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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: 80

latency: 0.65

contextWindow: 128000

inputPrice: 10

outputPrice: 30

benchmarkMmlu: 86.5

benchmarkHumanEval: 85

benchmarkMath: 73

benchmarkGpqa: 37.1

reasoningScore: 61

codingScore: 85

benchmarkSweBench: 32

OpenAI: GPT-4 Turbo

openai🔒 Proprietary

The preview GPT-4 model with improved instruction following, JSON mode, reproducible outputs, parallel function calling, and more. Training data: up to Dec 2023. **Note:** heavily rate limited by OpenAI while...

LLM Stats Scores

61.0

Reasoning

85.0

Coding

🎯

37.1%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.

View HLE source ↗

3.1%

Humanity's Last Exam

32.0%

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

tokens / second

TTFT

3.01s

BenchLM measured

Collected 10/28/2026

Quality Score

73.0

Overall (0–100)

Speed

80 c/s

characters/second

Latency (TTFT)

0.65s

to first token

Input Price

$10

per 1M tokens

Output Price

$30

per 1M tokens

Context Window

128K

tokens

Release Date

2024-Q1

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy

What do these benchmarks test?

🎯

GPQA Diamond37.1%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.

🧠

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.

💻

HumanEval85.0%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 Benchmark73.0%Solid

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