Multimodal Input
Processes both text and image inputs within a single unified model backbone, enabling vision-language tasks like image-grounded Q&A.
Meta
Llama 4 Scout
Llama 4 Scout is a multimodal AI model developed by Meta, released in early 2025 as part of the Llama 4 model family. It uses a Mixture of Experts (MoE) architecture with 17 billion active parameters, 16 experts, and 109 billion total parameters, processing both text and image inputs through a unified model backbone. The model supports a 130,000-token context window and is available under Meta's Llama 4 Community License. Llama 4 Scout is designed for developers and enterprises building applications that require multimodal understanding across text and vision. Its MoE design activates only a subset of parameters per token, making inference more compute-efficient relative to dense models of comparable total parameter count. It is well-suited for tasks such as document analysis, image-grounded question answering, and long-context text generation.
Apr 05, 2025
130,000 context
60,000 tokens output
Multimodal Input
Long Context Window
Mixture of Experts
Text Generation
Instruction Following
Model Overview
High-signal model metadata in a structured two-column overview table.
Provider
The entity that provides this model.
Meta
Model ID
The routed model identifier exposed by upstream providers.
meta-llama/llama-4-scout
Input Context Window
The number of tokens supported by the input context window.
130,000
tokens
Maximum Output Tokens
The number of tokens that can be generated by the model in a single request.
60,000 tokens
tokens
Open Source
Whether the model's code is available for public use.
No
Release Date
When the model was first released.
Apr 05, 2025
1 year ago
Knowledge Cut-off Date
When the model's knowledge was last updated.
2024-08-31
API Providers
The providers that offer this model. This is not an exhaustive list.
DeepInfra, Groq, Novita, Google
Modalities
Types of data this model can process.
Text
Image
What is Llama 4 Scout
A fuller summary of positioning, capabilities, and source-specific details for Llama 4 Scout.
Llama 4 Scout is a multimodal AI model developed by Meta, released in early 2025 as part of the Llama 4 model family. It uses a Mixture of Experts (MoE) architecture with 17 billion active parameters, 16 experts, and 109 billion total parameters, processing both text and image inputs through a unified model backbone. The model supports a 130,000-token context window and is available under Meta's Llama 4 Community License.
Llama 4 Scout is designed for developers and enterprises building applications that require multimodal understanding across text and vision. Its MoE design activates only a subset of parameters per token, making inference more compute-efficient relative to dense models of comparable total parameter count. It is well-suited for tasks such as document analysis, image-grounded question answering, and long-context text generation.
Capabilities
What Llama 4 Scout supports
Long Context Window
Supports up to 130,000 tokens of context, allowing analysis of lengthy documents or multi-turn conversations without truncation.
Mixture of Experts
Uses a 16-expert MoE architecture that activates only 17B of 109B total parameters per token, reducing compute requirements during inference.
Text Generation
Generates coherent, instruction-following text responses across a wide range of tasks including summarization, drafting, and question answering.
Instruction Following
Fine-tuned as an instruct model to follow user and system prompts, making it suitable for chat and task-oriented applications.
Pricing for Llama 4 Scout
Primary API pricing shown in the same “quick compare” spirit as the reference page.
Input tokens
$0.10
Per million tokens
Output tokens
$0.30
Per million tokens
Price Comparison
Additional usage-cost dimensions synced into the project for this model.
maxTemperature
1
maxResponseSize
60,000 tokens
API Access & Providers
Places where this model is available, based on the synced detail-page metadata.
DeepInfra
Groq
Novita
Google
Provider Endpoints
Endpoint-level provider data currently available for this model.
DeepInfra
Groq
Novita
Model Performance
Benchmark scores synced from the current model source and normalized into the local catalog.
| Benchmark | Score |
|---|---|
|
AIME 2024
American math olympiad problems
|
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GPQA Diamond
PhD-level science questions (biology, physics, chemistry)
|
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|
HLE
Questions that challenge frontier models across many domains
|
|
|
LiveCodeBench
Real-world coding tasks from recent competitions
|
|
|
MATH-500
Undergraduate and competition-level math problems
|
|
|
MMLU-Pro
Expert knowledge across 14 academic disciplines
|
|
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SciCode
Scientific research coding and numerical methods
|
Resources & Documentation
Official model cards, release notes, docs, and other references synced from the source page.
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Recent daily stories tied to Llama 4 Scout through direct model mentions or provider-level coverage.
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Community discussion
What people think about Llama 4 Scout
Llama 4 Scout discussions are most active in r/LocalLLaMA, r/AIToolsPerformance, r/learnmachinelearning.
Top Reddit threads cluster around benchmark and model-comparison threads, safety and censorship questions, coding workflow discussions. The strongest match in this snapshot has 2187 upvotes and 193 comments.
