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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: 0.45

contextWindow: 1050000

inputPrice: 0.1

outputPrice: 0.5

benchmarkMmlu: 96.5

benchmarkHumanEval: 97

benchmarkMath: 99

benchmarkGpqa: 96

reasoningScore: 98

codingScore: 97

benchmarkSweBench: 74

OpenAI: GPT-6 Luna Pro

openai🔒 Proprietary

LLM Stats Scores

98.0

Reasoning

97.0

Coding

🎯

96.0%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.

74.0%

SWE-bench

Quality Score

97.0

Overall (0–100)

Speed

80 c/s

characters/second

Latency (TTFT)

0.45s

to first token

Input Price

$0.1

per 1M tokens

Output Price

$0.5

per 1M tokens

Context Window

tokens

Release Date

2026-09-22

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy

What do these benchmarks test?

🎯

GPQA Diamond96.0%Top tier

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.

🧠

MMLU96.5%Top tier

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

💻

HumanEval97.0%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 Benchmark99.0%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%.