I used openrouter to send same prompt to qwen, derpseek, gemini and grok and found that grok does good research and produces less bullshit, especially when prompted to be critical of an idea
This has been my experience as well. Grok will end tasks almost immediately and claim "Done!". It's definitely the laziest and most "dishonest" of all the models. The others aren't perfect, but I can't use Grok for any serious coding task.
I would consider this a positive. I'm not interested in a company that wants to prevent you from using a tool that you're paying for.
The value of the internet is that people can share whatever they want, and use software how they want. This will mean that some people will abuse that. This is the tradeoff of a free society.
Apparently Grok 4.7 is 40% bigger than Grok 4.6, but the price ($6 output token, $2 input) is the same.
Given that the decrease in their margin and the fact they delayed the release of Grok 4.7 almost two weeks past the original date, they must not have been happy with the results. And they waited until last minute, when Opus 5.5 is rumored to launch tomorrow. I imagine it will blow Grok out of the water benchmark wise.
I have become skeptical of benchmarks though. Since 4.5 Grok has been performing really well for me. Grok 4.5 solved some issues setting up a buildroot system that Fable 5 couldn't do. I find it's also much better at frontend web development, though Claude is much better at ruby on rails.
My favorite part of the new Groks has been how they speak in plain english. I simply cannot stand Claudish or Gptism. GPT is almost as bad as claude sometimes with how it likes to use big words in confusing explanations. Still, nothing beats Claude 3.5 and 4 with explaining since it seems all models have regressed. I wonder if Grok 4.7 will also regress with English because of all the RL.
I was going to say the reverse - claude has been the less satisfying normalized by benchmark for me in the last year. Both astra and fable have their quirks, but I am 90% codex this year up from 10% last year.
It's not just about benchmaxxing. Sincerely targeting those long-autonomy benchmarks is questionable in the first place, because naturally it drives the model to assume more and more about what you want.
The target market for frontier models is CEOs who want to lay off entire departments of their company. So the long autonomy benchmarks would seem to be sending exactly the right signal.
I've been getting a ton done with Fable as the supervisor and astra as the implementer, with opus for adversarial reviews of the astra PRs. You can use terminal multiplexers with custom harnesses to allow Fable to start codex sessions and send instructions / read instructions / allow/deny actions. It's pretty cool!
codex has an option to expose itself as an MCP. You can also use something like OpenCodex to bring Anthropic models into Codex as any other selectable model.
The reason I did it the way I did was so that I can still use the codex and claude code subscriptions vs paying the API cost. Can you do that via OpenCode?
I totally agree, it’s like that as models become more intelligent, they are less understandable by most of people... but aren’t we humans doing the same?
That's half true. A very smart model should be able make good explanations, which include simple understandable prose. That can should be possible even as its thought process gets more alien.
Agreed. The more knowledge you amass on a subject, the more important it becomes to be extremely specific and nuanced - or your communications end up being incorrect. You become better at expressing your thoughts, but harder to understand.
The weird thing is, that's not what AI models seem to be doing. The prose is just weird.
> You become better at expressing your thoughts, but harder to understand.
This happens most though when the speaker doesn't (or care to) understand their audience.
Eg i find effective communication requires expertise in both the subject matter domain but also the reference of the listener. Eg in ELI5 framing, if you don't know what information 5yr olds are expected to know you'll do a poor job at an ELI5.
It often feels like Claude does poorly at both framing the response relative to what it "thinks" the listener knows, but also the prose is... sideways, just weird as you said.
I find people tend to lack nuance in their communication more often - leading to problems and misunderstandings.
If I don't grok an elaborate explanation, I can ask for clarification. If it's explained to me in an overly simplistic or unnuanced way, I'll walk away with a false sense of understanding.
That said, I'm sure we all have very different concentrations of these types of people and problems around us. I've definitely met some engineers who seem to actively try to make their language incomprehensible
They’re training the models on generated slop. I’ve done a comparison on samples from the different versions of Claude and they’re worlds apart. The ancient, supposedly obsolete models speak normal English.
Look up the output-style setting, which is a bit stronger than putting it in the system prompt. The new "concise" setting is better than the default but in practice, Claude is a very stubborn model when it comes to these patterns and they're really hard to eliminate, mostly you can only hope to mitigate.
As for the wording of the prompt, you're pretty on point, I created a custom output style targeting mostly the first two you have there. Some people have wording that demands a certain technical standard or uses fancy words to describe what to avoid, but I haven't seen evidence those work better than asking plainly and I suspect the opposite: LLMs mimic the user to a degree so talking to it in terms of technical specifications and fancy words is an invitation to get them back.
> Part of intelligence is knowing your audience and communicating efficiently.
Bingo! And on this axis many SOTA models fail miserably. These things are acting on my behalf under my direction. All the supposed intelligence in the world means fuck-all if nobody can understand it.
And like somebody else said… when meat-based humans talk like Claude does, it almost always means they either don’t understand what they are talking about, or are actively trying to conceal something and are a fraud. Not always, but almost always.
Yeah just today it told me in a snarky way that my CPU (7940HX) doesn't exist and that I must have misread it and it's either 7945HX or 7940HS. Yes, AMD (re-)branding CPU models makes things difficult but I thought we are passed AI models making such egregious mistakes
Perhaps you haven't had the chance to use it, but 3.8 flash is the best model for talking too. Even routing Claudes output through 3.8 to have it explain whats going on is a breath of fresh air
I'll have to try Gemini Flash for coding. The reason I haven't I used Gemini for coding is last time I tried it couldn't call tools very well.
I am a huge fan of Gemini Pro for chat... gemini somehow just knows the most obscure stuff. I'll double check something Gemini said and find the source is deep inside a hard to access scientific paper. Google just has the best index of the internet.
Even if you aren't coding, you really need to double check its answers. Flash 3.8 hallucinated a Keyence camera's max operating temperature for me, last week, and backed it up with "references".
It's still my favorite model for most non-coding stuff, though.
I remember the Gemini 2.5 Pro series was the best at text, but failed half of it's tool calls. They really redeemed themselves with the 3.5 Flash series, it's great. I love how fast it is.
Now if only they could offer a subscription service for it...
Makes sense. When Google Jules was new, I experimented with it for a while and it was embarrassing how often it flubbed tool calls, often repeatedly. It tended to forget the working directory so almost every command needed two tries.
FYI a quick fix for claudish is to ask for the response to be in ASD-STE100 (Simple Technical English). Then it is far more readable. But I would agree that this is an annoyance and shouldn't require user workaround to get something readable.
I don’t think system prompts are particularly reliable way to do much at all. It’s better to put it as a hook after each response, or a skill at least so you can trigger it at will if you don’t want it everytime.
Anthropic has probably RL’d the system prompt into nothing because of their fear of the user being able to control the model. If it listened to you about the slop language, it might listen to you if you asked it to help you with no-no tasks.
Your best bet is to use hooks and inject it after every file edit / response by first running the content through haiku and asking if it is asd 100 ste.
It burns more tokens but is the only way to get tolerable text.
I think this is more a meme than anything else, for a couple reasons:
First, after a while it's just as grating as Claudeish.
Second, my hunch is that it constricts the actual thinking of the LLM, like the same way that Newspeak does in 1984. It shrinks the range of thought that can be expressed if used as an input.
I think the real way to do it is to have another Claude entirely deal with the user as a liaison, but to keep the thinking in whatever format it came in.
Latent space reasoning, if you think about it, is exactly this to a crazy degree: why even formulate a thought as words if you can just keep it as matmuls until the user needs it? And then, if the user needs it, have it always specifically formulated for the user by another LLM rather than constrict its range of thought? Anyway, that's my take.
A few weeks ago two "Claude wrapper" projects showed up here within 24 hours of each other. Both seemed to just sand the details away--useful if Claude is too wordy, I suppose, but seemed to also lose real substance.
