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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: 80

latency: 0.2

contextWindow: 60000

inputPrice: 0.027

outputPrice: 0.201

benchmarkMmlu: 63.4

benchmarkHumanEval: 64

benchmarkMath: 42

benchmarkGpqa: 18

reasoningScore: 35

codingScore: 64

benchmarkSweBench: 15

Meta: Llama 3.2 1B Instruct

meta-llama🔒 Proprietary

LLM Stats Scores

35.0

Reasoning

64.0

Coding

🎯

18.0%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 ↗

5.5%

Humanity's Last Exam

15.0%

SWE-bench

Quality Score

49.0

Overall (0–100)

Speed

80 c/s

characters/second

Latency (TTFT)

0.20s

to first token

Input Price

$0.027

per 1M tokens

Output Price

$0.201

per 1M tokens

Context Window

60K

tokens

Release Date

2024-09-25

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy

What do these benchmarks test?

🎯

GPQA Diamond18.0%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.

🧠

MMLU63.4%Below avg

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

💻

HumanEval64.0%Below avg

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

∑

MATH Benchmark42.0%Below avg

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