agents benchmark
MLE-Bench Leaderboard
MLE-Bench evaluates AI agents on machine learning engineering tasks by measuring their performance on Kaggle competitions.
Updated Aug 17, 2026
Models2
Model coverage2
MetricScore
EvidenceB
MLE-Bench Ranking
Higher score ranks better on this benchmark.
Sort by
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelGO | Score63.9% | Percentile100.0% | Participants2 | EvidenceC | Evaluated |
| Rank02 | ModelGO | Score39.2% | Percentile0.0% | Participants2 | EvidenceC | Evaluated |
MLE-Bench Highlights
The leading models and scores on this benchmark.
MLE-Bench Score Distribution
A closer view of the leading scores on this benchmark.
The Top AI Models for MLE-Bench
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis mle-bench 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.
- 01GOGemini 3.6 FlashGoogle
- Score
- 63.9%
- Price
- $1.5 input / $7.5 output per 1M tokens
- Speed
- Up to 181 tok/s via Google
Strengths
- Ranks #1 of 2 compared models
- 100th percentile on this benchmark
- C evidence result
Considerations
- This result measures MLE-Bench, not total model capability
- 02GOGemini 3.5 Flash-LiteGoogle
- Score
- 39.2%
- Price
- $0.30 input / $2.5 output per 1M tokens
Strengths
- Ranks #2 of 2 compared models
- 0th percentile on this benchmark
- C evidence result
Considerations
- This result measures MLE-Bench, not total model capability
Selection summary
Best AI Models for MLE-Bench
Gemini 3.6 Flash currently leads MLE-Bench with 63.9%. 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 MLE-Bench?
What MLE-Bench measures and how its scores work.
MLE-Bench evaluates AI agents on machine learning engineering tasks by measuring their performance on Kaggle competitions.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- MLE-Bench
- Modality
- text
- Primary category
- agents
- Score direction
- higher
- LLMBoard eligible
- No
- Evaluation key
- mle-bench|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about MLE-Bench.
Which model scores highest on MLE-Bench?
Gemini 3.6 Flash is currently ranked first with 63.9%.
What does MLE-Bench measure?
MLE-Bench evaluates AI agents on machine learning engineering tasks by measuring their performance on Kaggle competitions.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
2 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.