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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: 262144

inputPrice: 0.5

outputPrice: 1.5

benchmarkMmlu: 84

benchmarkHumanEval: 82

benchmarkMath: 72

benchmarkGpqa: 42

reasoningScore: 59

codingScore: 82

benchmarkSweBench: 38

Mistral: Mistral Large 3 2512

mistralai🔓 Open Source

LLM Stats Scores

59.0

Reasoning

82.0

Coding

🎯

42.0%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.

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

$0.5

per 1M tokens

Output Price

$1.5

per 1M tokens

Context Window

262K

tokens

Release Date

2025-12-01

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy

What do these benchmarks test?

🎯

GPQA Diamond42.0%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%.