reasoning benchmark
RepoBench Leaderboard
RepoBench is a benchmark for evaluating repository-level code auto-completion systems through three interconnected tasks: RepoBench-R (retrieval of relevant code snippets across files), RepoBench-C (code completion with cross-file and in-file context), and RepoBench-P (pipeline combining retrieval and prediction). Supports Python and Java programming languages and addresses the gap in evaluating real-world, multi-file programming scenarios by providing a more complete comparison of performance in auto-completion systems.
Updated Aug 17, 2026
Models1
Model coverage1
MetricScore
EvidenceB
RepoBench Ranking
Higher score ranks better on this benchmark.
Sort by
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelMA | Score34.0% | Percentile100.0% | Participants1 | EvidenceC | Evaluated |
RepoBench Highlights
The leading models and scores on this benchmark.
The Top AI Models for RepoBench
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis repobench AI model leaderboard uses descending score in the benchmark's original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
- 01MACodestral-22BMistral AI
- Score
- 34.0%
Strengths
- Ranks #1 of 1 compared models
- 100th percentile on this benchmark
- C evidence result
Considerations
- This result measures RepoBench, not total model capability
Selection summary
Best AI Models for RepoBench
Codestral-22B currently leads RepoBench with 34.0%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.
What is RepoBench?
What RepoBench measures and how its scores work.
RepoBench is a benchmark for evaluating repository-level code auto-completion systems through three interconnected tasks: RepoBench-R (retrieval of relevant code snippets across files), RepoBench-C (code completion with cross-file and in-file context), and RepoBench-P (pipeline combining retrieval and prediction). Supports Python and Java programming languages and addresses the gap in evaluating real-world, multi-file programming scenarios by providing a more complete comparison of performance in auto-completion systems.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- RepoBench
- Modality
- text
- Primary category
- reasoning
- Score direction
- higher
- LLMBoard eligible
- No
- Evaluation key
- repobench|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about RepoBench.
Which model scores highest on RepoBench?
Codestral-22B is currently ranked first with 34.0%.
What does RepoBench measure?
RepoBench is a benchmark for evaluating repository-level code auto-completion systems through three interconnected tasks: RepoBench-R (retrieval of relevant code snippets across files), RepoBench-C (code completion with cross-file and in-file context), and RepoBench-P (pipeline combining retrieval and prediction). Supports Python and Java programming languages and addresses the gap in evaluating real-world, multi-file programming scenarios by providing a more complete comparison of performance in auto-completion systems.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
1 model results are currently shown.
Does this benchmark affect the overall score?
No. This benchmark is shown for reference but does not contribute to the overall score.