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Measurement sources and verification status (9 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: 179
latency: 0.5
contextWindow: 131072
benchmarkMmlu: 78
benchmarkHumanEval: 78
benchmarkMath: 74
benchmarkGpqa: 60
reasoningScore: 67
codingScore: 78

Kimi K2.6Moonshot AI

Moonshot AI🔓 Open Source
6.7
LLM Stats Scores
67.0
Reasoning
78.0
Coding
🎯
60.0%GPQA Diamond — graduate-level science accuracy
Quality Score
73.0
Overall (0–100)
Speed
179 c/s
characters/second
Latency (TTFT)
0.50s
to first token
Input Price
—
per 1M tokens
Output Price
—
per 1M tokens
Context Window
131K
tokens
Release Date
2024-09

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy
0%25%50%75%100%GPQA ◆MMLUHumanEvalMATH
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%.

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AI Score

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5.0/10

"Open‑source but limited benchmark data and modest quality keep it average"

🤖 AI Analysis

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