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Measurement sources and verification status (17 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: 1048576

inputPrice: 0.7

outputPrice: 2.1

benchmarkMmlu: 84

benchmarkHumanEval: 82

benchmarkMath: 72

benchmarkGpqa: 45

reasoningScore: 59

codingScore: 82

benchmarkSweBench: 38

aaIntelligenceIndex: 26

aaCostPerTask: 1.41

aaOutputSpeed: 161

aaLatency: 1.42

aaEndToEndTime: 16.92

Mistral: Mistral Large 4

mistralai🔓 Open Source

LLM Stats Scores

59.0

Reasoning

82.0

Coding

🎯

45.0%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 ↗

35.0%

Humanity's Last Exam

38.0%

SWE-bench

Artificial Analysis measurements

Published operational results. Cost per task, speed, and latency are kept separate from provider API prices.

View source ↗

Intelligence

AA Index

Cost / task

$1.41

AA measured

Output speed

161

tokens / second

TTFT

1.42s

AA measured

Response time

16.92s

end to end

Collected 11/11/2026

BenchLM performance measurements

Published output speed and first-token timing, kept separate from provider pricing and other benchmark sources.

View source ↗

Output speed

113

tokens / second

TTFT

19.37s

BenchLM measured

Collected 11/11/2026

Quality Score

71.0

Overall (0–100)

Speed

80 c/s

characters/second

Latency (TTFT)

0.50s

to first token

Input Price

$0.7

per 1M tokens

Output Price

$2.1

per 1M tokens

Context Window

tokens

Release Date

2026-10-06

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy

What do these benchmarks test?

🎯

GPQA Diamond45.0%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%.