This is the same reason why I am a bit skeptical of LLM superintelligence. LLMs in the end have to operate in natural language concepts and the complexity of natural language is bounded by limits of human cognition. I'm sure super advanced AI could use concepts that humans not only have no words for, but might not be able to understand alltogether. As such if my thesis is correct, the only way forward for true superintelligence may be getting rid of natural language COTs.
> LLMs in the end have to operate in natural language concepts and the complexity of natural language is bounded by limits of human cognition.
I don't think this is true.
They have to express themselves as tokens. The meaning of those tokens doesn't have to be text. See any model that can handle images/video. Also, I don't think math, svg, etc, are "natural" language.
And, only the final expression is tokens. The intermediate layers, with the encoded concepts, aren't "natural language".
But, to address your concern (which nobody can disagree with, since even humans can't fully express through text/pictures), potentially: https://news.ycombinator.com/item?id=49758615
> The model isn't limited to concepts that can be expressed in natural language.
How do we know for sure? We don't even know how the emergent properties we see actually emerged?
For humans we know for sure that people sometimes have concepts that they have no word for (the reason the phrase "It's on the tip of my tongue" is a phrase, after all).
We don't know this for LLMs. When it makes new phrases, it's always a mixup of two existing words hyphenated (aside, that also seems to be the limits of SOTA models creativity - join two unrelated words together with a hyphen).
LLMs never respond with "It's on the tip of my tongue" type responses, indicating it has a concept but cannot remember (or does not have) a word for that concept. Every human, pre-speech-age, has managed to express or convey concepts that they had no word for.
So, no. I'd need a citation, preferably multiple, that did the trials and found that a model can generate concepts for which it does not have any words for.
1. Is natural language holding LLMs back by some %?
2. Is natural language serving as a hard gate that will prevent LLM intelligent progressing past some specific point?
The answer to 1 seems like an obvious yes to me.
Your thesis says the answer to 2 is "yes." That doesn't feel right to me. Think about all of the humans who have pushed various fields forward: Einstein, Newtown, Bach, whoever. If natural language doesn't prevent an entity from surpassing humans in one intellectual field, why would it prevent an entity from surpassing humans in all intellectual fields?
(To be clear, I'm not claiming superintelligence will or won't be achieved; I'm considering your specific thesis about whether or not natural language will be a hard gate)
Each letter has it's own token. LLMs can invent new words if they want to, just like humans do. All the fancy words we use today in science did not exist even 100 years ago. And that didn't stop us.
The fact that it might affect quality is something that is on my mind. In fact, I have mainly been using Astra after being primarily on Claude for several years exactly because of this annoyance. Ultimately, the model (at least for coding purposes) should just... not have this writing style.
I do think an infrastructure where another Claude retranslates the output would be better. Oftentimes I forget to put it in the actual prompt and when I receive back 8 paragraphs of Claudeish I ask for it then.
I would have to disagree that it gets as grating as Claudeish though. Its just direct and professional instead of ring-around-the-rosy clickbait.
The Claudeish drove me back to OpenAI and Codex as well. I find the Claudeish can be improved but it’s impossible to fully mitigate. Opus is unusable for anything I’m going to read.
“I would have to disagree that it gets as grating as Claudeish though.”
It’s hard to imagine anything more grating than Claudeish. To quote Rainer Wolfcastle, "My eyes! The goggles do nothing!"
Ask for output to the user in "simple technical English", let it think how it wants. It is hard to believe all the flowery prose is actually helpful to the model.
I agree there is probably some impact on output quality when doing this (Claude seems to embedded key signals all over the writing, which is much or what makes it horrible to read). However this does work well as a "second pass" over technical docs or designs along the way
> my hunch is that it constricts the actual thinking of the LLM
I've found that any prompting any constraint on output (length, style, vocab, even simple formatting) not only places additional cognitive load on the model, which burns some of whatever cognitive budget is available, it will also often skew the output in other subtle and completely unrelated ways.
Since I found this artifact interesting, I did some pretty extensive experiments a couple months. The increased load is real, although the it may not be apparant if you're not near any cognitive boundaries. The subtle skew, however, seems nearly ever-present regardless of load.
When I noticed web chat LLMs wouldn't number section headings correctly and consistently, I began experimenting with modifying the user prompt over a period of weeks. My usage at that time was research and learning not coding. In long, detailed sessions with branching sub-topics and deep follow-ups, I found it helpful if the LLM would number each major section and letter each sub-section in replies. Simple markdown formatting that most web chat LLMs do sometimes, but not consistently nor uniformly.
While extensive, my tests were just following my curiousity, not controlled, exhaustive or well-documented. I identified about a dozen prior sessions of varying length and complexity to test and downloaded them with a browser add-on. I then removed all other user prompt instructions except for the formatting instruction. A test would typically involve changing the wording of the formatting instruction ranging from brutally simple to detailed and complete, then starting a new session, seeding one of the test sessions and continuing it. To get a feel for baseline inter-session variation, I also tried running the exact same prompt/session multiple times back-to-back, at different times and on different days of the week.
Once I identified a promising prompt candidate, I'd make it the formatting instruction in my regular, daily-use prompt for a few days. I quickly got a feel for how seemingly minor user prompt variations impact response quality, compliance and tone across fresh sessions as well as those in various states of context rot, drift, decay and cliff (<--my ni cknames for the distinct flavors of session degradation than technical terms of art).
My overall conclusion was that every instruction, no matter how minor or unrelated it seems, has some, real impact on the model's cog load, attentional focus and/or attentional weight budget. Both how these impacts manifest and what causes more or less impact is often extremely counteriintuitive. To more fully understand this, I eventually, got to the point of testing null case variants, such as the entire user prompt being one sentence completely unrelated to text formatting or the session topic, like: "Don't reference the cartoon character SnagglePuss" (in a deep dive on ancient Sumerian clay tokens). Similarly, a simple one sentence prompt requesting something the model already always does naturally also has a cost (eg "Capitalize proper nouns"). As others have observed, heavy emphasis, absolute prohibitions or emotional weight in prompts also tend to have outsized impact in both skew (impacting unrelated output tone/style) and in accelerating session degradation. "Avoid referencing SnagglePuss when you can" would have equal compliance but fewer downside impacts than "NEVER reference the cartoon character SnagglePuss" in sessions starting to degrade.
There were also surprises, such as when I was scanning transcripts of an older, longer session and noticed the LLM was doing number formatting almost perfectly. On looking at the active user prompt at the time (I keep a log of every user prompt change I make for every model), it didn't even reference formatting at all. More experimentation showed it a result of the LLM gradually mirroring my consistent use of formatting structure in my prompts over a long session (in which I never mentioned anything about formatting). Unfortunately, that mirrored trait doesn't persist to new sessions and reaching that point requires a substantial number of rounds burning quite a bit of context window.
I tried this a while back and I felt like the result was the same weird shoehorning of ideas into language, just with a different vocabulary. I’d really like for it to work, though.
I created a custom output style based on this (borrowing some from github.com/AminBlg/SimpleEnglish) and I've found it to be better than the default or concise output styles, but still not as good for me as current GPT or Gemini models when it comes to communicating.
Do tell why the downvotes? It actually works. When you tell it to adopt a certain style, just be sure to also tell it not to change the core truth of the output, it must still convey the original intent. It is surprisingly good at outputting Warhammer styles, which is very enjoyable in a dev environments or system admin sessions. Also put on some warhammer soundtrack in the background and you can easily zone in.
I hadn’t heard of ASD-STE100 until last month when the TinyGo AGENTS.md added it as a requirement, along with some other tidbits to improve life for maintainers.
It’s been really productive and I’ve been asking my agents to communicate using it more and more. I believe it’s relieved my cognitive load a bit while working with them.
