language benchmark
MMLU Chat is a chat-format variant of the Massive Multitask Language Understanding benchmark covering 57 academic and professional tasks.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelNV | Score80.58% | 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 mmlu chat 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
Llama 3.1 Nemotron 70B Instruct currently leads MMLU Chat with 80.58%. 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 MMLU Chat measures and how its scores work.
MMLU Chat is a language benchmark that evaluates models using a conversational prompting format across 57 tasks, including elementary mathematics, US history, computer science, and law.
It measures model performance with the Score metric, reported as a ratio, across the included tasks.
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 MMLU Chat.
Llama 3.1 Nemotron 70B Instruct is currently ranked first with 80.58%.
The current leaders are Llama 3.1 Nemotron 70B Instruct (80.58%).
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 model performance with the Score metric, reported as a ratio, across the included tasks.
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.