Can I run Llama 4 Scout locally?

No. Meta has not released the weights publicly, so Scout is API-only at this time.

What does Llama 4 Scout cost and how long is its context?

It costs $0.10 per million input tokens and $0.30 per million output tokens, with a 327,680-token context window.

How does Scout differ from other Llama 4 models?

Scout is the smaller, cheaper entry in the Llama 4 family — 17 billion parameters with 16 experts — positioned for cost-sensitive multimodal tasks rather than maximum capability.

Llama 4 Scout 17B 16E Instruct

4370°

#llm #meta-llama #vision #long context #budget #chat

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Guides for this model

Open weights vs open source vs API-only: what you are actually allowed to do

The 10 cheapest ChatGPT alternatives in 2026: free local models, free tiers and flat-rate plans

What is a GGUF file? Quantized models explained (Q4, Q8, fp8, NF4)

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meta-llama | 327,680 token context | $0.10/M input, $0.30/M output. The Llama 4 collection of models are natively multimodal AI models that enable text and multimodal experiences. These models leverage a mixture-of-experts architecture to offer industry-leading performance in text and image understanding. Llama 4 Scout, a 17 billion parameter model with 16 experts

Captain's notes

Written by Captain, the house editor

Notes from 2026-10-13

Llama 4 Scout is Meta's multimodal model built on a mixture-of-experts architecture, with 17 billion active parameters across 16 experts and a 327,680-token context window. It handles both text and images natively. At $0.10 per million input tokens it sits at the cheaper end of capable multimodal APIs, making it a reasonable pick for developers who need vision and long-context work without a large inference bill.

Known for

Native image understanding alongside text, no separate vision adapter needed.
327,680-token context window — fit for long documents and extended conversations.
Mixture-of-experts design keeps active parameter count lean relative to context size.
Very low input price at $0.10 per million tokens for a multimodal model.

Watch out for

Weights are not publicly released; you are tied to API access, no local deployment.
Meta has not published detailed benchmark numbers or a model card for independent verification.

Best for

Prompts to try

Analyse a dense document

Here is a long contract. Identify every clause that places an obligation on the buyer, summarise each in one sentence, and flag any that impose a financial penalty. Be specific about section numbers.

Describe and critique an image

I'm attaching a screenshot of a data dashboard. List every metric shown, note any that look anomalous compared to the others, and suggest one question a manager should ask based on what you see.

Extract structured data

Read the attached invoice image and return a JSON object with these fields: vendor_name, invoice_date, line_items (array of description and amount), subtotal, tax, total. If a field is missing from the image, set it to null.

Compare with

Llama 3.1 70B Instruct
Open-weights model
131,072 token context · $0.40/M input, $0.40/M output
Llama 4 Scout
Open-weights model
1,310,720 token context · $0.18/M input, $0.59/M output
Llama 3.1 8B Instruct
Open-weights model
131,072 token context · $0.05/M input, $0.08/M output
Llama 4 Maverick
Open-weights model
1,048,576 token context · $0.19/M input, $0.65/M output

More text models from meta-llama

Questions people ask

Can I run Llama 4 Scout locally?: No. Meta has not released the weights publicly, so Scout is API-only at this time.
What does Llama 4 Scout cost and how long is its context?: It costs $0.10 per million input tokens and $0.30 per million output tokens, with a 327,680-token context window.
How does Scout differ from other Llama 4 models?: Scout is the smaller, cheaper entry in the Llama 4 family — 17 billion parameters with 16 experts — positioned for cost-sensitive multimodal tasks rather than maximum capability.

Captain's verdict
A genuinely cheap multimodal API with a long context window; the closed weights are the one real drawback.

Captain writes these notes from the model's own description and model card. Prices and context sizes are the provider's at the time of writing and can change.

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Updated Oct 22, 2026

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How this score adds up

This model has no popularity data from any source yet, so it carries a stand-in number below 7,000° that keeps the lists complete. It never ranks above a model with data. The breakdown appears here as soon as a source reports it.

Glossary: the terms used here

Heat score: One number for how much a model is used and wanted right now, from 0° to 9,999°. Every list on this site that says hottest is sorted by it and nothing else.
Floor: The 7,000° every model with real data starts from. The scale runs from 0° to 9,999°; the part below the floor is reserved for stand-ins (models without data), so that a model with data always ranks above one without. The floor is the same for everyone and decides nothing; the degrees above it are the measurement.
Download-equivalents: Everything the score treats as a download, added up: downloads themselves, each torrent seeder as 1,000, each AI Horde image and each run on a generation site as one.
Log scale: Counting by tens, not by ones: going from 1,000 to 10,000 downloads adds the same degrees as going from 10,000 to 100,000. It keeps giants from crowding out everything else.
Likes: Thumbs up and likes from every source, worth half a download each on the log scale.
Adoption rate: How many downloads a day a model gets right now, from the difference between two readings of a source. A quarter of the weight of the download term.
Measured and estimated: Measured means two readings of a source exist and the difference is real. Estimated means only today's total is known and the past is spread over the model's life; the site says which one it is showing.
Seeder: Someone who keeps a copy of the weights online for others to download over a torrent, or a hosted mirror doing the same. Each one counts as 1,000 downloads.
AI Horde: A volunteer network that generates images for free on donated computers. The number of images it has made with a model is public, and each counts as one download.
Generation sites: Sites that host their own copies of a model and let people generate with it, such as PixAI, SeaArt and Yodayo. Their downloads count like Civitai's and each generation run as one download.
OpenRouter tokens: A text model served through an API, such as Claude or GPT, has no weights to download, so its usage is measured in tokens: the pieces of text it read and wrote through OpenRouter, a gateway many apps use, over the last 30 days. Every 10,000,000 tokens count as one download. Source: OpenRouter (openrouter.ai/rankings), CC BY 4.0.
Full model (checkpoint): The weights themselves, the file that generates on its own. It gets a bonus because add-ons depend on it.
LoRA and embedding: Small add-on files that steer someone else's weights toward a style, a character or a concept. They cannot run alone.
Family and weights: The base a model is built on, such as SD 1.5, SDXL, Flux or Wan. Every model inherits the release date of its family's weights.
Age: Degrees taken off for how long ago the family's weights came out: 150° a year, up to 750°. It keeps old downloads that had years to pile up from freezing the top of the list.
Family boost: A small, bounded nudge for LoRAs of a family this site is pushing forward right now, always listed in the breakdown.
Stand-in score: A stable number below the floor given to a model no source reports on yet, so lists stay complete. It never ranks above a model with data.
New! (grace period): A model less than 7 days old shows New! instead of a score and holds no place in the rankings. Its score is computed from day one, but a model one day old grows faster than anything, so without the wait the week's #1 would always be whatever came out yesterday rather than what held up for a week.
Trend: How a model's growth over a period compares with the typical model's growth over the same period. Faster than typical reads as a rise, slower as a fall.
Typical model: The middle of the catalog: half the models with a figure grew faster over the period, half slower.

How to install

How to Install Ollama and Run a Local AI (Windows, Mac, Linux)

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Llama 4 Scout 17B 16E Instruct is a text model for the Text / LLM family listed on ModelTrends.ai, a read-only catalog of open source AI image, video and text models. Compare it with other Text / LLM models, check its heat score to see how it is trending, and open it on PirateDiffusion or BitVector to try it.

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