Llama 4 Scout, a 17 billion active parameter model with 16 experts, is the best multimodal model in the world in its class and is more powerful than all previous generation Llama models, while fitting in a single H100 GPU. Additionally, Llama 4 Scout offers an industry-leading context window of 10M and delivers better results than Gemma 3, Gemini 2.0 Flash-Lite, and Mistral 3.1 across a broad range of widely reported benchmarks.
Added Sep 5, 2025
Model weightsContext Window
328.0K
Max Output
65.5K
Avg output tokens (7d)
137 tokens
Input Price (Auto)
$0.085/1M
Output Price (Auto)
$0.46/1M
Cache Read (Auto)
$0.043/1M
Capabilities
Benchmarks
Benchmarks
Performance metrics and benchmarks
Sourced from Artificial Analysis.
Intelligence Index
6.5
Coding Index
8.2
Agentic Index
0.5
Agentic work
AutomationBench-AA
Workflow automation with guardrail penalties
0.2%
Better than 2% of models compared
AA-Briefcase
Agentic knowledge work (Elo)
0 Elo
Better than 2% of models compared
GDPval-AA v2
Economically valuable tasks (Elo)
60 Elo
Better than 7% of models compared
Document reasoning
GDP.pdf
Professional PDF reasoning: all-pass rate
0.0%
Better than 1% of models compared
AA-LCR v1.1
Long context reasoning with updated grading
27.7%
Better than 30% of models compared
Reasoning
HLE
Humanity's Last Exam
3.8%
Better than 10% of models compared
IFBench
Instruction-following benchmark
39.5%
Better than 37% of models compared
CritPt
Research-level physics reasoning
0.0%
Coding
Terminal-Bench v4.0
Practical coding and terminal tasks
0.0%
Better than 18% of models compared
SciCode
Python programming for scientific computing
21.3%
Better than 3% of models compared
LiveCodeBench
Contamination-free coding benchmark
29.9%
Better than 35% of models compared
Math
AIME 2025
American Invitational Mathematics Examination 2025
14.0%
Better than 17% of models compared
AIME
American Invitational Mathematics Examination
28.3%
Better than 55% of models compared
Math-500
Diverse mathematical problem solving benchmark
84.4%
Better than 51% of models compared
Knowledge
MMLU-Pro
Professional and academic subject knowledge
75.2%
Better than 50% of models compared
AA-Omniscience Accuracy
Proportion of correctly answered questions
15.2%
AA-Omniscience Hallucination Rate
Rate of incorrect answers among non-correct responses
79.4%
Legacy benchmarks
GPQA Diamond (legacy)
Graduate-level scientific reasoning
58.7%
Better than 33% of models compared
Terminal-Bench Hard (legacy)
Agentic coding and terminal use
1.5%
Better than 16% of models compared
T²-Bench Telecom (legacy)
Conversational AI agents in dual-control scenarios
15.5%
Better than 13% of models compared
AA-LCR (unversioned / legacy)
Long context reasoning evaluation
27.7%
Better than 30% of models compared
GDPval-AA (unversioned / legacy)
Economically valuable tasks
0.0%
Last updated Sep 13, 2026
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