Meta: Llama 4 Scout

Meta: Llama 4 Scout

meta-llama · Released Apr 5, 2025 Efficient
42.8
Our Score

Performance Profile

Intelligence3.1Technical1.5Value8Content4.5
Intelligence 3.1/10
Technical 1.5/10
Content 4.5/10
Value 8/10

Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input..

$0.08 / 1M
Input Price
$0.30 / 1M
Output Price
327,680 tokens
Context Window
16,384 tokens
Max Output

Capabilities

Tool Use Function Calling Vision

Architecture

ModalityText + Image → Text
TokenizerLlama4

Performance Indices

Source: Artificial Analysis

13.5 Intelligence Index
6.7 Coding Index
8.5 Agentic Index
14 Math Index

Benchmark Scores

Intelligence

GPQA Diamond 58.7% Graduate-level scientific reasoning
HLE 4.3% Humanity's Last Exam
MMLU Pro 75.2% Multi-task language understanding
MATH 500 84.4% Mathematical problem-solving
AIME 28.3% Competition mathematics
AIME 2025 14% Competition mathematics (2025)
SciCode 17% Scientific computing

Technical

LiveCodeBench 29.9% Live coding evaluation
TerminalBench Hard 1.5% Agentic terminal tasks
τ²-Bench 15.5% Conversational agent benchmark

Content

IFBench 39.5% Instruction following
LCR 25.8% Long-context reasoning

Benchmark data from Artificial Analysis and Hugging Face

How does Meta: Llama 4 Scout stack up?

Compare side-by-side with other efficient models.

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Model Information

OpenRouter ID meta-llama/llama-4-scout
Providermeta-llama
Model FamilyLlama 4
Release Date April 5, 2025
Context Length327,680 tokens
Max Completion16,384 tokens
Status Active

Pricing

Token Type Cost per 1M tokens Cost per 1K tokens
Input $0.08 $0.000080
Output $0.30 $0.000300

Live Performance

Live endpoint metrics — refreshed every 30 minutes.

100%
Avg Uptime
211ms
Best Latency (TTFT)
188 tok/s
Best Throughput
4/4
Active Endpoints
Available via: DeepInfra, Groq, Novita, Google

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