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Measurement sources and verification status (17 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: 0.38

contextWindow: 1048576

inputPrice: 0.051

outputPrice: 0.567

benchmarkMmlu: 87.5

benchmarkHumanEval: 89

benchmarkMath: 92

benchmarkGpqa: 69.5

reasoningScore: 81

codingScore: 89

benchmarkSweBench: 48

aaIntelligenceIndex: 40

aaCostPerTask: 0.15

aaOutputSpeed: 380

aaLatency: 0.82

aaEndToEndTime: 2.14

DeepSeek: DeepSeek V4.1 Flash

deepseek🔓 Open Source

Published BenchLM leaderboard evidence.

LLM Stats Scores

81.0

Reasoning

89.0

Coding

🎯

69.5%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 ↗

39.2%

Humanity's Last Exam

48.0%

SWE-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

$0.15

AA measured

Output speed

380

tokens / second

TTFT

0.82s

AA measured

Response time

2.14s

end to end

Quality Score

85.0

Overall (0–100)

Speed

160 c/s

characters/second

Latency (TTFT)

0.38s

to first token

Input Price

$0.051

per 1M tokens

Output Price

$0.567

per 1M tokens

Context Window

tokens

Release Date

2026-09-10

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy

What do these benchmarks test?

🎯

GPQA Diamond69.5%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.

💻

HumanEval89.0%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 Benchmark92.0%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%.