The state of the art,
measured honestly.

Codesota is the open registry ML engineers consult before choosing a model — benchmarks linked to code, scores cross-checked against the paper, and original analysis of how the market actually uses these models. A calmer, stricter successor to Papers with Code.

No paywall. No signup. No sponsored leaderboards. Every result carries its source type — reproduced, paper, or vendor-reported — so you can decide how much to believe each number.

Current state of the art.

A transverse slice of the registry — the leading published score on each canonical benchmark, grouped by area. Shaded rows are independently verified by CodeSOTA; unshaded rows cite the paper or vendor.

Results

988

Models tracked

163

Datasets indexed

97

Research areas

17

The frontier climbs.

HumanEval — the oldest public code-generation benchmark — is nearing saturation. The step chart shows each successive SOTA-setting submission in the registry; the current leader is a reasoning-augmented mini model, not a frontier flagship.

Below, small multiples plot the real SOTA envelope across 7 modalities. Every point is a dated benchmark result from the registry; each step up is a submission that beat the running best. X-axis is calendar time.

Sixteen domains. One registry.

Everyone tracks frontier LLM scores. We also track what your pipeline depends on — OCR, ASR, detection, retrieval, embedded inference — with the same standard of evidence.

What we have been writing.

Sections are written like issues: a leaderboard at the top, the methodology below it, then essays that explain what the numbers mean — not just what they are.

Trained something
that beats the table?

Submit a checkpoint or a paper result. We verify open-weight models against the public benchmark, cross-check vendor-reported numbers against the source, and add the row to the registry with its date and code trail.

If Codesota informed your research, please cite the registry. A citation helps other readers find reproduced, dated numbers — and helps us keep independent benchmarks sustainable.

@misc{codesota2026,
  title        = {Codesota: The Open Registry of State-of-the-Art Machine Learning},
  author       = {Wikiel, Kacper},
  year         = {2026},
  url          = {https://codesota.com},
  note         = {Accessed: 2026-04-24}
}

@misc{codesota-registry2026,
  title        = {Codesota Benchmark Registry},
  author       = {Wikiel, Kacper},
  year         = {2026},
  howpublished = {\url{https://codesota.com/data/benchmarks.json}},
  note         = {Open JSON registry of reproduced benchmark results}
}