Which LLM is best on average?

A single high score is easy to game — train on the test set, hand-tune one case, publish a paper. Average performance across many benchmarks is harder to fake.

We rank every LLM that placed on at least 2 of 8 public benchmarks. Then — where we've verified the model on our own held-out set — we show our number next to the public consensus.

Within each benchmark we rank every model that has a score, then map the rank to a 0–100 percentile (top = 100). This neutralises that some metrics are lower-better while others are higher-better — both end up on the same 0–100 axis.

Power score is the unweighted mean of a model's percentiles across the benchmarks where it has a score. We require a minimum of 2 benchmarks — one strong showing isn't enough.

Where CodeSOTA has run its own eval (currently 0 of 19 ranked models), the right-most column shows that score. When the public consensus and our number disagree, that disagreement is the most useful thing on this page.

The Power Ranking, 19 models.

Sorted by average percentile across the 8 axes. Coverage column is load-bearing — a model on top with 2/8 is making a narrower claim than one on top with most axes.

Pills below each model show per-benchmark percentile. Copper = top quartile (≥75), grey = middle, faded = bottom quartile.

Public benchmarks aren’t enough.

Three problems compound. One: popular benchmarks are easy to overfit — six months after a paper ships, the test set is in the next training run. Two: they miss the cases that actually pay rent — your data, your edge cases, your failure modes. Three: a vendor’s self-reported score is a marketing artefact until somebody else runs the same eval.

Our verified column closes the third gap. The first two we close with a hold-out architecture: methodology and sample items are public, the actual test set rotates quarterly and stays private — so even when our questions eventually leak into a training corpus, they’re no longer the questions we’re using.

Currently 0 of 19 models on this page have a CodeSOTA-verified score. Expanding that coverage is the work.

Want a model verified against your data?

If you're choosing an LLM for production and a model on this list doesn't have a CodeSOTA-verified score, tell us. We run a private, hold-out evaluation on the tasks you actually care about — so you're not picking on a contaminated public number.