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Measurement sources and verification status (15 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: 48

latency: 0.85

contextWindow: 131072

inputPrice: 1

outputPrice: 3

benchmarkMmlu: 78

benchmarkHumanEval: 78

benchmarkMath: 74

benchmarkGpqa: 60

reasoningScore: 67

codingScore: 78

aaIntelligenceIndex: 7

aaOutputSpeed: 41

aaLatency: 5.21

aaEndToEndTime: 14.79

Nous: Hermes 4 405B

nousresearch🔒 Proprietary

Hermes 4 is a large-scale reasoning model built on Meta-Llama-3.1-405B and released by Nous Research. It introduces a hybrid reasoning mode, where the model can choose to deliberate internally with...

LLM Stats Scores

67.0

Reasoning

78.0

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 ↗

10.9%

Humanity's Last Exam

Artificial Analysis measurements

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

View source ↗

Intelligence

AA Index

Output speed

tokens / second

TTFT

5.21s

AA measured

Response time

14.79s

end to end

Collected 11/11/2026

Quality Score

73.0

Overall (0–100)

Speed

48 c/s

characters/second

Latency (TTFT)

0.85s

to first token

Input Price

per 1M tokens

Output Price

per 1M tokens

Context Window

131K

tokens

Release Date

2025-08-26

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.

🧠

MMLU78.0%Solid

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

💻

HumanEval78.0%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 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%.