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Measurement sources and verification status (18 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: 0.5

contextWindow: 1310720

inputPrice: 0.1

outputPrice: 0.3

benchmarkMmlu: 84

benchmarkHumanEval: 85

benchmarkMath: 82

benchmarkHellaSwag: 86

benchmarkGpqa: 57.2

benchmarkMmluPro: 62

reasoningScore: 67

codingScore: 78

aaIntelligenceIndex: 6

aaCostPerTask: 0.11

aaOutputSpeed: 98

aaLatency: 1.09

aaEndToEndTime: 5.94

Meta: Llama 4 Scout

meta-llama🔒 Proprietary

Meta's ultra-efficient MoE with 10M context and extreme speed.

LLM Stats Scores

67.0

Reasoning

78.0

Coding

🎯

57.2%GPQA Diamond — graduate-level science accuracy

62.0%MMLU-Pro

Published benchmark evidence

Task scores are reported independently by each publisher; they are not combined into a hidden quality score.

View HLE source ↗

3.8%

Humanity's Last Exam

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

$0.11

AA measured

Output speed

tokens / second

TTFT

1.09s

AA measured

Response time

5.94s

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

0.91s

BenchLM measured

Collected 11/11/2026

Quality Score

73.0

Overall (0–100)

Speed

80 c/s

characters/second

Latency (TTFT)

0.50s

to first token

Input Price

$0.1

per 1M tokens

Output Price

$0.3

per 1M tokens

Context Window

tokens

Release Date

2025-04-05

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy

What do these benchmarks test?

🎯

GPQA Diamond57.2%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.

📚

MMLU-Pro62.0%Below avg

The harder successor to MMLU — more options per question eliminate single-answer guessing. Tests depth of professional knowledge across 14 disciplines.

🧠

MMLU84.0%Solid

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

💻

HumanEval85.0%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 Benchmark82.0%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%.

🌐

HellaSwag86.0%Solid

Tests grounded, everyday common sense rather than memorised facts. Most frontier models now score 90%+, so it best separates small/mid models from large ones.