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Measurement sources and verification status (18 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: 160

latency: 0.28

contextWindow: 262144

inputPrice: 0.6

outputPrice: 3.6

benchmarkMmlu: 90.5

benchmarkHumanEval: 92

benchmarkMath: 93

benchmarkHellaSwag: 91

benchmarkGpqa: 66

benchmarkMmluPro: 72

reasoningScore: 67

codingScore: 78

aaIntelligenceIndex: 18

aaCostPerTask: 0.47

aaOutputSpeed: 59

aaLatency: 2.53

aaEndToEndTime: 10.68

Qwen: Qwen3.5 397B A17B

qwen🔒 Proprietary

Published Artificial Analysis leaderboard evidence.

LLM Stats Scores

67.0

Reasoning

78.0

Coding

🎯

66.0%GPQA Diamond — graduate-level science accuracy

72.0%MMLU-Pro

Published benchmark evidence

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

View HLE source ↗

29.0%

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

Cost / task

$0.47

AA measured

Output speed

tokens / second

TTFT

2.53s

AA measured

Response time

10.68s

end to end

Collected 11/11/2026

Quality Score

73.0

Overall (0–100)

Speed

160 c/s

characters/second

Latency (TTFT)

0.28s

to first token

Input Price

$0.6

per 1M tokens

Output Price

$3.6

per 1M tokens

Context Window

262K

tokens

Release Date

2026-02-16

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy

What do these benchmarks test?

🎯

GPQA Diamond66.0%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.

📚

MMLU-Pro72.0%Solid

The harder successor to MMLU — more options per question eliminate single-answer guessing. Tests depth of professional knowledge across 14 disciplines.

🧠

MMLU90.5%Top tier

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

💻

HumanEval92.0%Top tier

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

∑

MATH Benchmark93.0%Top tier

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

🌐

HellaSwag91.0%Solid

Tests grounded, everyday common sense rather than memorised facts. Most frontier models now score 90%+, so it best separates small/mid models from large ones.