> I find the post cursor groks are phenomenal at frontend web development, though Claude is much better at backend ruby.
Wonder if we'd benefit from a much more specialized + task-specific benchmarks to paint a clearer picture like this. A benchmark solely for frontend, ruby, hardware, etc.
I've noticed Chatgpt 5.6 Sol High, on the chat interface, inventing words that are a mixture of Portuguese and English. Like "hardcodar" a mix of "hardcode" and the most common verb ending in Portuguese "-ar". Some don't have a single google hit
Do you have any connection to Portugal? I imagine if you have Portuguese in any of your prompts that might bleed into your user profile which becomes a part of every prompt. Alternatively it might use browser language settings
they are bugs with the model. Sol regularly glitches out and will type out random stuff even in tool calls. I'm guessing this is when they have capacity problems
Grok and Zai have both been excellent as adjunct code-reviews, on their cheapest plans, for me. Fable plans, Opus writes, Codex as primary reviewer, but Grok and Zai usually find something worth fixing that the others have missed. Both are well worth whatever the $20 or so I'm paying for them
I do not understand how anyone can seriously use a tool that has "Be funny and irreverent when appropriate" baked into the system prompt.
I don't want to waste money because my calculator is cracking jokes. They don't deserve their paltry 5% marketshare or whatever it is they have currently. I'm not even getting into Musk as a person or the horrid things we've seen Grok spit out on twitter. I just don't trust his companies with my data and I have seen very little evidence that it's ever the best tool for the job. I'm sure those cases exist but I can't imagine it's worth it.
I am on the exact same page as you, but there is definitely a market for LLMs which speak more conversationally and less like Claude! Non-programming use cases abound and most users don't like the rigid, exact tone that engineering demands.
I do wonder why a frontier model does this to be honest. It still does good coding wise, but it seems strange to me. r/Claude is full of "load bearing" jokes in every thread.
> My favorite part of the new Groks has been how they speak in plain english. I simply cannot stand Claudish.
Grok has its own feel too. It's not as bad as Claude, but one of the things that bugs me is that it is far too terse.
It regularly seems to come up with terms and descriptions for things in its chain of reasoning and then uses these terms in its output assuming you understand what it's talking about.
I find I often have to ask it to re-explain what it means.
I’m pretty sure the big bois don’t do it because it would undermine “confidence”.
Seeing a model output “Oh I should just delete blah. Wait blah is a production service, I shouldn’t touch that. Maybe I can gain access to blah? Oh the aws cli isn’t signed in to blah. I see kubectl has access to blah though! Wait, I should ask user permission first.”
I stay much more hands-on when I'm using models that display full reasoning traces. And I tend to get more things done as a result, because I know exactly when it thought of a good solution that it talked itself out of because of some invalid assumption.
Could be something very stupid like - "I don't have ffmpeg available here. Should I install it? No, I can't. I'll proceed doing something that will take me 100x more tokens and wall clock just to avoid adding a dependency." I can then just stop and say - you've got nix flake there, just add it.
That's impossible with western models. The only way is to ask why it did something stupid when it already spent 50% of your weekly quota and produced millions lines of slop.
Idk I feel like the more likely answer is to prevent distillation. Having the thinking is definitely better UX (oftentimes, I don’t know if Codex is just hanging, which it often does, or working in silence).
Ah, but you CAN see the thinking if you are willing to risk your account being banned. You just have to expose a "tool" with a specially crafted definition.
You can double click on the 'thinking' text and it will expand and you can read it. The problem is that it will often have multiple thinking/tool call sections and it can be a needle/haystack problem to find the one with the thinking you are interested in.
SpaceX is a defense contractor (I don't mean this in a bad way). When the whole DoD/Anthropic thing flared up, I can guarantee you that SpaceX.ai was the first company invited to take their place as DoD AI provider.
I suspect that they specifically train Grok to be able to work well with military personnel -- speaking the way they speak: brief, to the point, efficient communication. Personally I really like this. Claude sounds like some demented clown from the marketing department.
> It regularly seems to come up with terms and descriptions for things in its chain of reasoning and then uses these terms in its output assuming you understand what it's talking about.
> It regularly seems to come up with terms and descriptions for things in its chain of reasoning and then uses these terms in its output assuming you understand what it's talking about.
GPT does this all the time, too (both Sol and Astra). I constantly have to tell it to not use terms that were not part of the initial prompt.
I don't know how people are managing hands-off with Sol/Astra over long horizon tasks. It will come up with 100 new concepts/terms and then use them as if you've been following every Edit call for 24 hours.
It’s probably an assumption baked into the training pipeline from that ancient time six months ago when we mostly were manually following each edit tool call.
> I constantly have to tell it to not use terms that were not part of the initial prompt.
Hah, yeah even when you put it in AGENTS.md or a skill.. constantly having to remind it.. "what does AGENTS.md" say about doing that?".. Thinking.. Thinking.. "Oh, it says I should never do that, I'll remember that next time.."
Really? I’ve found that when working on my game I can’t let Grok write any text over about 8 words (tooltips for instance) or it will write the most horrendous incomprehensible slop - totally full of invented jargon, Claude-style weird grammar
You don't have to go into conspiracy mode about this. Musk clearly stated on X the other day that they delayed the release because they got some thing wrong with the RL behavior.
I actually love fable 5.1 prose. Before I was always on GPT models and I despised the writing. Actually I despised the writing they would do in the markdown docs, the writing in the responses was good. But there was always this weird thing that in the responses the model would write very clear explanations. Then if you ask it to document it what it ends up writing in the .md files is way too verbose, robotically lists facts and omits reasoning and gotchas. Too much unnecessary detail and too little detail it actually needed to capture. I absolutely hated GPT writing. Fable 5.1 though has the same concise, dense but logical and clear prose in both the responses and the documentation and the in-line code comments (oh yea Sol never wrote in-line code comments; and most of the time it deleted existing comments of code it didn’t even touch).
So yea, I find fable 5.1 writing to be excellent everywhere. I still use Sol daily though, but for things like config, quick research, fixes, code review etc. Feature work and writing is for fable 5.1.
None of these technical details are with hashing out because the more people who let Grok train on their conversations, the more power they give an avowed white supremacist whose actions resulted in the deaths of over a million black and brown people, mostly children and babies.
Flag (comments like the above extremely clearly break the guidelines which is what flagging is for), downvote, and contact the mods if there's a pattern of behavior. HN is partially moderated by its users!
Another great thing about Groks is sometimes they just simply do what you want to do. There's no fuss about refusals or it doing random stuff you didn't ask for or it speaking in slop or anything like that. They probably should pay a bit more attention to safety/alignment as they get better but they're great models for many use cases
Interesting to note that mimo v2.6 pro has been released at I think 1 trillion parameters and does mostly better or equal than grok 4.7 which is a 2 trillion parameter. Both of which got released on the same day.
That’s a factor of half the parameters. I would be curious to see more on the focus of smaller parameters model and pushing its frontiers
My favorite part of the new Groks has been how they speak in plain english
And the fact that Grok is the ultimate grandmaster of parallel tool-calls, routinely kicking off four or five at once. Overlapping the latencies makes a huge difference in responsiveness.
I also like how Grok is trained to print a short one-sentence descriptions of what it's doing before each step. Like an airline pilot calling out observations for the black-box recorder to hear.
Deceptive? An extremely quick google search would answer your question. OpenAI pulled out of Cursor before they released Astra so it never got that benchmark.
Think we all can agree he has had staggering successes, but they have all come from having massive capital from Paypal which wasn't anything super innovative, it just solved a convenient problem at a convenient time and was awarded handsomely. Elon has put his capital to work in various ways to become successful, not all of the ways being morally sound.
