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Measurement sources and verification status (16 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: 0.48

contextWindow: 400000

inputPrice: 1.75

outputPrice: 14

benchmarkMmlu: 92.8

benchmarkHumanEval: 94

benchmarkMath: 96.5

benchmarkGpqa: 47

reasoningScore: 89

codingScore: 94

benchmarkSweBench: 62

aaIntelligenceIndex: 33

aaOutputSpeed: 114

aaLatency: 36.83

aaEndToEndTime: 58.41

OpenAI: GPT-5.3-Codex

openai🔒 Proprietary

Published Artificial Analysis leaderboard evidence.

LLM Stats Scores

89.0

Reasoning

94.0

Coding

🎯

47.0%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 ↗

42.5%

Humanity's Last Exam

62.0%

SWE-bench

Artificial Analysis measurements

Published operational results. Cost per task, speed, and latency are kept separate from provider API prices.

View source ↗

Intelligence

AA Index

Output speed

114

tokens / second

TTFT

36.83s

AA measured

Response time

58.41s

end to end

Collected 11/11/2026

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

56.62s

BenchLM measured

Collected 11/11/2026

Quality Score

91.0

Overall (0–100)

Speed

80 c/s

characters/second

Latency (TTFT)

0.48s

to first token

Input Price

$1.75

per 1M tokens

Output Price

$14

per 1M tokens

Context Window

400K

tokens

Release Date

2026-02-24

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy

What do these benchmarks test?

🎯

GPQA Diamond47.0%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.

🧠

MMLU92.8%Top tier

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

💻

HumanEval94.0%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 Benchmark96.5%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%.