Meta · Open weights
Llama 4 Scout
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.
| Llama 4 ScoutMetaThis model | Muse Spark OpenMetaRemove | |
|---|---|---|
| Provider | Meta | Meta |
| Discovery tier | Open weights | Frontier |
| Added | Sep 5, 2025 | Oct 15, 2026 |
| Limits | ||
| Context window | 328K | 1.05MBest |
| Max output | 65.54K | — |
| Average output | 79 tokens | 1.3K tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.085Best | $1.25 |
| Output | $0.46Best | $4.25 |
| Cache read | — | $0.15 |
| Performance | ||
| Request success (24h) | 91.6% | — |
| Benchmark indexes | ||
| Intelligence Index | 6.5 | 38.9Best |
| Coding Index | 8.2 | — |
| Math Index | 14.0 | — |
| Capabilities | ||
| Reasoning | No | Yes |
| Tools | Yes | Yes |
| Vision | Yes | Yes |
| Structured output | Yes | Yes |
| Reasoning effort | — | low, medium, high, xhigh |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- Llama 4 Scout
- Muse Spark Open
Intelligence Index
- Llama 4 Scout6.5
- Muse Spark Open38.9
Llama 4 Scout: Better than 5% of 326 scored models
Indexes
Coding Index
Composite of the coding evaluations
- Llama 4 Scout: 8.2
- Muse Spark Open: —
Math Index
Composite of the math evaluations
- Llama 4 Scout: 14.0
- Muse Spark Open: —
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- Llama 4 Scout: 0.2%
- Muse Spark Open: 49.7%Top
AutomationBench-AA tasks completed
Workflows finished end to end without a guardrail violation
- Llama 4 Scout: 0.0%
- Muse Spark Open: 20.9%Top
AA-Briefcasev1.1
Agentic knowledge work, rated head to head
- Llama 4 Scout: —
- Muse Spark Open: 1,279 Elo
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- Llama 4 Scout: —
- Muse Spark Open: 1,401 Elo
Document reasoning
GDP.pdf
Professional reasoning over PDF documents, all-pass rate
- Llama 4 Scout: 0.0%
- Muse Spark Open: 18.6%Top
AA-LCRv1.1
Reasoning across long documents
- Llama 4 Scout: 27.7%
- Muse Spark Open: 78.3%Top
MLCR-AA
Long-context reasoning over medical records
- Llama 4 Scout: —
- Muse Spark Open: 7.8%
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- Llama 4 Scout: 3.8%
- Muse Spark Open: 43.9%Top
GPQA Diamond
Graduate-level science questions
- Llama 4 Scout: 58.7%
- Muse Spark Open: 89.0%Top
MMLU-Pro
Multi-subject knowledge and reasoning
- Llama 4 Scout: 75.2%
- Muse Spark Open: —
IFBench
Following precise output instructions
- Llama 4 Scout: 39.5%
- Muse Spark Open: —
Coding
Terminal-Benchv4.0
Practical software engineering in a terminal
- Llama 4 Scout: 0.0%
- Muse Spark Open: 8.1%Top
SciCode
Scientific computing in Python
- Llama 4 Scout: 21.3%
- Muse Spark Open: 58.4%Top
LiveCodeBench
Recent competitive programming problems
- Llama 4 Scout: 29.9%
- Muse Spark Open: —
Math
AIME 2025
Competition mathematics, 2025 problems
- Llama 4 Scout: 14.0%
- Muse Spark Open: —
AIME
Competition mathematics
- Llama 4 Scout: 28.3%
- Muse Spark Open: —
MATH-500
Competition math problems
- Llama 4 Scout: 84.4%
- Muse Spark Open: —
Legacy evaluations
Terminal-Bench Hard
Earlier terminal task suite
- Llama 4 Scout: 1.5%
- Muse Spark Open: —
τ²-Bench Telecom
Tool use in a customer-support simulation
- Llama 4 Scout: 15.5%
- Muse Spark Open: —
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- Llama 4 Scout: 27.7%
- Muse Spark Open: 78.3%Top
Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis
- Llama 4 Scout
- Muse Spark Open
| Leaderboard | Llama 4 Scout | Muse Spark Open |
|---|---|---|
| Website | Not in top 20 | #15 · 1306 |
Ranks among Noumen catalog models on Design Arena leaderboards. Only top-20 placements are listed.Design Arena
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.
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
Related models
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