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Measurement sources and verification status (10 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: 80

latency: 0.32

contextWindow: 200000

benchmarkMmlu: 82

benchmarkHumanEval: 84.5

benchmarkMath: 71

benchmarkGpqa: 52

reasoningScore: 62

codingScore: 84.5

benchmarkSweBench: 38

Claude 3.5 Haiku

anthropic🔒 Proprietary

Published Humanity’s Last Exam leaderboard evidence.

LLM Stats Scores

62.0

Reasoning

84.5

Coding

🎯

52.0%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 ↗

3.6%

Humanity's Last Exam

38.0%

SWE-bench

Quality Score

72.0

Overall (0–100)

Speed

80 c/s

characters/second

Latency (TTFT)

0.32s

to first token

Input Price

—

per 1M tokens

Output Price

—

per 1M tokens

Context Window

200K

tokens

Release Date

2024-10

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy

What do these benchmarks test?

🎯

GPQA Diamond52.0%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.

🧠

MMLU82.0%Solid

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

💻

HumanEval84.5%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 Benchmark71.0%Solid

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