CodeElo ↗385 Elo
Codestral-22B-v0.1 · CodeElo · Overall competition Elo · main comparison ↗
Mistral / MODEL EVIDENCE
Codestral-22B-v0.1
4 benchmarks · 4 observations · 2 sources
Scores at a glance
4 benchmarks · large dot: Codestral-22B-v0.1; small dots: closest models where reported
HumanEval+ ↗73.8%
Codestral-22B-v0.1 · HumanEval+ · Pass@1 · EvalPlus · official result data ↗
MBPP+ ↗61.9%
Codestral-22B-v0.1 · MBPP+ · Pass@1 · EvalPlus · official result data ↗
HumanEval ↗79.9%
Codestral-22B-v0.1 · HumanEval · Pass@1 · EvalPlus · official result data ↗
Closest models by score
Similar positions on shared benchmark leaderboards. Smaller gaps are closer.
3 of 4 shared benchmark comparisons
3 of 4 shared benchmark comparisons
3 of 4 shared benchmark comparisons
How similarity is measured
Mean absolute gap in percentile positions on shared benchmark settings; lower is closer. At least 60% overlap is required, with a minimum of three when available. Independent evaluations are used when there are at least three; otherwise published report comparisons are used. Repeated model observations within one setting are averaged before ranking. Writing-style traits and composite indices are excluded. This describes similarity, not an overall quality ranking.
Sources
Every plotted observation links to its evaluation. Reported configurations, benchmark versions and task subsets can differ; similarity describes shared scores, not training lift.
- CodeElo: Benchmarking Competition-level Code Generation of LLMs with Human-comparable Elo Ratings — published result table ↗
All 17 authors
Shanghaoran Quan, Jiaxi Yang, Bowen Yu, Bo Zheng, Dayiheng Liu, An Yang, Xuancheng Ren, Bofei Gao, Yibo Miao, Yunlong Feng, Zekun Wang, Jian Yang, Zeyu Cui, Yang Fan, Yichang Zhang, Binyuan Hui, Junyang Lin
- Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation — published result table ↗
Names used in source tables
Codestral-22B-v0.1