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 weightsPricing
Auto routing · per 1M tokens- Input
- $0.15
- Output
- $0.60
Specifications
- Context window
- 1M
- Max output
- 65.5K
- Parameters
- 400B / 17B
- Total / active
- Avg output (7d)
- 195 tokens
- Longer than 14% of models
Benchmarks
Benchmarks
Sourced from Artificial Analysis.
Intelligence Index
10.0
Coding Index
16.3
Agentic Index
0.6
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
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