
Of course, they don't do this out of pure kindness, but I really struggle to see a negative for subscribers, especially given changing to another model is essentially frictionless, so once the monthly allowance is used up, you can still just decide not to use Anthropic models for the remainder.
[0] https://www.anthropic.com/legal/credit-terms
Opus 5.5 runs 117 tps average on Openrouter, so it must be at least 10-20 tps slower for them to mention. IDK why they mention this as it does not help for marketing though. https://openrouter.ai/anthropic/claude-opus-5.5
Haiku 5.5: https://html.non.io/lcars-haiku-5.5/
Opus 5.5 for comparison: https://html.non.io/lcars-opus-5.5
Designs it was building from: https://diffui.ai/app/canvas/5093e689-1e74-4f26-b632-2a4500f...
One interesting thing is it took a look at the job at hand, and immediately delegated it to Opus 5.5. It at least knows what it isn't good at. Very fast though, and likely best used for small subagent tasks / tightly scoped work.
It's meant to be a good test, not a good design.
Considering the price, no model comes close to being as good as this. However, it did take an extremely long time.
TIME 19m COST $0.16 https://jonclegg.github.io/pacman-bakeoff/#claude-haiku-5-5
All results: https://jonclegg.github.io/pacman-bakeoff/
Hasn't this always been the case with Haiku?
Like could total war become a browser game?
They often ship the original assets in a somewhat brazen disregard for basic copyright law even when the games are still for sale on places like GOG though.
Off-topic sidenote: what's with all these new projects targeting WASM instead of native, even if packaged for desktop anyway?