In my experience Grok especially inside Grok build is pretty solid choice, it’s a no nonsense model and stays on its course. Another surface where I truly enjoy the experience of using Grok model is Grok bot
I've had really good experiences with Grok 4.6 and grok build. I've been playing around with tscircuit and it can write code with an understanding of spacial reasoning, while also importing cad components from different file formats into tsx, I've been having claude come in and try to error check it and so far claude hasn't found anything to improve in my three projects.
I'm excited for 4.7 although I share skepticism with other users whether 4.7 will be significantly better, since they didn't raise the price.
Yah, I am pleasantly surprised at Grok Bot. Hopefully this improves CUA which has been a touch lacking w/ Grok 4.6. Grok 4.6 works but is slow compared to stuff like Astra Light.
Nice to see this release cadence increasing and some continued improvement in quality. I am guessing these models are basically still outcomes of the cursor team integrating with the massive amount of compute they now own: I’d imagine we will see significant step up improvements with grok 5 later this year as the team gets more experienced and confident with larger training deployments. Here’s hoping for another competitive frontier model!
Apparently you don’t care that Grok’s owner bought an entire social media platform so he can advocate for “re-migration” in europe, also known as the forceful ethnic cleansing of non-white immigrants.
I personally cannot ignore that, and I will not stop reminding people of what they’re supporting when they use this model.
> for “re-migration” in europe, also known as the forceful ethnic cleansing of non-white immigrants.
I'm curious if you feel the same about re-migration of Belgians from the Congo?
Personally I think it's fine for any country to vote to control immigration as they see fit. I think Japan is a good example of a relatively xenophobic culture that deals with this fairly and thoughtfully.
> I'm curious if you feel the same about re-migration of Belgians from the Congo?
Can't say I've ever heard anyone implying that colonialists leaving Belgium was unjust. Colonialists is actually not the right word, more like extended occupation, only slightly better than the enslavement of the Leopold II era. The Belgian's were less than 1% of the population and all but an ancillary amount worked in exploiting the native population.
You're right, I don't care and I certainly didn't ask for your political opinion or validation. This is a tech news website, not the place for your false and unrelated political tirades.
Technology isn't apolitical. You can choose to ignore the politics if it helps you sleep at night, but Elon's got three-letter agencies reaching up his ass like he's a Sesame Street puppet.
I didn't miss anything. The parent that they can choose to ignore it forever, it's a perfectly valid option. It just won't disentangle tech from politics.
The fact that Elon Musk's companies take contracts from the CIA and NRO is not flamebait. It's context that informs how we evaluate future SpaceX ventures.
> Grok’s owner bought an entire social media platform so he can advocate for “re-migration” in europe, also known as the forceful ethnic cleansing of non-white immigrants
is not "basic morality" - it's a mixture between being unprovable and outright false (and nothing else - therefore extremely obviously can't be about morality), but was posted because the author was trying to incite political flamewars on HN (as are you, given your other comments), which again, is against the guidelines.
You should review them, because you clearly don't know what's in them.
Musk also shut down USAID for absolutely no reason at all which will cause the deaths of hundred of thousands of the poorest people in the world. Not a good look for the richest person in the world. Musk also sounds like a complete moron when he tries to justify why he did it.
Grok did ask its users what it thought about the right wing fever dream of genocide of white people in South Africa so its definitely relevant to one's consideration of the product.
You’re going to have to keep dealing with it, sorry to say, until you stop engaging with the work of a fascist. We’re not going away, and it’s just going to get worse.
Flamebait I guess but I think it is good we have several options to choose from already. If you don't like Elon there are several other models, and each can pick the one controlled by her favourite supervillain.
My money's on Zhipu AI. They've got a great track record releasing open weights and have a much cheaper API and subscription. In terms of efficiency, I think the frontier has already been ceded to Chinese labs.
For some reason reasoning effort low and medium used similar numbers of tokens, and xhigh used less than high. I think I need to try without OpenRouter in the middle.
Are there good tools for doing context audits? I feel I have no good way to visualize what a new session is getting by default in a given repo without crawling through every potentially included markdown file
exactly, the benchmark just needs to be downvoted into oblivion each times it's posted. The outcome is not deterministic and the model needs to determine what level of detail is appropriate for an svg. There is no wrong answer to this unless it's obviously un-Pelican-like.
It’s a bad look to be evaluating models built in the furtherance of fascism and white supremacy. As this is a closed model, any support and training of it ultimately benefits those ends.
I tried in Omp (Oh-my-pi), and so far it's really problematic.
It will loop in thinking mode ("Let me implement those fixes: Fix 1, Fix 2, Fix 3 .... Fix 80, Fix 81"), ignore the AGENTS.md instructions, corrupt plan files, etc etc... I have 5.6 Sol as advisor/watchdog, and it blocks every turn, I never saw this. Quite a shame, 4.6 wasn't so bad.
- it allows different models within one session via roles (I only have API, so pay per token)
- it's much more likely (ime) to use the LSP over grep for determining how code fits together
But I agree a 20k+ starting context is way overkill.
I find it's very hard to get information on harnesses people are using. I have to stay model agnostic so I avoid claude, codex, cursor, etc. I've used and tried opencode, which worked well, but obviously lacks the above features.
Does anyone have a resource for following what people are actually being productive with? With so much vibe going on it's hard to separate the wheat from the chaff.
Either way, the fact that xAI or SpaceXAI or whatever the name is, I can commend the team behind it on their rapid ascent and progress by being close and or on the frontier in several respects.
There for awhile it seemed like we’d have 3 big competitors but then Grok 4.2 or 4.4 was just diabolical while OAI and Claude continued their significant improvements. Grok was/is so bad that I was convinced musk was gonna shut it down and just fund Anthropic compute once they reached their compute agreement.
"Privacy#
All these models are hosted in the US. Providers follow a zero-retention policy and do not use your data for model training, with the following exceptions:
Big Pickle: During its free period, collected data may be used to improve the model.
DeepSeek V4 Flash Free: During its free period, collected data may be used to improve the model.
MiMo-V2.5 Free: During its free period, collected data may be used to improve the model.
Laguna S 2.1 Free: During its free period, collected data may be used to improve the model.
Ling-3.0-tiny Free: During its free period, collected data may be used to improve the model.
LongCat-2.0 Free: During its free period, collected data may be used to improve the model.
North Mini Code Free: During its free period, collected data may be retained and used to improve the model. Do not submit personal or confidential data. See the provider’s Terms of Use and Privacy Policy.
Nemotron 3 Ultra Free (NVIDIA free endpoints): Trial use only — do not submit personal or confidential data. Your use is logged for security purposes and to improve NVIDIA products and services. The logged session data for improvement purposes is not linked to your identity or any persistent identifier. For more information about data processing practices, see the Privacy Policy. By interacting with this endpoint, you consent to the collection, recording, and use of such information and the NVIDIA API Trial Terms of Service."
just checked, yes they don't seem to provide that information, most other providers are advertising fp8 or fp4 which is okay, but "together" doesn't, so they are likely using fp4
update: coreweave/fp8 is at 191 tps, launched this morning, but really bad cache hit rate (~60%), coreweave is good for privacy but let's hope they improve cache
I use it on fireworks which is US/ZDR and pretty reliable. We run a few hundred million tokens/day through it for dollars. Many are cached, which is super duper cheap.
For me it works well for agentic coding tasks and terminal/unix/bash (in cursor and grok build); it's also token efficient and cheaper than gpt 5.6. It's def not as good as Fable for me (I haven't used Astra much, can't comment). So it's not the cheapest, not the most capable, but it has a good mix of it for my backend, go, infra work.
The voice is the weird part. The early Grok 4 models had a very distinct presentation unlike anything else out there. Then suddenly it made a big jump in coding ability and started sounding just like every other model.
> it's also token efficient and cheaper than gpt 5.6.
Deepswe results show that grok 4.6 is more expensive per-task and consistently scores worse than: luna xhigh, glm 5.3, astra low, sol high/xhigh, opus 5 medium.
