Llama 4 Maverick

Llama 4 Maverick, a 17 billion active parameter model with 128 experts, is the best multimodal model in its class, beating GPT-4o and Gemini 2.0 Flash across a broad range of widely reported benchmarks, while achieving comparable results to the new DeepSeek v3 on reasoning and coding—at less than half the active parameters. Llama 4 Maverick offers a best-in-class performance to cost ratio with an experimental chat version scoring ELO of 1417 on LMArena.

  • Vision
  • Structured Output

Added Sep 5, 2025

Model weights

Pricing

Auto routing · per 1M tokens
Input
$0.15
Output
$0.60
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Specifications

Context window
1M
Max output
65.5K
Parameters
400B / 17B
Total / active
Avg output (7d)
195 tokens
Longer than 14% of models

Benchmarks

Sourced from Artificial Analysis.

Intelligence Index

10.0

Better than 40% of models compared

Coding Index

16.3

Better than 19% of models compared

Agentic Index

0.6

Better than 4% of models compared

Agentic work

AutomationBench-AA

Workflow automation with guardrail penalties

0.2%

Better than 4% of models compared

AA-Briefcase

Agentic knowledge work (Elo)

0 Elo

Better than 3% of models compared

GDPval-AA v2

Economically valuable tasks (Elo)

-272 Elo

Better than 4% of models compared

Document reasoning

AA-LCR v1.1

Long context reasoning with updated grading

50.0%

Better than 44% of models compared

MLCR-AA

Medical long-context reasoning

2.2%

Better than 10% of models compared

Reasoning

HLE

Humanity's Last Exam

4.9%

Better than 26% of models compared

IFBench

Instruction-following benchmark

43.0%

Better than 46% of models compared

CritPt

Research-level physics reasoning

0.0%

Coding

Terminal-Bench v4.0

Practical coding and terminal tasks

0.0%

Better than 16% of models compared

SciCode

Python programming for scientific computing

31.7%

Better than 11% of models compared

LiveCodeBench

Contamination-free coding benchmark

39.7%

Better than 46% of models compared

Math

AIME 2025

American Invitational Mathematics Examination 2025

19.3%

Better than 22% of models compared

AIME

American Invitational Mathematics Examination

39.0%

Better than 63% of models compared

Math-500

Diverse mathematical problem solving benchmark

88.9%

Better than 61% of models compared

Knowledge

MMLU-Pro

Professional and academic subject knowledge

80.9%

Better than 72% of models compared

AA-Omniscience Accuracy

Proportion of correctly answered questions

24.9%

AA-Omniscience Hallucination Rate

Rate of incorrect answers among non-correct responses

88.9%

Legacy benchmarks

GPQA Diamond (legacy)

Graduate-level scientific reasoning

67.1%

Better than 44% of models compared

Terminal-Bench Hard (legacy)

Agentic coding and terminal use

6.8%

Better than 37% of models compared

T²-Bench Telecom (legacy)

Conversational AI agents in dual-control scenarios

17.8%

Better than 16% of models compared

AA-LCR (unversioned / legacy)

Long context reasoning evaluation

50.0%

Better than 44% of models compared

GDPval-AA (unversioned / legacy)

Economically valuable tasks

0.0%

Last updated Oct 3, 2026

Artificial Analysis

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