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Measurement sources and verification status (19 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: 160

latency: 0.29

contextWindow: 1048576

inputPrice: 0.75

outputPrice: 3.75

benchmarkMmlu: 87.5

benchmarkHumanEval: 87

benchmarkMath: 90

benchmarkGpqa: 71

reasoningScore: 81

codingScore: 87

benchmarkSweBench: 44

benchmarkAutoBench: 30.4

benchmarkTerminalBench: 85.8

aaIntelligenceIndex: 56

aaCostPerTask: 0.16

aaOutputSpeed: 390

aaLatency: 1.02

aaEndToEndTime: 2.68

Google: Gemini 3.7 Flash

google🔒 Proprietary

Published Artificial Analysis leaderboard evidence.

LLM Stats Scores

81.0

Reasoning

87.0

Coding

🎯

71.0%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 ↗

47.9%

Humanity's Last Exam

44.0%

SWE-bench

30.4%

AutoBench

85.8%

Terminal-Bench

Artificial Analysis measurements

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

Intelligence

AA Index

Cost / task

$0.16

AA measured

Output speed

390

tokens / second

TTFT

1.02s

AA measured

Response time

2.68s

end to end

BenchLM performance measurements

Published output speed and first-token timing, kept separate from provider pricing and other benchmark sources.

View source ↗

Output speed

285

tokens / second

TTFT

8.52s

BenchLM measured

Collected 11/11/2026

Quality Score

84.0

Overall (0–100)

Speed

160 c/s

characters/second

Latency (TTFT)

0.29s

to first token

Input Price

$0.75

per 1M tokens

Output Price

$3.75

per 1M tokens

Context Window

tokens

Release Date

2026-08-13

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy

What do these benchmarks test?

🎯

GPQA Diamond71.0%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.

🧠

MMLU87.5%Top tier

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

💻

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 Benchmark90.0%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%.