alt Hacker Newsso i did some apples to bananas comparison.
i let 4 different agents review the same dirty code base (200+ files changed, c++, game engine):
- deepseek v4.1 flash via deepseek API: took 20m to find one p1 issue that was relevant and a p2 issue that was irrelevant
- glm 5.3 flash NVFP4 via TP2 DGX Sparks: took 1h 5m to find a p3 documentation issue
- gpt 5.6 sol: took 5m30s for one p1 and one p2 issue. both relevant
- gpt 6.1 sol: took 4m30s for two p1 and one p2 issue. both relevant.
Worth to mention: none of those findings overlapped.
I let deepseek v4.1 flash and gpt 6.1 sol both plan fixes for the summary of the findings. in this particular scenario: gpt 6.1 sol planned to increase a buffer size from 128 to 256 to fit the 191 elements that blew past a test scenario. it included some headroom. deepseek v4.1 flash on the other hand decided to make it exactly 191 to satisfy the test, for example.
The TPS decode on deepseek is impressive. I never had a second / feeling of being left aone and though "damn this thing is working". Despite all my efforts, it took ~4x longer in a review, found less and planned worse than GPT 6.1 Sol. Wall clock time. I don't have token counts for comparison. If I were to grade those models from 1-10 (10 = top tier GG), I'd give deepseek a solid 6.5 and 6.1 sol a 7.5-8.0 maybe.
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you probably tried with DS on max and I don't blame you.
But try it on high instead of max if possible. max is known for being overly verbose to the point of being detrimental compared to high at times.
For example Simmons svg pelican took 11 minutes on max and 1 minute on high. And they are comparable.
In your case, is it a public repository that the llms might have found the issues for online?
I too am thinking about creating some benchmarks that I can understand myself. The LLM 'cheating' by finding known issues through some sort of history or online source is a path you want to protect against. Then again, you DO want them to be able to look online for people having similar issues.
> Worth to mention: none of those findings overlapped.
This is why using multiple model families is important. Don't limit yourself by being a stan for this or that company.
> Worth to mention: none of those findings overlapped.
Yet to be obsolete.
What is p1, p2?
This read like a conversation you were having in your head -- unfiltered.
Flagship gpt models are crazy efficient. 6.1 Sol is like a savant that looks at a problem and spits out the answer seemingly without thinking.
Opposite of my experience - openai models, and codex are clearly the slowest (out of 5 different models/harnesses I use on a regular basis).
LLMs are non-deterministic, you could run the same prompt on the same code base with the same models, and have different result. I don't think only one run with each model is enough to draw conclusions.