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Measurement sources and verification status (14 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: 96

latency: 0.85

contextWindow: 262144

inputPrice: 0.6

outputPrice: 2.5

benchmarkMmlu: 86

benchmarkHumanEval: 89

benchmarkMath: 88

benchmarkHellaSwag: 88

benchmarkGpqa: 60

benchmarkMmluPro: 62

reasoningScore: 77

codingScore: 82

benchmarkHle: 44.9

MoonshotAI: Kimi K2 Thinking

moonshotai🔒 Proprietary

Kimi K2 Thinking is Moonshot AI’s most advanced open reasoning model to date, extending the K2 series into agentic, long-horizon reasoning. Built on the trillion-parameter Mixture-of-Experts (MoE) architecture introduced in...

LLM Stats Scores

77.0

Reasoning

82.0

Coding

🎯

60.0%GPQA Diamond — graduate-level science accuracy

62.0%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 ↗

44.9%

Humanity's Last Exam

BenchLM performance measurements

Published output speed and first-token timing, kept separate from provider pricing and other benchmark sources.

View source ↗

Output speed

tokens / second

TTFT

1.49s

BenchLM measured

Collected 11/11/2026

Quality Score

80.0

Overall (0–100)

Speed

96 c/s

characters/second

Latency (TTFT)

0.85s

to first token

Input Price

$0.6

per 1M tokens

Output Price

$2.5

per 1M tokens

Context Window

262K

tokens

Release Date

2025-11-06

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.

📚

MMLU-Pro62.0%Below avg

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

🧠

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.

💻

HumanEval89.0%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 Benchmark88.0%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%.

🌐

HellaSwag88.0%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.