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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: 0.5

contextWindow: 400000

inputPrice: 2.5

outputPrice: 2

benchmarkMmlu: 89.2

benchmarkHumanEval: 88

benchmarkMath: 90

benchmarkGpqa: 40

reasoningScore: 81

codingScore: 88

benchmarkSweBench: 46

OpenAI: GPT-5 Image Mini

openai🔒 Proprietary

LLM Stats Scores

81.0

Reasoning

88.0

Coding

🎯

40.0%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.

46.0%

SWE-bench

Quality Score

85.0

Overall (0–100)

Speed

160 c/s

characters/second

Latency (TTFT)

0.50s

to first token

Input Price

$2.5

per 1M tokens

Output Price

per 1M tokens

Context Window

400K

tokens

Release Date

2025-10-16

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy

What do these benchmarks test?

🎯

GPQA Diamond40.0%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.

🧠

MMLU89.2%Top tier

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

💻

HumanEval88.0%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 Benchmark90.0%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%.