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.
Added Sep 5, 2025
Model weightsContext Window
1.0M
Max Output
65.5K
Avg output tokens (7d)
138 tokens
Input Price (Auto)
$0.15/1M
Output Price (Auto)
$0.60/1M
Cache Read (Auto)
$0.075/1M
Capabilities
Benchmarks
Benchmarks
Performance metrics and benchmarks
Sourced from Artificial Analysis.
Intelligence Index
9.3
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 2% of models compared
GDPval-AA v2
Economically valuable tasks (Elo)
-45 Elo
Better than 4% of models compared
Document reasoning
GDP.pdf
Professional PDF reasoning: all-pass rate
0.8%
Better than 6% of models compared
AA-LCR v1.1
Long context reasoning with updated grading
50.0%
Better than 47% of models compared
Reasoning
HLE
Humanity's Last Exam
4.9%
Better than 28% 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 17% 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 47% of models compared
GDPval-AA (unversioned / legacy)
Economically valuable tasks
0.0%
Last updated Sep 13, 2026
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