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Discussion (73 Comments)Read Original on HackerNews
> so their tok/s is a ceiling, not a true decode rate. The clear read: the GPT-5.6 tiers are the snappiest models here on short prompts (Luna answers in about a second), Qwen is absurdly cheap and fast, and DeepSeek and GLM are the slowpokes
You put in a lot of good work, and kudos for that, but man, reading paragraphs like these just puts me off of the entire piece.
Like…how hard would it have been really to type these two sentences by hand, in your own natural voice?
I asked for hemingway in a planning document one time and the result was highly amusing to everyone. "We will not wreck it with small greeds." was an all time favorite for me.
So no matter how good or thoughtful the writing is it gets tiresome.
LLMs have a bunch of tricks for dressing up their infodumps, but they are almost purely infodumps, and no real opinion comes through. There's no sense of more importance to one statement or the other, it's all monotone (and usually over the top.)
Man generates text.
Their writing was already painted into this corner long before the LLM epoch and they continue to publish more than anyone else.
Please write like a normal human and put the effort in to type what you want to say. Using AI to make your writing is not only lazy, it's bland, tiresome, and disrespectful of the reader's time.
I'm sick and tired of reading comments like these.
On the other hand, do we have to complain about every seemingly AI written text?
I suppose it’s interesting to see how they make better greenfield apps. But I am much more interested in how they solve hard problems in existing gnarly codebases.
Agent: https://arena.ai/leaderboard/agent
Web dev: https://arena.ai/leaderboard/code/webdev
Currently Fable and 5.6 are neck and neck on web dev which is basically the same finding as this.
I work at OpenAI, and am happy to say we don't try to juice our scores here, as doing so would be counterproductive and make Arena a worse signal for everyone.
Always fun to ask them to recreate classic demoscene effects (sadly they're still pretty bad at generating music, though at least claude seems to create decent synths).
I keep trying to get them to recreate the fluid+particle stuff from Agenda Circling Forth etc., but even giving them the blog posts describing the implementation (and screenshots) they're still pretty bad.
Is there any evidence that novel reasoning is present in LLM? I've never been able to make that work, and I believe Apple's paper some time ago was good evidence that it doesn't exist. In my experience, sparse latent spaces result in a complete, comical, failure in reasoning.
I’m not very valiant to verify its veracity. But even if the math is merely derivative it merits mention.
Real world is messy, other benchmarks are clearly gameable by the Chinese open models.
Great job! And I don’t care about the tone of the article, it’s readable just fine.
(edit: seems to be an issue with inline videos)
https://d1md4c6gq9re9p.cloudfront.net/blog/gpt-5.6-buildoff/...
https://d1md4c6gq9re9p.cloudfront.net/blog/gpt-5.6-buildoff/...
https://d1md4c6gq9re9p.cloudfront.net/blog/gpt-5.6-buildoff/...
We made Grok 4.5, GPT-5.5, and Claude build the same apps - https://news.ycombinator.com/item?id=48838772 - July 2026 (92 comments)
https://arena.logic.inc/
It's really interesting to see the Sol/Terra/Luna apps side-by-side.
I need to add these stats somewhere in the UI, but one interesting take away: Terra took 1/2 as much wall-clock time as Sol, but Luna took more wall-clock time than Sol (by about 23%). It's still much much cheaper, but it seems like Terra is likely a more optimal time/cost balance for most use cases.
The Terra quality is usually nearly as good as Sol, but much faster and cheaper. I do appreciate Sol's design sensibilities (see, for example, the audio sequencer). It's the first model in a while that is clearly distinct on that front. They'd all converged to very similar visuals for a while.
The lessons that should have been learned here, surely, include:
1) you probably should not one-shot apps like this unless you're really not that bothered with consistency
2) if you are remaining in control of the code you generate, Qwen 3.7 plus is pretty competitive with Fable.
My questions:
How is "good results when it worked" a 4/5 score?
And how can any of these really be considered indicators of performance for the "genuinely novel" when the results are all so similar?
> Draw a horse riding an astronaut in svg
https://www.svgviewer.dev/s/if4gi3e7
https://www.tryai.dev/models/grok-4.5
Update: kibae above and below is correct and I'm not. They have fixed their blog post.
I want to know how well it can follow instructions, manage various potentially competing desires in the context, and so on. It's much more interesting how it can turn 100k tokens (e.g. a codebase and lots of tool calls) into 100 tokens.
Look at the top comment on their previous HN submission: https://news.ycombinator.com/item?id=48839886
Really, the deeper problem is the upvotes that cause such posts to rise to the top of a thread and stick there, drowning out curious conversation and giving people a bad impression of the entire community. Unfortunately, the upvote problem seems basically unsolvable - people don't do it consciously and it's very much a tragedy-of-the-commons problem. So we're stuck with moderation on the comments.
Given that type of reaction is inevitable, it just saves the conversation.
also the article itself is clearly LLM generated though
My opinion is that two gimmicky "one-shot prompting shootout" marketing pieces in two days smells like desperation. I'm not sure you understand what a turnoff this is for potential customers.
Measuring "intelligence" is hard, but giving an "intelligent" entity tasks, and seeing what comes out, and then comparing the output with others, seems like a very reasonable, relative, way to do it.