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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: 0.006

outputPrice: 0.5

benchmarkMmlu: 87.5

benchmarkHumanEval: 89

benchmarkMath: 92

benchmarkGpqa: 69.5

reasoningScore: 81

codingScore: 89

benchmarkSweBench: 48

aaIntelligenceIndex: 45

aaCostPerTask: 0.26

aaOutputSpeed: 1486

aaLatency: 0.89

aaEndToEndTime: 1.49

DeepSeek: DeepSeek V4.1 Flash 1017

deepseek🔓 Open Source

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.

48.0%

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

Cost / task

$0.26

AA measured

Output speed

1486

tokens / second

TTFT

0.89s

AA measured

Response time

1.49s

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

184

tokens / second

TTFT

12.06s

BenchLM measured

Collected 11/11/2026

Quality Score

85.0

Overall (0–100)

Speed

160 c/s

characters/second

Latency (TTFT)

0.38s

to first token

Input Price

$0.006

per 1M tokens

Output Price

$0.5

per 1M tokens

Context Window

tokens

Release Date

2026-10-17

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%.