Great effort and a bit closer to my private evals than DeepSWE. I greatly appreciate the focus on false negative and positives, along with simply being far more focused on actual, mergeable quality output over plain passing. Could see a lot of others adopt your list of metrics as a basis, they are very well defined and solid coverage of everything one should want out of code provided, not just focused on one or two narrow targets. Will incorporate a lot of these ideas in my own tests and polish some other parts where I somewhat unintentionally already went into a roughly similar direction.
- 3000 rubrics on code quality. First benchmark to measure: "would this code get actually merged?"
- 20+ expert open-source maintainer created tasks on their own repos to capture their opinion & taste.
- 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)
- results in 81% lower false positive rate than SWE-Bench Pro
- High quality bar: many QA stages & each task manually reviewed by Cognition researchers (examples in post)
Opus 4.8 scores 13% on FrontierCode Diamond.
one of my goals was also to datamine interesting stuff even on the easy tasks. for example, if you squint you can see the answer to "WTF Happened in late 2025" with coding models: https://x.com/swyx/status/2064081945567580323
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)
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.
This looks great. Well reasoned, tons of work put into eval, thanks for building it.
It strikes me as kind of wild that good evals can drive tens to hundreds of millions of dollars of compute deployment in the wild — there’s something new and collaborative and competitive about the eval / frontier model race that’s quite interesting..
In this case “shorter actually mergable patches that open source maintainers would accept” feels like a great thing to deliver to the world.
I didn’t deep dive into good and bad patches, but I wonder if swyx or others on the team have predictions on saturation. Both when, and how useful will it be? That is, do you guys think this test is broad enough as written to get better behavior out of models, and if there is saturation on this test, will we see generalized better patch / coding behavior?
thanks - credit to silas, eric, ben, and team for the depth of the evals, and the rest of the research team for doing the transcript reading parties lol
by nature of being based on open source, frontiercode public will saturate very very quickly. frontiercode main will be >80% in less than a year. hopefully diamond will last a bit longer. we can do annual refreshes, thats not my strategy for staying relevant - what i'm more excited to get funding for is private held out version of frontiercode based on repros of real enterprise customer problems. in an ideal agent lab (https://latent.space/p/agent-labs) you meticulously build up this domain understanding and that is essentially why both model labs and serious customers come to you.
Great effort and a bit closer to my private evals than DeepSWE. I greatly appreciate the focus on false negative and positives, along with simply being far more focused on actual, mergeable quality output over plain passing. Could see a lot of others adopt your list of metrics as a basis, they are very well defined and solid coverage of everything one should want out of code provided, not just focused on one or two narrow targets. Will incorporate a lot of these ideas in my own tests and polish some other parts where I somewhat unintentionally already went into a roughly similar direction.
:wave: i was on the team! AMA.
some headlines
- 3000 rubrics on code quality. First benchmark to measure: "would this code get actually merged?"
- 20+ expert open-source maintainer created tasks on their own repos to capture their opinion & taste.
- 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)
- results in 81% lower false positive rate than SWE-Bench Pro
- High quality bar: many QA stages & each task manually reviewed by Cognition researchers (examples in post)
Opus 4.8 scores 13% on FrontierCode Diamond.
one of my goals was also to datamine interesting stuff even on the easy tasks. for example, if you squint you can see the answer to "WTF Happened in late 2025" with coding models: https://x.com/swyx/status/2064081945567580323
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)
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.
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 great. Well reasoned, tons of work put into eval, thanks for building it.
It strikes me as kind of wild that good evals can drive tens to hundreds of millions of dollars of compute deployment in the wild — there’s something new and collaborative and competitive about the eval / frontier model race that’s quite interesting..
In this case “shorter actually mergable patches that open source maintainers would accept” feels like a great thing to deliver to the world.
I didn’t deep dive into good and bad patches, but I wonder if swyx or others on the team have predictions on saturation. Both when, and how useful will it be? That is, do you guys think this test is broad enough as written to get better behavior out of models, and if there is saturation on this test, will we see generalized better patch / coding behavior?
thanks - credit to silas, eric, ben, and team for the depth of the evals, and the rest of the research team for doing the transcript reading parties lol
by nature of being based on open source, frontiercode public will saturate very very quickly. frontiercode main will be >80% in less than a year. hopefully diamond will last a bit longer. we can do annual refreshes, thats not my strategy for staying relevant - what i'm more excited to get funding for is private held out version of frontiercode based on repros of real enterprise customer problems. in an ideal agent lab (https://latent.space/p/agent-labs) you meticulously build up this domain understanding and that is essentially why both model labs and serious customers come to you.
Since no one knows or can agree on what "code quality" is and we can't measure it for human output, I'm dubious about measuring it for LLMs
babe wake up another eval dropped
> Today’s coding benchmarks have established that models can write correct code.
I wouldn't say that.
> But as AI-generated code becomes the dominant path to production
I really hope that's not the case.
How do you define "correct" code?