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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: 524288

inputPrice: 0.35

outputPrice: 1

benchmarkMmlu: 87.5

benchmarkHumanEval: 88

benchmarkMath: 88

benchmarkHellaSwag: 89

benchmarkGpqa: 69.8

benchmarkMmluPro: 69.8

reasoningScore: 67

codingScore: 78

aaIntelligenceIndex: 9

aaCostPerTask: 0.04

aaOutputSpeed: 58

aaLatency: 1.6

aaEndToEndTime: 9.69

Meta: Llama 4 Maverick

meta-llama🔒 Proprietary

Meta's MoE flagship with best-in-class coding and 1M context.

LLM Stats Scores

67.0

Reasoning

78.0

Coding

🎯

69.8%GPQA Diamond — graduate-level science accuracy

69.8%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 ↗

4.9%

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

$0.04

AA measured

Output speed

tokens / second

TTFT

1.60s

AA measured

Response time

9.69s

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

0.98s

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

$0.35

per 1M tokens

Output Price

per 1M tokens

Context Window

524K

tokens

Release Date

2025-04-05

30-Day Rank History

📊 Benchmark Scores

Reported scores · % accuracy

What do these benchmarks test?

🎯

GPQA Diamond69.8%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-Pro69.8%Solid

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

🧠

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.

💻

HumanEval88.0%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 Benchmark88.0%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%.

🌐

HellaSwag89.0%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.