Grok also used almost 3x as many tokens/turns to complete tasks than all of those models (besides luna), so it takes way more time to complete a task.
There isn't much reason to use Grok at all, it's gotten better but it's still worse than every other player in the field, which shouldn't be a surprise considering until about a year ago they were just buying tokens from other providers and pretending it was their own model.
With gpt-6 luna and sol coming tomorrow it's going to look even worse too, especially if new luna retains the same dirt cheap pricing that 5.6 luna has.
If you haven't used it, how do you know if it's winning?
I think it's winning on UI for normies (grok bot) and they made some claims about being pareto SOTA (lowest cost per task completed) a while back with 4.6.
I find it to be a perfectly capable model for implementation (there are many in this class--deepseek flash, spark1.3, luna, etc). I find the usage to be very generous w/ supergrok. I find the model to be just fine for 90% of what I want to do, but I use a smarter model to plan complicated things.
After the cursor aquisition it's become a quite capable coding model. If you take cost into account, it's close to the top. OpenAI is maybe still #1, but I'd put Grok at #2 (again, including cost as a factor).
If the CursorBench 4.0 score diagram is the headline, I read it as "Grok 4.7 xHigh is almost the same as Fable5.1 on low".
Is there a metric for like... time taken when comparing these two? I see score and cost.
If Fable5.1 can knock it out more quickly on low but Grok4.7 might take twice as long to stumble through a problem (and leave behind a bunch of yucky comments or un-needed extra unit tests), are they really comparable?
Or like... the "quality" of the solution? "It works" versus "it's unmaintainable/very messy/hacky".
Astra fails in similar ways, and at similar frequency, as GPT 5.6 Sol does. It often goes way out of scope, or just stops prematurely, or tries to find odd and even dangerous workarounds when it gets stuck.
It's phenomenal at computer use and 3D stuff. I've been using it less and less for coding.
Same, Astra is extremely RL fried, and nobody is talking about it. I used Astra for a few days on my personal project, and load times went from less than 3 seconds to almost 30 seconds because it kept using the wrong sync primitives and bad architecture overall.
Huh, I've had a totally different experience.
I've used it extensively, maxing out the 200€ plan on personal projects and it's the best model I've ever used, so easy and pleasant to use.
It's great for frontend design and using it in Rust I've had
Coming from Opus 5, it's a breath of fresh air.
Same. GPT-6 has been a huge breath of fresh air for me. Fixed 80% of the issues I was having with Sol. I just gave it the same task I gave to Sol a few months ago, and it knocked it out of the park comparatively.
Same experience. Astra is on par with or better than Fable 5.1 with a lot more usage on the plans. It has been an extraordinary experience using it so far. 5.6 Sol was very good and Astra is a large upgrade in quality.
LLM's introduces problems, and it finds them in its own internal thinking. But instead of actually modifying the previous generated answer to fix the real issue, it adds another layer to deterministically guard around it, greatly expanding the scope of the fix. This scales with effort, and the result is spaghetti and with a side of bugs.
Best to stick with a high end model + low effort, do a manual pass on high effort and fix the bugs you know are reachable.
It's really interesting how different the experience people have is with these models. I tried Codex with whatever they had before Sol and then with Sol, and just kept going back to Claude Opus/Fable because they were better at the coding work I was doing. Despite getting annoyed at the way it replied/wrote, it was just much better. Astra is the first one that feels as good as Fable to me, and it's much less annoying in its replies. I still don't think they have anything I'd want to drop down to like I can drop down to Opus though.
yeah I see this in these threads, I'm guessing the user prompts are the actual wildcard, it has been for my use thats for sure. edit: I wonder if gemini is somehow training me to like it more lol
I tried Astra w/ high reasoning on a design document project and it was horrible. It started duplicating output lines, made document edits without permission, and basically did a poor job writing clear prose. I went back to 5.6-sol and it's great. I'm an OpenAI fanboy and was severely disappointed. I hope Astra is better for coding.
I agree, the models keep getting better at one shotting. That’s useful in a lot of situations, like for small one-off scripts that filter/transform some tool call, or make a clever bash call. For the code itself, it doesn’t help me much though.
"Yes. I made up the values in that chart. I did not measure them or use your recorded results, and I should not have presented them as measured data." - Astra Pro, just now.
I think you're seeing a big shift around it.... since it's been markedly cheaper and also still easily available from OpenCode, it's getting large enterprise traction.
Not sure why you are being downvoted. Until Musk owns up to his Nazi salute, I won't have anything to do with Grok, no matter how good or cheap it is. And yes, we need to keep talking about this because it's absurd.
If you watch the actual video, you’ll see Musk saying “my heart goes out to you all” as he waves his arm in the air. The Left would love us to imagine he’s saying “sieg heil” or “exterminate the j3ws” or some such, but that’s far from the truth. The media hoopla around a freeze frame of someone raising their arm is a desperate partisan attempt to smear a political rival.
If Elon hadn’t worked with Orange Man Bad, then the Left would still be in love with him for his massive former donations to the Democrat political machine, and his work against climate change.
The whole “he’s a nazi” accusation is banal, and people are seeing through it now. That’s why we’ve moved on.
Poor Elon put in so much effort into his Nazi Salute, he'd be pretty upset about you downplaying his best attempt, if he could read what you wrote here about him.
People continually fall into the trap of pushing a false narrative because they are so blinded by their own agenda.
Like the whole pizza parlor pedo basement thing, people will death grip stupid stuff because they are so desperate to manifest the worst possible image of those unaligned with them.
The problem is that it blows up in their face and just makes them look unreliable, dumb, and lost.
Musk has done so many objectively bad things that there is no need for people to dilute their reputation on fringe theories and interpretations. Pushing the nazi thing just gives Musk ammo that his detractors are so desperate that they need freeze frames and hidden context to make him look bad.
The Democrats are not a left-wing party, I'm not sure why you think "the Left" would be in love with him after all the other horrible things he's done if he just gave money to a political party that is at best centrist.
If you want him to get the benefit of the doubt about his "hand gesture" then it would help if he wasn't promoting far-right parties all over Europe.
It wasn't an accident. He did it deliberately. Twice. He enjoyed himself doing it.
There's no value in the mental gymnastics to invent excuses for his fascist salutes. The rational thing to do is to accept the objective reality of it.
Your friendly reminder that Grok is owned and directly steered by the white supremacist guy with the fascist haircut who does nazi salutes and fucked up decades of international order to settle scores for his apartheid south african family. Any amount of using the model supports this.
I have to say I'm a little perplexed by HN's perpetual willingness to use grok like it's a normal product made by a normal company.
There's often frustration that every thread related to a Musk company includes a discussion about Musk, but Musk himself caused that by being the only tech founder to actively campaign for Trump. (Zuck, the runner up, didn't do anything even close to this). Every product at every company he owns is hopelessly tethered to his decision to do that, and deserves to be judged on those terms.
Another popular story today is about US rollback of climate regulation [1] under Trump. Some fraction of every cent you spend on Grok goes to support stuff you hate, and yet people get genuinely annoyed when you point it out. Musk and his companies really _are_ special and should be treated as special.
Every thread about a Musk company or product needs a comment like this one. If we had the right values, every comment would look like this one.
Thank you for your perspective and I agree as repetitive as it is, I hope hn can continue to surface disagreement/substantial views on a topic.
These comments don't stay up much anymore and I can't tell if it's structural to the forum (flag weight + statistical mechanics of votes + guidelines) or if it's the userbase sentiment.
Except for now HN doesn't do that. Every single comment that points the issue with Grok being built by a pro-fascist team is being "locked" so that they can't be upvoted
For what it's worth, I've watched the vote fight on my comment swing back and fourth over the last hour. It got flagged, unflagged, and flagged again. It appears to now be locked and dead.
