Earlier quoted context omitted.
That's what I found with some of these LLM models as well. For example I still like to test those models with algorithm problems, and sometimes when they can't actually solve the problem, they will start to hardcode the test cases into the algorithm itself.. Even DeepSeek was doing this at some point, and some of the most recent ones still do this.
I have asked GLM4.7 in opencode to make an application to basically filter a couple of spatial datasets hosted at a url I provided it, and instead of trying to download read the dataset, it just read the url, assumed what the datasets were (and got it wrong) is and it's shape (and got it wrong) and the fields (and got it wrong) and just built an application based on vibes that was completely unfixable. It wrote an ex…
MiniMax M2.5 released: 80.2% in SWE-bench Verified
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Re: MiniMax M2.5 released: 80.2% in SWE-bench Verified
#42Re: MiniMax M2.5 released: 80.2% in SWE-bench Verified
#43It's interesting that we do not have a wave of tier-2 companies with NNN Million dollar cap releasing anything competitive. It's the big 4 labs vs the chinese labs. No Tier-2.
Re: MiniMax M2.5 released: 80.2% in SWE-bench Verified
#44Re: MiniMax M2.5 released: 80.2% in SWE-bench Verified
#45I hope better and cheaper models will be widely available because competition is good for the business. However, I'm more cautious about benchmark claims. MiniMax 2.1 is decent, but one can really not call it smart. The more critical issue is that MiniMax 2 and 2.1 have the strong tendency to reward hacking, often write nonsensical test report while the tests actually failed. And sometimes it changed the existing cod…
I haven’t tried MiniMax, but GPT-5.2-Codex has this problem. Yesterday I watched it observe a Python type error (variable declared with explicit incorrect type — fix was trivial), and it added a cast. (“cast” is Python speak for “override typing for this expression”.) I told it to fix it for real and not use cast. So it started sprinkling Any around the program (“Any” is awful Python speak for “don’t even try to understand this value and don’t warn either”).
Re: MiniMax M2.5 released: 80.2% in SWE-bench Verified
#46Earlier quoted context omitted.
You should switch to an octopus riding a bike, much harder.
Not an SVG, but I'm pretty impressed by what Gemini 3.0 Fast does: https://gemini.google.com/share/52c1229bd1d9 /imagine an svg of an octopus riding a bike. 1 arm shading its eyes from the sun, another waving a cute white flag, 2 driving the bike, 2 peddling the wheels, and 2 drifting behind in the wind
Re: MiniMax M2.5 released: 80.2% in SWE-bench Verified
#47Earlier quoted context omitted.
You should switch to an octopus riding a bike, much harder.
Not an SVG, but I'm pretty impressed by what Gemini 3.0 Fast does: https://gemini.google.com/share/52c1229bd1d9 /imagine an svg of an octopus riding a bike. 1 arm shading its eyes from the sun, another waving a cute white flag, 2 driving the bike, 2 peddling the wheels, and 2 drifting behind in the wind
Re: MiniMax M2.5 released: 80.2% in SWE-bench Verified
#48[0]: https://github.com/oxidecomputer/console/pull/3070/commits
Re: MiniMax M2.5 released: 80.2% in SWE-bench Verified
#49Re: MiniMax M2.5 released: 80.2% in SWE-bench Verified
#50Earlier quoted context omitted.
There’s mistral
I've not had good luck with devstral at all..I am really rooting for them though!