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Meta · Open weights

Llama 4 Scout

Added Sep 5, 2025Open weights

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Use in the app

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.

  • Tools
  • Vision
  • Structured output
Input
Text, Images
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.085
Output
$0.46

Reference prices as published by the model provider. API example

Specifications

Context window
328K
Max output
65.5K
Average output (7 days)
79 tokens
Longer than 7% of 465 models

Side by side

Llama 4 Scout compared

Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.

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Llama 4 ScoutMetaThis modelMistral SabaMistralRemove
ProviderMetaMistral
Discovery tierOpen weightsOpen weights
AddedSep 5, 2025Jan 1, 2024
Limits
Context window328KBest32.77K
Max output65.54KBest26.21K
Average output79 tokens63 tokens
Pricing, USD per 1M tokens
Input$0.085Best$0.20
Output$0.46Best$0.60
Performance
Request success (24h)91.6%100.0%Best
Benchmark indexes
Intelligence Index6.56.5
Coding Index8.2—
Math Index14.0—
Capabilities
ToolsYesNo
VisionYesNo
Structured outputYesNo
Reasoning effort——

Public evaluations

Benchmarks

Independent evaluation results, with how each score ranks among the models in the Noumen catalog.

  • Llama 4 Scout
  • Mistral Saba

Intelligence Index

  • Llama 4 Scout6.5
  • Mistral Saba6.5

Llama 4 Scout: Better than 5% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Llama 4 Scout: 8.2
    • Mistral Saba: —

  • Math Index

    Composite of the math evaluations

    • Llama 4 Scout: 14.0
    • Mistral Saba: —

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Llama 4 Scout: 0.2%
    • Mistral Saba: —

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Llama 4 Scout: 0.0%
    • Mistral Saba: —

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Llama 4 Scout: 0.0%
    • Mistral Saba: —

  • AA-LCRv1.1

    Reasoning across long documents

    • Llama 4 Scout: 27.7%
    • Mistral Saba: —

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Llama 4 Scout: 3.8%
    • Mistral Saba: 4.3%Top

  • GPQA Diamond

    Graduate-level science questions

    • Llama 4 Scout: 58.7%Top
    • Mistral Saba: 42.4%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Llama 4 Scout: 75.2%Top
    • Mistral Saba: 61.1%

  • IFBench

    Following precise output instructions

    • Llama 4 Scout: 39.5%
    • Mistral Saba: —

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Llama 4 Scout: 0.0%
    • Mistral Saba: —

  • SciCode

    Scientific computing in Python

    • Llama 4 Scout: 21.3%
    • Mistral Saba: —

  • LiveCodeBench

    Recent competitive programming problems

    • Llama 4 Scout: 29.9%
    • Mistral Saba: —

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Llama 4 Scout: 14.0%
    • Mistral Saba: —

  • AIME

    Competition mathematics

    • Llama 4 Scout: 28.3%Top
    • Mistral Saba: 13.0%

  • MATH-500

    Competition math problems

    • Llama 4 Scout: 84.4%Top
    • Mistral Saba: 67.7%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Llama 4 Scout: 1.5%
    • Mistral Saba: —

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Llama 4 Scout: 15.5%
    • Mistral Saba: —

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Llama 4 Scout: 27.7%
    • Mistral Saba: —

Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis

Live routing data

Performance and reliability

Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.

Prompt cache hits
0.0%
Request success (24h)
91.6%

Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.

Developers

Use it through the API

Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.

Read the quickstart
curl https://www.noumen.xyz/api/v1/chat/completions \
  -H "Authorization: Bearer $NOUMEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meta-llama/llama-4-scout",
    "messages": [{ "role": "user", "content": "Explain this stack trace." }]
  }'
import os
from openai import OpenAI

client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
    model="meta-llama/llama-4-scout",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

Meta

Other Meta models, starting with the same family.