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Models / Mistral

Mistral / MODEL EVIDENCE

Mistral Large

12 benchmarks · 16 observations · 12 sources · released 2024-02-26

Scores at a glance

12 benchmarks · large dot: Mistral Large; small dots: closest models where reported

GPQA Diamond ↗38.76%
9.2896.4

Mistral Large · GPQA diamond · Best score (across scorers) · % ↗

MATH Level 5 ↗24.46%
3.2998.13

Mistral Large · MATH level 5 · Best score (across scorers) · % ↗

MMLU ↗68.8%
29.587.3

Mistral Large · MMLU · EM · % ↗

DTBench ↗55.9%
41.6598.93

Mistral Large · DTBench · Accuracy · % ↗

τ-Bench ↗30.7%
14.861.2

mistral-large · τ-Bench · Retail · pass^1 · Original tau-bench · function-calling agents ↗

2 more reported results

22.4% · mistral-large · τ-Bench · Airline · pass^1 · Original tau-bench · function-calling agents · $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains ↗

26.6% · mistral-large · τ-Bench · Domain average · pass^1 · Original tau-bench · function-calling agents · $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains ↗

WildBench ↗54.2 points
6.265.3

Mistral-Large · WildBench · WB-Score · reported comparison ↗

BioLP-Bench ↗16%
1636

Mistral Large · BioLP-Bench · rounded reported accuracy · Figure 1 caption · three-run means ↗

Closest models by score

Similar positions on shared benchmark leaderboards. Smaller gaps are closer.

2.6 percentile-point gap

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.

Filter comparisons to this model →

Sources

Every plotted observation links to its evaluation. Reported configurations, benchmark versions and task subsets can differ; similarity describes shared scores, not training lift.

  1. WildBench: Benchmarking LLMs with Challenging Tasks from Real Users in the Wild — published result table ↗

    Lin, Bill Yuchen, Deng, Yuntian, Chandu, Khyathi, Brahman, Faeze, Ravichander, Abhilasha, Pyatkin, Valentina et al. · 2024

    All 9 authors

    Lin, Bill Yuchen, Deng, Yuntian, Chandu, Khyathi, Brahman, Faeze, Ravichander, Abhilasha, Pyatkin, Valentina, Dziri, Nouha, Bras, Ronan Le, Choi, Yejin

  2. MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures — published result table ↗

    Jinjie Ni, Fuzhao Xue, Xiang Yue, Yuntian Deng, Mahir Shah, Kabir Jain et al. · 2024

    All 8 authors

    Jinjie Ni, Fuzhao Xue, Xiang Yue, Yuntian Deng, Mahir Shah, Kabir Jain, Graham Neubig, Yang You

  3. $\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains — published result table ↗

    Yao, Shunyu, Shinn, Noah, Razavi, Pedram, Narasimhan, Karthik · 2024

  4. BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions — published result table ↗

    Terry Yue Zhuo, Minh Chien Vu, Jenny Chim, Han Hu, Wenhao Yu, Ratnadira Widyasari et al. · 2024

    All 33 authors

    Terry Yue Zhuo, Minh Chien Vu, Jenny Chim, Han Hu, Wenhao Yu, Ratnadira Widyasari, Imam Nur Bani Yusuf, Haolan Zhan, Junda He, Indraneil Paul, Simon Brunner, Chen Gong, Thong Hoang, Armel Randy Zebaze, Xiaoheng Hong, Wen-Ding Li, Jean Kaddour, Ming Xu, Zhihan Zhang, Prateek Yadav, Naman Jain, Alex Gu, Zhoujun Cheng, Jiawei Liu, Qian Liu, Zijian Wang, Binyuan Hui, Niklas Muennighoff, David Lo, Daniel Fried, Xiaoning Du, Harm de Vries, Leandro Von Werra

  5. DTBench — Conceptual Reasoning Index ↗

    Conceptual Reasoning Index

  6. Holistic Evaluation of Language Models (HELM) ↗

    Stanford Center for Research on Foundation Models

  7. GPQA Diamond ↗

    Epoch AI

  8. MATH Level 5 ↗

    Epoch AI

  9. OTIS Mock AIME 2024-2025 ↗

    Epoch AI

  10. AI benchmarks & capabilities ↗

    Epoch AI

  11. DistressBench — evaluation methodology and results ↗

    Scale Labs / SEAL · 2026

  12. BioLP-bench: Measuring understanding of biological lab protocols by large language models ↗

    Igor Ivanov · 2024

Names used in source tables

Mistral / Large · Mistral Large · Mistral-Large · mistral-large