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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: 168

latency: 0.3

contextWindow: 128000

inputPrice: 0.15

outputPrice: 0.6

benchmarkMmlu: 82

benchmarkHumanEval: 87.2

benchmarkMath: 70.2

benchmarkHellaSwag: 87

benchmarkGpqa: 40.2

benchmarkMmluPro: 63

reasoningScore: 63

codingScore: 87.2

benchmarkSweBench: 26.5

OpenAI: GPT-4o-mini

openai🔒 Proprietary

LLM Stats Scores

63.0

Reasoning

87.2

Coding

🎯

40.2%GPQA Diamond — graduate-level science accuracy

63.0%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 ↗

4.2%

Humanity's Last Exam

26.5%

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

tokens / second

TTFT

3.16s

BenchLM measured

Collected 11/11/2026

Quality Score

70.0

Overall (0–100)

Speed

168 c/s

characters/second

Latency (TTFT)

0.30s

to first token

Input Price

$0.15

per 1M tokens

Output Price

$0.6

per 1M tokens

Context Window

128K

tokens

Release Date

2024-07-18

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy

What do these benchmarks test?

🎯

GPQA Diamond40.2%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-Pro63.0%Below avg

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

🧠

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.

💻

HumanEval87.2%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 Benchmark70.2%Solid

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

🌐

HellaSwag87.0%Solid

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.