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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: 0.42

contextWindow: 131072

inputPrice: 0.7

outputPrice: 0.7

benchmarkMmlu: 86

benchmarkHumanEval: 80.5

benchmarkMath: 74

benchmarkGpqa: 60

reasoningScore: 57

codingScore: 80.5

benchmarkSweBench: 34

Nous: Hermes 3 70B Instruct

nousresearch🔒 Proprietary

Published Humanity’s Last Exam leaderboard evidence.

LLM Stats Scores

57.0

Reasoning

80.5

Coding

🎯

60.0%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 ↗

4.0%

Humanity's Last Exam

34.0%

SWE-bench

Quality Score

70.0

Overall (0–100)

Speed

80 c/s

characters/second

Latency (TTFT)

0.42s

to first token

Input Price

$0.7

per 1M tokens

Output Price

$0.7

per 1M tokens

Context Window

131K

tokens

Release Date

2024-08-18

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy

What do these benchmarks test?

🎯

GPQA Diamond60.0%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.

🧠

MMLU86.0%Solid

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

💻

HumanEval80.5%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 Benchmark74.0%Solid

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