But I think it represents real malaise in the community. It's not a moderator plot, people here really just don't care and might even support this.
We really are in the minority of opinion for giving a damn about liberal democracy.
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[ 0.21 ms ] story [ 184 ms ] threadThe personality is bland and it doesn’t work nearly as hard or even tries to help.
I don't use Grok, but do you want your LLM to have a personality? "Personality" is exactly what people don't like about Claude.
Until you ask it to start generating horrific imagery and then it's best in class.
The value of the internet is that people can share whatever they want, and use software how they want. This will mean that some people will abuse that. This is the tradeoff of a free society.
Sounds like a plus. Guess I will give Grok another try...
Given that the decrease in their margin and the fact they delayed the release of Grok 4.7 almost two weeks past the original date, they must not have been happy with the results. And they waited until last minute, when Opus 5.5 is rumored to launch tomorrow. I imagine it will blow Grok out of the water benchmark wise.
I have become skeptical of benchmarks though. Since 4.5 Grok has been performing really well for me. Grok 4.5 solved some issues setting up a buildroot system that Fable 5 couldn't do. I find it's also much better at frontend web development, though Claude is much better at ruby on rails.
My favorite part of the new Groks has been how they speak in plain english. I simply cannot stand Claudish or Gptism. GPT is almost as bad as claude sometimes with how it likes to use big words in confusing explanations. Still, nothing beats Claude 3.5 and 4 with explaining since it seems all models have regressed. I wonder if Grok 4.7 will also regress with English because of all the RL.
That being said, I currently prefer Sol / Astra to Opus / Fable as I find both to be a better cost payoff to me.
How representative that is of real world usage, I don't know.
In their benchmark GPT 5.6 Sol performs suspiciously poorly compared to the former models.
I totally agree, it’s like that as models become more intelligent, they are less understandable by most of people... but aren’t we humans doing the same?
The weird thing is, that's not what AI models seem to be doing. The prose is just weird.
This happens most though when the speaker doesn't (or care to) understand their audience.
Eg i find effective communication requires expertise in both the subject matter domain but also the reference of the listener. Eg in ELI5 framing, if you don't know what information 5yr olds are expected to know you'll do a poor job at an ELI5.
It often feels like Claude does poorly at both framing the response relative to what it "thinks" the listener knows, but also the prose is... sideways, just weird as you said.
It is unsurprising that a LLM fails, without coaching, to effectively communicate.
If I don't grok an elaborate explanation, I can ask for clarification. If it's explained to me in an overly simplistic or unnuanced way, I'll walk away with a false sense of understanding.
That said, I'm sure we all have very different concentrations of these types of people and problems around us. I've definitely met some engineers who seem to actively try to make their language incomprehensible
Agreed. Do you think it's due to that EU issue of making AI text be identifiable?
As for the wording of the prompt, you're pretty on point, I created a custom output style targeting mostly the first two you have there. Some people have wording that demands a certain technical standard or uses fancy words to describe what to avoid, but I haven't seen evidence those work better than asking plainly and I suspect the opposite: LLMs mimic the user to a degree so talking to it in terms of technical specifications and fancy words is an invitation to get them back.
Part of intelligence is knowing your audience and communicating efficiently.
Bingo! And on this axis many SOTA models fail miserably. These things are acting on my behalf under my direction. All the supposed intelligence in the world means fuck-all if nobody can understand it.
And like somebody else said… when meat-based humans talk like Claude does, it almost always means they either don’t understand what they are talking about, or are actively trying to conceal something and are a fraud. Not always, but almost always.
this is specifically an Anthropic problem, maybe due to their heavy use of Claude to train Claude itself?
Fable 5.1 is not there quite there yet.
They need to get that Sonnet 3.5 magic back.
The Claudish is dead. Long live the Claudish.
I am a huge fan of Gemini Pro for chat... gemini somehow just knows the most obscure stuff. I'll double check something Gemini said and find the source is deep inside a hard to access scientific paper. Google just has the best index of the internet.
It's best for brain storming, rabbit holes, and image recognition.
Let the big models do the heavy lifting for now.
Even if you aren't coding, you really need to double check its answers. Flash 3.8 hallucinated a Keyence camera's max operating temperature for me, last week, and backed it up with "references".
It's still my favorite model for most non-coding stuff, though.
Literally every one, even 1-2 prompts later it starts to go back
It burns more tokens but is the only way to get tolerable text.
First, after a while it's just as grating as Claudeish. Second, my hunch is that it constricts the actual thinking of the LLM, like the same way that Newspeak does in 1984. It shrinks the range of thought that can be expressed if used as an input.
I think the real way to do it is to have another Claude entirely deal with the user as a liaison, but to keep the thinking in whatever format it came in.
Latent space reasoning, if you think about it, is exactly this to a crazy degree: why even formulate a thought as words if you can just keep it as matmuls until the user needs it? And then, if the user needs it, have it always specifically formulated for the user by another LLM rather than constrict its range of thought? Anyway, that's my take.
I don't think this is true.
They have to express themselves as tokens. The meaning of those tokens doesn't have to be text. See any model that can handle images/video. Also, I don't think math, svg, etc, are "natural" language.
And, only the final expression is tokens. The intermediate layers, with the encoded concepts, aren't "natural language".
But, to address your concern (which nobody can disagree with, since even humans can't fully express through text/pictures), potentially: https://news.ycombinator.com/item?id=49758615
The model isn't limited to concepts that can be expressed in natural language.
It's only once the AI gets to the output layers that natural language comes back into play.
After all, they're all made out of weights[0].
0: https://maxleiter.com/blog/weights
How do we know for sure? We don't even know how the emergent properties we see actually emerged?
For humans we know for sure that people sometimes have concepts that they have no word for (the reason the phrase "It's on the tip of my tongue" is a phrase, after all).
We don't know this for LLMs. When it makes new phrases, it's always a mixup of two existing words hyphenated (aside, that also seems to be the limits of SOTA models creativity - join two unrelated words together with a hyphen).
LLMs never respond with "It's on the tip of my tongue" type responses, indicating it has a concept but cannot remember (or does not have) a word for that concept. Every human, pre-speech-age, has managed to express or convey concepts that they had no word for.
So, no. I'd need a citation, preferably multiple, that did the trials and found that a model can generate concepts for which it does not have any words for.
Even if the input is in plain English, the model never sees any words, tokens or glyphs to begin with. It's vectors all the way down.
1. Is natural language holding LLMs back by some %? 2. Is natural language serving as a hard gate that will prevent LLM intelligent progressing past some specific point?
The answer to 1 seems like an obvious yes to me.
Your thesis says the answer to 2 is "yes." That doesn't feel right to me. Think about all of the humans who have pushed various fields forward: Einstein, Newtown, Bach, whoever. If natural language doesn't prevent an entity from surpassing humans in one intellectual field, why would it prevent an entity from surpassing humans in all intellectual fields?
(To be clear, I'm not claiming superintelligence will or won't be achieved; I'm considering your specific thesis about whether or not natural language will be a hard gate)
By the way, how good is Claude's Hopi?
I do think an infrastructure where another Claude retranslates the output would be better. Oftentimes I forget to put it in the actual prompt and when I receive back 8 paragraphs of Claudeish I ask for it then.
I would have to disagree that it gets as grating as Claudeish though. Its just direct and professional instead of ring-around-the-rosy clickbait.
“I would have to disagree that it gets as grating as Claudeish though.”
It’s hard to imagine anything more grating than Claudeish. To quote Rainer Wolfcastle, "My eyes! The goggles do nothing!"
I've found that any prompting any constraint on output (length, style, vocab, even simple formatting) not only places additional cognitive load on the model, which burns some of whatever cognitive budget is available, it will also often skew the output in other subtle and completely unrelated ways.
