Very cool! So glad to see people building and sharing evals that are better than SWE bench.

I'm curious - any particular reason you didn't put error bars on the graphs? Seems like it could be helpful when there are only 50 unique problems in the diamond set.

*50 unique problems but 20-40 rubrics per problem (something I had to keep reminding people internally who were unimpressed with the N)

simple answer is our reporting was pass@5. feel like you'd need like 50+ runs to have reasonable confidence intervals, which somehow i dont see other people do, so i also didnt insist on it.

hoping to work with <prominent third party evals shop> to get this on their infra and evaluated along with whatever the industry standard is.

Makes sense, thanks. I suppose error bars are tricky if trying to handle problem-to-problem variance, rubric-to-rubric variance, and run-to-run variance all at once.

What did you do around cross-harness testing? I don't see anything in the blog post about what harnesses were used in evaluation. SOTA benchmarks have consistently shown that frontier model performance is quite sensitive to what tools are exposed (e.g. str_replace vs. apply_patch) as the labs are RLing on their own harnesses. Did you do testing of the models in a standard setup or in their native harnesses?

yes well aware :) numbers shown are on "house" harnesses eg codex with gpt and claude code with opus.

fwiw we have examples of each model doing better on NON-house harnesses too - speaking jsut for myself i think the "the labs are RLing on their own harnesses" narrative is kinda overstated if you think through wanting to have any meaningful api business (often eg the labs will give guidance on what is prefered and the agent labs can easily match tool contract to that, which is to say, the "home turf advantage" isnt as large as you think it is if you try a little bit)

What "non-house" harnesses have you found to work best?

What is the "house" harness for minimax? They haven't released any

How do you measure quality at scale ? Is there another model that determines if it adheres to codebase standard ?

see Beyond Unit Tests and Novel Grading Methods in TFA.

i think something like ~60% llm as judge rubrics and the rest as described. every rubric validated by maintainer. 3000 rubrics

This looks really great, more thoughtful than any benchmark that I've seen until now!

I'm curious if you're only interested in scoring frontier models or you would accept submission from custom harnesses? I am working on multi-model harnesses and would love to test them against your benchmark. Do you plan on releasing the tasks publicly?

> Do you plan on releasing the tasks publicly?

yep

yay! looking forward, and thanks!

I'm a bit disappointed that Opus 4.6 wasn't in this because the tokenizer changed quite a bit from 4.7 onward. I was so annoyed by 4.7 that I've been forcing 4.6 ever since. I've been annoyed by 4.8 a bit too, so I haven't felt the urge to move on.

shared older model numbers here https://www.latent.space/p/ainews-frontiercode-benchmarking

tldr theres been broad progress despite your observed regressions

Meaningless comment filled with buzzwords and marketing numbers.

> total 1000+ hours of real life software maintainer work captured in dataset. ON TOP of that, 40+ hours of real human work to turn that real life work into well validated and structured tasks with rubrics (even more work to turn tasks/prompts from devin-infra-specific to pluggable coding agent)

Heartening. We still haven’t automated making the world worse.

this gives my non tired self a chance to fix the typo:

- “ ON TOP of that, 40+ hours of real human work to turn…”

+ “ ON TOP of that, 40+ hours of real human work PER TASK to turn…”

Does reporting each model at its best performing reasoning effort introduce a best-of-N/multiple-comparisons bias, especially if models have different numbers of effort levels?

to you it may do idk. note that if you scroll past fig 1 you get into a nice data explorer that breaks out pass@5 by reasoning level with token and $ and step cost visualized. i think some other commenters on this hn thread got very worked up about stuff we actually agree on.

internally ive charted everything and am satisfied that theres no meaningful rank bias introduced. weve sliced it every which way. in fact we have not even published the best looking charts for this story to be told, because we have further publishing plans on frontiercode

tldr “trust me bro” this isnt the issue and if anything we couldve done more to increase N as tedsanders below points out

Any chance of also benchmarking a couple of more affordable Chinese models? (specifically Deepseek and Xiaomi's MiMo)

i think <third party evals platform> will help us do that best on their standardized model matrix. for frontiercode’s launch we were focused on.. the frontier models

What qualifies as a frontier model? From my personal "taste tests", I wouldn't have placed Sonnet or Kimi above Deepseek Pro or MiMo, or Gemini 3.1 Flash Lite above Deepseek Flash, but they're listed in the benchmark.

The biggest strength of "would this get merged" is that it is actually a compound property: does it work, does it match convention, is it maintainable. And of course: does the reviewer actually want to take ownership of it. A single score has to average across these disjoint axes, which is probably why you needed so many rubrics per problem.

What is the shape of the failures? When a model loses points, do they cluster on correctness? On convention? I run an autonomous pipeline and I can handle model shortcomings, but this kind of detail tells me what I need to shore up.

where's minimax m3? Can we get a live table for this with as many models as possible? instead of blog post?