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Measurement sources and verification status (12 fields)

Collected third-party figures are not independent tests. A source link and collection date do not establish reproducibility. Exact model version, dataset and test settings may be missing. No independent verification date is supplied.

speed: 80

latency: 0.5

contextWindow: 128000

inputPrice: 2

outputPrice: 6

benchmarkMmlu: 84

benchmarkHumanEval: 82

benchmarkMath: 72

benchmarkGpqa: 38

reasoningScore: 59

codingScore: 82

benchmarkSweBench: 38

Mistral Large

mistralai🔒 Proprietary

This is Mistral AI's flagship model, Mistral Large 2 (version `mistral-large-2407`). It's a proprietary weights-available model and excels at reasoning, code, JSON, chat, and more. Read the launch announcement [here](https://mistral.ai/news/mistral-large-2407/)....

LLM Stats Scores

59.0

Reasoning

82.0

Coding

🎯

38.0%GPQA Diamond — graduate-level science accuracy

Published benchmark evidence

Task scores are reported independently by each publisher; they are not combined into a hidden quality score.

View HLE source ↗

3.5%

Humanity's Last Exam

38.0%

SWE-bench

Quality Score

71.0

Overall (0–100)

Speed

80 c/s

characters/second

Latency (TTFT)

0.50s

to first token

Input Price

per 1M tokens

Output Price

per 1M tokens

Context Window

128K

tokens

Release Date

2024-02-26

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy

What do these benchmarks test?

🎯

GPQA Diamond38.0%Below avg

Designed so that even PhD-level human experts average ~65–70%. A score above 80% signals near-expert scientific reasoning that goes far beyond memorisation.

🧠

MMLU84.0%Solid

The classic academic breadth benchmark. Top frontier models now score above 85%, so it best distinguishes mid-tier from strong generalist models.

💻

HumanEval82.0%Solid

Measures whether a model writes functionally correct code — not just plausible-looking syntax. Strong scores (>85%) indicate reliable day-to-day coding assistance.

∑

MATH Benchmark72.0%Solid

Tests multi-step mathematical reasoning under strict correctness. Even a 60% score requires strong chain-of-thought; top reasoning models now exceed 90%.