general benchmark
HumanEvalFIM-Average is the average evaluation score for HumanEval Fill-in-the-Middle benchmark variants covering single-line, multi-line, and random-span code infilling.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelMA | Score91.60% | Percentile100.00% | Participants1 | EvidenceC | Evaluated |
The leading models and scores on this benchmark.
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis humanevalfim-average AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
Selection summary
Codestral-22B currently leads HumanEvalFIM-Average with 91.60%. 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 HumanEvalFIM-Average measures and how its scores work.
HumanEvalFIM-Average is an average evaluation of HumanEval Fill-in-the-Middle benchmark variants, including single-line, multi-line, and random-span variants.
It measures code infilling capabilities of language models using the Score metric, reported as a ratio.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
LLM Stats. Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about HumanEvalFIM-Average.
Codestral-22B is currently ranked first with 91.60%.
The current leaders are Codestral-22B (91.60%).
No matched official input price is currently available.
No matched runtime record is currently available.
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures code infilling capabilities of language models using the Score metric, reported as a ratio.
Yes. Higher values rank better for this benchmark.
1 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.