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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: 160

latency: 0.26

contextWindow: 32768

inputPrice: 0.8

outputPrice: 1.6

benchmarkMmlu: 73

benchmarkHumanEval: 72.6

benchmarkMath: 51.9

benchmarkGpqa: 20

reasoningScore: 42

codingScore: 72.6

benchmarkSweBench: 21

AionLabs: Aion-RP 1.0 (8B)

aion-labs🔒 Proprietary

LLM Stats Scores

42.0

Reasoning

72.6

Coding

🎯

20.0%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.

21.0%

SWE-bench

Quality Score

58.0

Overall (0–100)

Speed

160 c/s

characters/second

Latency (TTFT)

0.26s

to first token

Input Price

$0.8

per 1M tokens

Output Price

$1.6

per 1M tokens

Context Window

33K

tokens

Release Date

2025-02-04

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy

What do these benchmarks test?

🎯

GPQA Diamond20.0%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.

🧠

MMLU73.0%Below avg

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

💻

HumanEval72.6%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 Benchmark51.9%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%.