Llama 4 Scout
Llama 4 Scout is a 17B active parameter mixture-of-experts language model from Meta with a 130,000 token context window.
Meta
DeepInfraEfficient mixture-of-experts text generation from Meta
Llama 4 Scout is a text generation model developed by Meta, released in April 2025. It uses a mixture-of-experts (MoE) architecture with 17 billion active parameters across 16 experts, identified by its full model name meta-llama/Llama-4-Scout-17B-16E-Instruct. This instruct-tuned variant is designed to follow user instructions and engage in multi-turn conversation. It is served through DeepInfra on MindStudio.
The model supports a context window of 130,000 tokens and a maximum response size of 60,000 tokens, making it suitable for tasks that involve long documents, extended conversations, or detailed generation. As part of Meta's Llama 4 model family, Scout is positioned as an efficient option within the lineup, activating only a subset of its total parameters per token due to its MoE design. It is well-suited for instruction-following tasks, summarization of long content, and general-purpose text generation.
What Llama 4 Scout supports
Long Context Window
Processes up to 130,000 tokens in a single request, enabling analysis of lengthy documents, codebases, or extended conversation histories.
Instruction Following
Fine-tuned on instruction data to respond accurately to user prompts, supporting multi-turn dialogue and task-oriented interactions.
Mixture-of-Experts Architecture
Uses a 16-expert MoE design that activates 17 billion parameters per token rather than the full parameter set, enabling efficient inference at scale.
Extended Response Generation
Supports output up to 60,000 tokens per response, allowing generation of long-form content such as reports, summaries, or detailed explanations.
Text Summarization
Can condense large volumes of text into concise summaries, taking advantage of its 130K token context to handle full documents in one pass.
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Get Started FreeBenchmark scores
Scores represent accuracy — the percentage of questions answered correctly on each test.
| Benchmark | What it tests | Score |
|---|---|---|
| MMLU-Pro | Expert knowledge across 14 academic disciplines | 75.2% |
| GPQA Diamond | PhD-level science questions (biology, physics, chemistry) | 58.7% |
| MATH-500 | Undergraduate and competition-level math problems | 84.4% |
| AIME 2024 | American math olympiad problems | 28.3% |
| LiveCodeBench | Real-world coding tasks from recent competitions | 29.9% |
| HLE | Questions that challenge frontier models across many domains | 4.3% |
| SciCode | Scientific research coding and numerical methods | 17.0% |
Common questions about Llama 4 Scout
What is the context window size for Llama 4 Scout?
Llama 4 Scout supports a context window of 130,000 tokens, allowing it to process long documents or extended conversations in a single request.
What does the '17B-16E' in the model name mean?
It refers to the model's mixture-of-experts architecture: 17 billion active parameters are used per token, distributed across 16 experts. Not all experts are activated for every token, which is characteristic of MoE models.
Is Llama 4 Scout an instruct model or a base model?
The version available on MindStudio is the instruct-tuned variant (Llama-4-Scout-17B-16E-Instruct), which has been fine-tuned to follow user instructions and engage in conversational tasks.
What is the maximum response length Llama 4 Scout can produce?
The model supports a maximum response size of 60,000 tokens per output, making it suitable for generating long-form content.
When was Llama 4 Scout released?
Llama 4 Scout was released in April 2025 by Meta as part of the Llama 4 model family.
Do I need an API key to use Llama 4 Scout on MindStudio?
No. MindStudio provides access to Llama 4 Scout without requiring you to supply your own API key or manage provider credentials directly.
What people think about Llama 4 Scout
Community reception of Llama 4 Scout on Reddit has been mixed, with some users acknowledging its multimodal architecture and efficient MoE design as technically interesting. However, the most upvoted threads reflect significant disappointment, with many users feeling the model did not meet expectations set by Meta's announcements.
Common criticisms include concerns about real-world performance not matching benchmark claims, and frustration with the gap between marketing and practical results. Threads discussing the Hugging Face release received comparatively little engagement, suggesting the broader community response was dominated by negative sentiment at launch.
Meta's Llama 4 Fell Short
I'm incredibly disappointed with Llama-4
meta-llama/Llama-4-Scout-17B-16E · Hugging Face
Parameters & options
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