I just watched Llamacon this morning and did some quick research while reading comments, and it seems like the vast majority of people aren't happy with the new Llama 4 Scout and Maverick models. Can someone explain why? I've finetuned some 3.1 models before, and I was wondering if it's even worth switching to 4. Any thoughts?
Is Qwen 3.5 a direct replacement to Llama 4 in your opinion? Seems too much of a coincidence
Edit: 3.5 Plus and not Max
*TEXT ONLY* forgot to mention in title :')
Quants seem coherent, conversion seems to match original model's output, things look good thanks to Son over on llama.cpp putting great effort into it for the past 2 days :) Super appreciate his work!
Static quants of Q8_0, Q6_K, Q4_K_M, and Q3_K_L are up on the lmstudio-community page:
https://huggingface.co/lmstudio-community/Llama-4-Scout-17B-16E-Instruct-GGUF
(If you want to run in LM Studio make sure you update to the latest beta release)
Imatrix (and smaller sizes) are up on my own page:
https://huggingface.co/bartowski/meta-llama_Llama-4-Scout-17B-16E-Instruct-GGUF
One small note, if you've been following along over on the llama.cpp GitHub, you may have seen me working on some updates to DeepSeek here:
https://github.com/ggml-org/llama.cpp/pull/12727
These changes though also affect MoE models in general, and so Scout is similarly affected.. I decided to make these quants WITH my changes, so they should perform better, similar to how Unsloth's DeekSeek releases were better, albeit at the cost of some size.
IQ2_XXS for instance is about 6% bigger with my changes (30.17GB versus 28.6GB), but I'm hoping that the quality difference will be big. I know some may be upset at larger file sizes, but my hope is that even IQ1_M is better than IQ2_XXS was.
Q4_K_M for reference is about 3.4% bigger (65.36 vs 67.55)
I'm running some PPL measurements for Scout (you can see the numbers from DeepSeek for some sizes in the listed PR above, for example IQ2_XXS got 3% bigger but PPL improved by 20%, 5.47 to 4.38) so I'll be reporting those when I have them. Note both lmstudio and my own quants were made with my PR.
In the mean time, enjoy!
Edit for PPL results:
Did not expect such awful PPL results from IQ2_XXS, but maybe that's what it's meant to be for this size model at this level of quant.. But for direct comparison, should still be useful?
Anyways, here's some numbers, will update as I have more:
| quant | size (master) | ppl (master) | size (branch) | ppl (branch) | size increase | PPL improvement |
| ------ | ------------------- | ------------ | --------------- | ------ | ------| ----- |
| Q4_K_M | 65.36GB | 9.1284 +/- 0.07558 | 67.55GB | 9.0446 +/- 0.07472 | 2.19GB (3.4%) | -0.08 (1%) |
| IQ2_XXS | 28.56GB | 12.0353 +/- 0.09845 | 30.17GB | 10.9130 +/- 0.08976 | 1.61GB (6%) | -1.12 9.6% |
| IQ1_M | 24.57GB | 14.1847 +/- 0.11599 | 26.32GB | 12.1686 +/- 0.09829 | 1.75GB (7%) | -2.02 (14.2%) |
As suspected, IQ1_M with my branch shows similar PPL to IQ2_XXS from master with 2GB less size.. Hopefully that means successful experiment..?
Dam Q4_K_M sees basically no improvement. Maybe time to check some KLD since 9 PPL on wiki text seems awful for Q4 on such a large model 🤔
I've seen a lot of negativity surrounding the new Llama-4-Scout, and I wanted to share my experience is completely different. I love especially the natural tone and large context understanding
I'm curious to hear if anyone else is having a positive experience with Llama-4-Scout, or if there are specific use cases where it shines. What are your thoughts?
I am running it on a 64 core Ampere Altra arm system with 128GB ram, no GPU, in llama.cpp with q6_k quant. It averages about 10 tokens a second which is great for personal use. It is answering coding questions and technical questions well. I have run Llama 3.3 70b, Mixtral 8x7b, Qwen 2.5 72b, some of the PHI models. The performance of scout is really good. Anecdotally it seems to be answering things at least as good as Llama 3.3 70b or Qwen 2.5 72b, at higher speeds. People aren't liking the model?
FAQ
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, which allows it to handle long documents and extended conversations in a single pass.
How many parameters does Llama 4 Scout have?
Llama 4 Scout has 109 billion total parameters, but uses a Mixture of Experts architecture that activates only 17 billion parameters per token during inference.
What input types does Llama 4 Scout support?
Llama 4 Scout is a multimodal model that accepts both text and image inputs, processing them through a unified model backbone.
Who developed Llama 4 Scout and when was it released?
Llama 4 Scout was developed by Meta and released in early 2025 as part of the Llama 4 model family.
What is the knowledge cutoff for Llama 4 Scout?
The model's training data has a cutoff in early 2025, based on the training date provided in the model metadata.
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