Since I found this artifact interesting, I did some pretty extensive experiments a couple months. The increased load is real, although the it may not be apparant if you're not near any cognitive boundaries. The subtle skew, however, seems nearly ever-present regardless of load.
While extensive, my tests were just following my curiousity, not controlled, exhaustive or well-documented. I identified about a dozen prior sessions of varying length and complexity to test and downloaded them with a browser add-on. I then removed all other user prompt instructions except for the formatting instruction. A test would typically involve changing the wording of the formatting instruction ranging from brutally simple to detailed and complete, then starting a new session, seeding one of the test sessions and continuing it. To get a feel for baseline inter-session variation, I also tried running the exact same prompt/session multiple times back-to-back, at different times and on different days of the week.
Once I identified a promising prompt candidate, I'd make it the formatting instruction in my regular, daily-use prompt for a few days. I quickly got a feel for how seemingly minor user prompt variations impact response quality, compliance and tone across fresh sessions as well as those in various states of context rot, drift, decay and cliff (<--my ni cknames for the distinct flavors of session degradation than technical terms of art).
My overall conclusion was that every instruction, no matter how minor or unrelated it seems, has some, real impact on the model's cog load, attentional focus and/or attentional weight budget. Both how these impacts manifest and what causes more or less impact is often extremely counteriintuitive. To more fully understand this, I eventually, got to the point of testing null case variants, such as the entire user prompt being one sentence completely unrelated to text formatting or the session topic, like: "Don't reference the cartoon character SnagglePuss" (in a deep dive on ancient Sumerian clay tokens). Similarly, a simple one sentence prompt requesting something the model already always does naturally also has a cost (eg "Capitalize proper nouns"). As others have observed, heavy emphasis, absolute prohibitions or emotional weight in prompts also tend to have outsized impact in both skew (impacting unrelated output tone/style) and in accelerating session degradation. "Avoid referencing SnagglePuss when you can" would have equal compliance but fewer downside impacts than "NEVER reference the cartoon character SnagglePuss" in sessions starting to degrade.
There were also surprises, such as when I was scanning transcripts of an older, longer session and noticed the LLM was doing number formatting almost perfectly. On looking at the active user prompt at the time (I keep a log of every user prompt change I make for every model), it didn't even reference formatting at all. More experimentation showed it a result of the LLM gradually mirroring my consistent use of formatting structure in my prompts over a long session (in which I never mentioned anything about formatting). Unfortunately, that mirrored trait doesn't persist to new sessions and reaching that point requires a substantial number of rounds burning quite a bit of context window.
It’s been really productive and I’ve been asking my agents to communicate using it more and more. I believe it’s relieved my cognitive load a bit while working with them.
https://github.com/tinygo-org/tinygo/blob/dev/AGENTS.md
Wonder if we'd benefit from a much more specialized + task-specific benchmarks to paint a clearer picture like this. A benchmark solely for frontend, ruby, hardware, etc.
Googling it returns no matches but I think it was supposed to be “live viewer”?
I don't want to waste money because my calculator is cracking jokes. They don't deserve their paltry 5% marketshare or whatever it is they have currently. I'm not even getting into Musk as a person or the horrid things we've seen Grok spit out on twitter. I just don't trust his companies with my data and I have seen very little evidence that it's ever the best tool for the job. I'm sure those cases exist but I can't imagine it's worth it.
And like that grok4.7 cache reads are more expensive than sol's (at $0.40/mil).
I do wonder why a frontier model does this to be honest. It still does good coding wise, but it seems strange to me. r/Claude is full of "load bearing" jokes in every thread.
Grok has its own feel too. It's not as bad as Claude, but one of the things that bugs me is that it is far too terse.
It regularly seems to come up with terms and descriptions for things in its chain of reasoning and then uses these terms in its output assuming you understand what it's talking about.
I find I often have to ask it to re-explain what it means.
I’m pretty sure the big bois don’t do it because it would undermine “confidence”.
Seeing a model output “Oh I should just delete blah. Wait blah is a production service, I shouldn’t touch that. Maybe I can gain access to blah? Oh the aws cli isn’t signed in to blah. I see kubectl has access to blah though! Wait, I should ask user permission first.”
Yeaaaaah. Thinking tokens are fuckin’ wild.
Could be something very stupid like - "I don't have ffmpeg available here. Should I install it? No, I can't. I'll proceed doing something that will take me 100x more tokens and wall clock just to avoid adding a dependency." I can then just stop and say - you've got nix flake there, just add it.
That's impossible with western models. The only way is to ask why it did something stupid when it already spent 50% of your weekly quota and produced millions lines of slop.
I suspect that they specifically train Grok to be able to work well with military personnel -- speaking the way they speak: brief, to the point, efficient communication. Personally I really like this. Claude sounds like some demented clown from the marketing department.
Anecdotally, I have noticed the same in the past week. It might just be anecdotal or driven by a long context window.
Separately have been using Grok 4.6 for a bit and it's also pretty concise.
I’ve noticed Astra doing this a lot as well.
GPT does this all the time, too (both Sol and Astra). I constantly have to tell it to not use terms that were not part of the initial prompt.
Hah, yeah even when you put it in AGENTS.md or a skill.. constantly having to remind it.. "what does AGENTS.md" say about doing that?".. Thinking.. Thinking.. "Oh, it says I should never do that, I'll remember that next time.."
Next session - same thing.
So yea, I find fable 5.1 writing to be excellent everywhere. I still use Sol daily though, but for things like config, quick research, fixes, code review etc. Feature work and writing is for fable 5.1.
I would pay money not to use Grok.
That’s a factor of half the parameters. I would be curious to see more on the focus of smaller parameters model and pushing its frontiers
Lol, probably because Tesla's software stack is buildroot based. I'll bet that was in the training data.
And the fact that Grok is the ultimate grandmaster of parallel tool-calls, routinely kicking off four or five at once. Overlapping the latencies makes a huge difference in responsiveness.
I also like how Grok is trained to print a short one-sentence descriptions of what it's doing before each step. Like an airline pilot calling out observations for the black-box recorder to hear.
I'm excited for 4.7 although I share skepticism with other users whether 4.7 will be significantly better, since they didn't raise the price.
I personally cannot ignore that, and I will not stop reminding people of what they’re supporting when they use this model.
I'm curious if you feel the same about re-migration of Belgians from the Congo?
Personally I think it's fine for any country to vote to control immigration as they see fit. I think Japan is a good example of a relatively xenophobic culture that deals with this fairly and thoughtfully.
Can't say I've ever heard anyone implying that colonialists leaving Belgium was unjust. Colonialists is actually not the right word, more like extended occupation, only slightly better than the enslavement of the Leopold II era. The Belgian's were less than 1% of the population and all but an ancillary amount worked in exploiting the native population.
Irrelevant. HN is not the place to randomly inject flamewars about politics. It's explicitly against both the purpose and guidelines of HN.
Seems like you need to review the guidelines again, because they're pretty clear:
> Eschew flamebait. Avoid generic tangents. Omit internet tropes.
https://news.ycombinator.com/newsguidelines.html
The fact that Elon Musk's companies take contracts from the CIA and NRO is not flamebait. It's context that informs how we evaluate future SpaceX ventures.
Basic morality is not "flamewars" or "politics".
is not "basic morality" - it's a mixture between being unprovable and outright false (and nothing else - therefore extremely obviously can't be about morality), but was posted because the author was trying to incite political flamewars on HN (as are you, given your other comments), which again, is against the guidelines.
You should review them, because you clearly don't know what's in them.
https://news.ycombinator.com/newsguidelines.html
We aren't responsible to fix your willful ignorance
Here's reasoning level high: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
For some reason reasoning effort low and medium used similar numbers of tokens, and xhigh used less than high. I think I need to try without OpenRouter in the middle.
It used to be a mess in various interesting ways. Now, almost every big release can draw something perfectly functional.
