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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: 1047576

inputPrice: 2

outputPrice: 8

benchmarkMmlu: 90.5

benchmarkHumanEval: 92.3

benchmarkMath: 79.9

benchmarkHellaSwag: 95.7

benchmarkGpqa: 42

benchmarkMmluPro: 74

reasoningScore: 61

codingScore: 85

benchmarkSweBench: 32

OpenAI: GPT-4.1

openai🔒 Proprietary

LLM Stats Scores

61.0

Reasoning

85.0

Coding

🎯

42.0%GPQA Diamond — graduate-level science accuracy

74.0%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

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

155

tokens / second

TTFT

0.89s

BenchLM measured

Collected 11/11/2026

Quality Score

73.0

Overall (0–100)

Speed

80 c/s

characters/second

Latency (TTFT)

0.65s

to first token

Input Price

per 1M tokens

Output Price

per 1M tokens

Context Window

tokens

Release Date

2025-Q1

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy

What do these benchmarks test?

🎯

GPQA Diamond42.0%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.

📚

MMLU-Pro74.0%Solid

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

🧠

MMLU90.5%Top tier

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

💻

HumanEval92.3%Top tier

Measures whether a model writes functionally correct code — not just plausible-looking syntax. Strong scores (>85%) indicate reliable day-to-day coding assistance.

∑

MATH Benchmark79.9%Solid

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

🌐

HellaSwag95.7%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.