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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: 120

latency: 0.9

contextWindow: 16000

inputPrice: 3

outputPrice: 3

benchmarkMmlu: 91.8

benchmarkHumanEval: 94.8

benchmarkMath: 96.4

benchmarkGpqa: 40

reasoningScore: 68

codingScore: 70

benchmarkSweBench: 48.9

Sao10K: Llama 3.1 70B Hanami x1

sao10k🔒 Proprietary

LLM Stats Scores

68.0

Reasoning

70.0

Coding

🎯

40.0%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.

48.9%

SWE-bench

Quality Score

81.0

Overall (0–100)

Speed

120 c/s

characters/second

Latency (TTFT)

0.90s

to first token

Input Price

per 1M tokens

Output Price

per 1M tokens

Context Window

16K

tokens

Release Date

2024-09

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy

What do these benchmarks test?

🎯

GPQA Diamond40.0%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.

🧠

MMLU91.8%Top tier

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

💻

HumanEval94.8%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 Benchmark96.4%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%.