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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: 80

latency: 0.65

contextWindow: 8191

inputPrice: 30

outputPrice: 60

benchmarkMmlu: 86.4

benchmarkHumanEval: 87

benchmarkMath: 67

benchmarkHellaSwag: 95.3

benchmarkGpqa: 35.7

benchmarkMmluPro: 63.4

reasoningScore: 61

codingScore: 85

benchmarkSweBench: 32

OpenAI: GPT-4

openai🔒 Proprietary

OpenAI's flagship model, GPT-4 is a large-scale multimodal language model capable of solving difficult problems with greater accuracy than previous models due to its broader general knowledge and advanced reasoning...

LLM Stats Scores

61.0

Reasoning

85.0

Coding

🎯

35.7%GPQA Diamond — graduate-level science accuracy

63.4%MMLU-Pro

Published benchmark evidence

Task scores are reported independently by each publisher; they are not combined into a hidden quality score.

32.0%

SWE-bench

Quality Score

73.0

Overall (0–100)

Speed

80 c/s

characters/second

Latency (TTFT)

0.65s

to first token

Input Price

$30

per 1M tokens

Output Price

$60

per 1M tokens

Context Window

tokens

Release Date

2023-Q1

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy

What do these benchmarks test?

🎯

GPQA Diamond35.7%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-Pro63.4%Below avg

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

🧠

MMLU86.4%Solid

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

💻

HumanEval87.0%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 Benchmark67.0%Solid

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

🌐

HellaSwag95.3%Top tier

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.