571–580 of 797 posts
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I am on the opposite camp. Open models are starting to perform better. GPT 5.5 keeps on messing things up. On the contrary, pi + glm + DeepSeek… bliss. Fable was a different kind of beast though. Rip.
Every time I use opus these days I go shut up... you are not fable.. Hard to imagine how just three days with it changed how I saw LLM use.
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We rolled out Deepseek V4 Flash to our customers and it was an absolute disaster, unfortunately. It was not able to follow simple commands, always "forgot" to do things, lied consistently about its work, and so on. It was pretty good though on on-off work, like summarizing something or executing simple commands, so we are experimenting now with using it for subagent work with clear instructions and hand off. Deepseek…
I found Flash to be a bit shaky as well until I started using it in xhigh/max thinking effort, then it became my daily driver. It runs quite well on a couple of DGX Sparks. I still wish it was a little better, but there's hope for another model checkpoint (maybe with some of GLM 5.2's goodness distilled into it, that would be nice).
This is true for most of the open weight chinese models, to be fair. They're really built around long reasoning chains.
Also you're making me want a second Spark-alike :') but they're so expensive...
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The tech has raised the floor not the ceiling. Whether the latter happens remains to be seen.
That sounds accurate. Or rather, they raised the perceived floor. IDK if we're seeing better output, but at least the illusion of output is stronger.
Well, I guess it's just a difference of opinion on who's right
I think GPT writes code the best. How well will it write in version 5.6? It gives me chills. Recently, I went head-to-head with GPT on nearly 2,000 lines of code, and GPT's solution was superior and faster. I even referenced multiple codebases on GitHub while trying, but they were incomparable to GPT. So using GPT brings both fear and excitement. The fear comes from realizing that this level of code is now the averag…
I have long felt like "out of the box", I really dislike gpt's coding style. It seems really verbose and likely to write way too much error handling and wordy comments and worse at finding existing functionality to reuse rather than writing everything from scratch. This has been relatively easy to mitigate with prompting, but I still find it annoying. YMMV I guess!
But most of my time is spent on delivery, and the biggest problem with delivery is that if a bug occurs during runtime, the client curses me out. So to me, GPT code feels meticulous.
Open source contributors might be different. Most of them write code after long periods of deliberation. They take their brightest ideas and put them into open source. Those pieces of code are probably the best answers those programmers can give.
But for someone like me, who works primarily on delivery, we mostly plug in proven patterns and focus on getting things done. 'It works' and 'it's beautiful' are different terms, after all. In that sense, I highly value the meticulousness of GPT code — the very thing you called verbose. Because even if it's inefficient, at least it runs, and it catches and wraps around far more of the parts where things break.
Given a month, I could probably write code at GPT's level, at least to some degree. The problem is the difference between one hour and one month. At its core, AI code is still based on training data.
Easily the most interesting part of this announcement is buried in the second to last paragraph: "We're also launching GPT‑5.6 Sol on Cerebras at up to 750 tokens per second in July, bringing frontier intelligence to customers at unprecedented speed. Access will initially be limited to select customers as we expand capacity." 750 tokens/s on a frontier model is going to be extremely interesting. I doubt this new vers…
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AGI should be able to do every job a human can do using a computer at least as well as the average human.
And what is it worse at than an average human today that can be done on a computer?
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Don't appreciate the slander, but I'll respond anyhow. Contrary to your predisposition, we're actually quite peeved that we might be seeing results from 5.6 instead of 5.5, as it's muddying our own internal data. We've run the tasks on this benchmark hundreds of times for our own internal harness. It got magically better yesterday. Last week we were seeing worse performance (sub-80%). I agree that benchmarks don't me…
"We're also launching GPT‑5.6 Sol on Cerebras at up to 750 tokens per second in July, bringing frontier intelligence to customers at unprecedented speed. Access will initially be limited to select customers as we expand capacity." This seems like it would be the largest and first closed-source model Cerebras has offered till date
Codex Spark models already run on Cerebras