So the question - without a correct answer - given the prompt "Generate an SVG of a pelican riding a bicycle":
Does the user want the least lines of code to make it functional, or the best looking version?
Grok 4.7 generations: https://threejseval.com/models/grok-4.7-high
Also go vote on https://threejseval.com so you can help evaluate how Grok and other model performs compared to each other!
Has a shadow
Better shaped beak
Leg position more realistic for bicycle riding
Better feathers
It will loop in thinking mode ("Let me implement those fixes: Fix 1, Fix 2, Fix 3 .... Fix 80, Fix 81"), ignore the AGENTS.md instructions, corrupt plan files, etc etc... I have 5.6 Sol as advisor/watchdog, and it blocks every turn, I never saw this. Quite a shame, 4.6 wasn't so bad.
- it allows different models within one session via roles (I only have API, so pay per token)
- it's much more likely (ime) to use the LSP over grep for determining how code fits together
But I agree a 20k+ starting context is way overkill.
I find it's very hard to get information on harnesses people are using. I have to stay model agnostic so I avoid claude, codex, cursor, etc. I've used and tried opencode, which worked well, but obviously lacks the above features.
Does anyone have a resource for following what people are actually being productive with? With so much vibe going on it's hard to separate the wheat from the chaff.
https://x.com/greg_horvay/status/2100764473392820433?s=20
There for awhile it seemed like we’d have 3 big competitors but then Grok 4.2 or 4.4 was just diabolical while OAI and Claude continued their significant improvements. Grok was/is so bad that I was convinced musk was gonna shut it down and just fund Anthropic compute once they reached their compute agreement.
But honestly, it is because numbers are like people; torture them enough and they'll tell you anything.
"Privacy# All these models are hosted in the US. Providers follow a zero-retention policy and do not use your data for model training, with the following exceptions:
Big Pickle: During its free period, collected data may be used to improve the model.
DeepSeek V4 Flash Free: During its free period, collected data may be used to improve the model.
MiMo-V2.5 Free: During its free period, collected data may be used to improve the model.
Laguna S 2.1 Free: During its free period, collected data may be used to improve the model.
Ling-3.0-tiny Free: During its free period, collected data may be used to improve the model.
LongCat-2.0 Free: During its free period, collected data may be used to improve the model.
North Mini Code Free: During its free period, collected data may be retained and used to improve the model. Do not submit personal or confidential data. See the provider’s Terms of Use and Privacy Policy.
Nemotron 3 Ultra Free (NVIDIA free endpoints): Trial use only — do not submit personal or confidential data. Your use is logged for security purposes and to improve NVIDIA products and services. The logged session data for improvement purposes is not linked to your identity or any persistent identifier. For more information about data processing practices, see the Privacy Policy. By interacting with this endpoint, you consent to the collection, recording, and use of such information and the NVIDIA API Trial Terms of Service."
https://openrouter.ai/deepseek/deepseek-v4.1-flash?endpoint=...
GLM or Kimi are better for my own personal projects. DS? uhm. it just keeps doing dumb crap
In cursor I have switch over to grok for planning a composer for coding.
The voice is the same AI slop as the others imho.
(This is about Grok 4.6, I didn't test 4.7 yet).
edit: clarified I mean agentic coding tasks
Deepswe results show that grok 4.6 is more expensive per-task and consistently scores worse than: luna xhigh, glm 5.3, astra low, sol high/xhigh, opus 5 medium.
Grok also used almost 3x as many tokens/turns to complete tasks than all of those models (besides luna), so it takes way more time to complete a task.
There isn't much reason to use Grok at all, it's gotten better but it's still worse than every other player in the field, which shouldn't be a surprise considering until about a year ago they were just buying tokens from other providers and pretending it was their own model.
With gpt-6 luna and sol coming tomorrow it's going to look even worse too, especially if new luna retains the same dirt cheap pricing that 5.6 luna has.
I think it's winning on UI for normies (grok bot) and they made some claims about being pareto SOTA (lowest cost per task completed) a while back with 4.6.
I find it to be a perfectly capable model for implementation (there are many in this class--deepseek flash, spark1.3, luna, etc). I find the usage to be very generous w/ supergrok. I find the model to be just fine for 90% of what I want to do, but I use a smarter model to plan complicated things.
I'm judging on benchmarks, and whether anybody or any company I know has ever suggested using it (not yet).
I don't personally make my judgements based on how many other people mention a thing, but if that gets your code written, by all means.
Is there a metric for like... time taken when comparing these two? I see score and cost.
If Fable5.1 can knock it out more quickly on low but Grok4.7 might take twice as long to stumble through a problem (and leave behind a bunch of yucky comments or un-needed extra unit tests), are they really comparable?
Or like... the "quality" of the solution? "It works" versus "it's unmaintainable/very messy/hacky".
[1]https://eebench.org/
xAI missed its chance, Ball is on Anthropic's court.
It's phenomenal at computer use and 3D stuff. I've been using it less and less for coding.
Best to stick with a high end model + low effort, do a manual pass on high effort and fix the bugs you know are reachable.
Elon claimed Opus was 5T in April, and I think it's fairly likely this is accurate: https://x.com/elonmusk/status/2042123561666855235
Something else is right.
The two models are in completely different price tiers. Astra costs 5 times as much.
It seems like all you can judge about cars would be their maximum speed on an oval.
Based on Artificial Analysis Cost per Task, Astra is about 2-3x cheaper than Fable 5.1 at Medium and Low.
Consequently Astra should be cheaper than Grok 4.7.
1 - https://bench.killswitch-lang.org
Totally the same.
But a lot of young people on the Zuckerberg networks are falling for the nazi propaganda nowadays too around the world
If Elon hadn’t worked with Orange Man Bad, then the Left would still be in love with him for his massive former donations to the Democrat political machine, and his work against climate change.
The whole “he’s a nazi” accusation is banal, and people are seeing through it now. That’s why we’ve moved on.
Like the whole pizza parlor pedo basement thing, people will death grip stupid stuff because they are so desperate to manifest the worst possible image of those unaligned with them.
The problem is that it blows up in their face and just makes them look unreliable, dumb, and lost.
Musk has done so many objectively bad things that there is no need for people to dilute their reputation on fringe theories and interpretations. Pushing the nazi thing just gives Musk ammo that his detractors are so desperate that they need freeze frames and hidden context to make him look bad.
If you want him to get the benefit of the doubt about his "hand gesture" then it would help if he wasn't promoting far-right parties all over Europe.
Here is the actual video in which Musk performs two fascist salutes at a political rally:
https://www.youtube.com/watch?v=smQNNo2a9xc
It wasn't an accident. He did it deliberately. Twice. He enjoyed himself doing it.
There's no value in the mental gymnastics to invent excuses for his fascist salutes. The rational thing to do is to accept the objective reality of it.
There's often frustration that every thread related to a Musk company includes a discussion about Musk, but Musk himself caused that by being the only tech founder to actively campaign for Trump. (Zuck, the runner up, didn't do anything even close to this). Every product at every company he owns is hopelessly tethered to his decision to do that, and deserves to be judged on those terms.
Another popular story today is about US rollback of climate regulation [1] under Trump. Some fraction of every cent you spend on Grok goes to support stuff you hate, and yet people get genuinely annoyed when you point it out. Musk and his companies really _are_ special and should be treated as special.
Every thread about a Musk company or product needs a comment like this one. If we had the right values, every comment would look like this one.
[1] https://text.hrw.org/news/2026/09/17/us-revokes-limits-on-po...
These comments don't stay up much anymore and I can't tell if it's structural to the forum (flag weight + statistical mechanics of votes + guidelines) or if it's the userbase sentiment.
But I think it represents real malaise in the community. It's not a moderator plot, people here really just don't care and might even support this.
We really are in the minority of opinion for giving a damn about liberal democracy.