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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: 160

latency: 0.48

contextWindow: 200000

inputPrice: 1.1

outputPrice: 4.4

benchmarkMmlu: 86.5

benchmarkHumanEval: 88.9

benchmarkMath: 90

benchmarkHellaSwag: 82.4

benchmarkGpqa: 62.9

benchmarkMmluPro: 67.4

reasoningScore: 85

codingScore: 94

benchmarkSweBench: 56

OpenAI: o3 Mini

openai🔒 Proprietary

OpenAI o3-mini is a cost-efficient language model optimized for STEM reasoning tasks, particularly excelling in science, mathematics, and coding. This model supports the `reasoning_effort` parameter, which can be set to...

LLM Stats Scores

85.0

Reasoning

94.0

Coding

🎯

62.9%GPQA Diamond — graduate-level science accuracy

67.4%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 ↗

7.9%

Humanity's Last Exam

56.0%

SWE-bench

BenchLM performance measurements

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

View source ↗

Output speed

160

tokens / second

TTFT

7.12s

BenchLM measured

Collected 10/28/2026

Quality Score

88.0

Overall (0–100)

Speed

160 c/s

characters/second

Latency (TTFT)

0.48s

to first token

Input Price

$1.1

per 1M tokens

Output Price

$4.4

per 1M tokens

Context Window

200K

tokens

Release Date

2025-Q1

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy

What do these benchmarks test?

🎯

GPQA Diamond62.9%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-Pro67.4%Solid

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

🧠

MMLU86.5%Solid

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

💻

HumanEval88.9%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%.

🌐

HellaSwag82.4%Below avg

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.