llama-4-scout
Llama 4 Scout 17B Instruct (16E) is a Mixture-of-Experts model from Meta that activates 17B of its 109B parameters, supports native multimodal input and offers a very long context window.
Pricing: Input 20 credits / 1M tokens (≈ $0.10), Output 60 credits / 1M tokens (≈ $0.30).
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Llama 4 Scout API on ModelFold
Llama 4 Scout 17B Instruct (16E) is a Mixture-of-Experts model from Meta that activates 17B of its 109B parameters, supports native multimodal input and offers a very long context window. Try Llama 4 Scout in the ModelFold Playground, then call it from your own code through an OpenAI-compatible chat completions API.
What Is the Llama 4 Scout API
Llama 4 Scout at a glance
Llama 4 Scout is a chat model from Meta. It accepts text and images as input and returns text, with a context window of up to 1M tokens.
Use it without hosting anything
On ModelFold you call Llama 4 Scout with a single API key — no model weights to download and no inference servers to run. The same key also works for the other chat, image, video and audio models on ModelFold.
Core Capabilities of Llama 4 Scout
Up to 1M tokens of context
Keep long conversations, large documents and sizeable code in a single request, so the model can reason over the full material instead of fragments.
Understands images
Besides text, Llama 4 Scout accepts images as input, so a single request can combine instructions with visual or other media.
Function calling
Llama 4 Scout supports tool (function) calling: describe your functions in the request and the model returns structured calls your code can run — the basis for agents and integrations.
Structured outputs
Ask for JSON that follows a schema (response_format), which makes extraction, classification and system integrations more reliable.
How to Use Llama 4 Scout on ModelFold
- 1
Step 1: Try it in the Playground
Open the Playground at the top of this page, type a prompt and see how Llama 4 Scout responds before writing any code.
- 2
Step 2: Create an API key
Sign in to ModelFold and create an API key in the console. Keep it secret and send it as a Bearer token.
- 3
Step 3: Send a request
Call POST https://modelfold.ai/v1/chat/completions with "model": "llama-4-scout" and your messages. Existing OpenAI-compatible SDKs work by changing the base URL and key.
- ✓
Step 4: Ship and monitor
Integrate the response into your product and check token usage and spend for every call in the console logs.
What You Can Build with Llama 4 Scout
Long-document workflows
Feed in long reports, contracts, manuals or codebases and ask for summaries, comparisons and answers grounded in the material.
Screenshot and image understanding
Read screenshots, charts, diagrams and photos alongside text — for UI review, visual QA or turning images into structured data.
High-volume pipelines
Run classification, extraction, rewriting and batch jobs at scale where cost per request matters.
Multilingual products
Serve users in many languages with translation, localized support replies and cross-language search.
Frequently Asked Questions
What is Llama 4 Scout?⌄
Llama 4 Scout 17B Instruct (16E) is a Mixture-of-Experts model from Meta that activates 17B of its 109B parameters, supports native multimodal input and offers a very long context window.
Which inputs does Llama 4 Scout support?⌄
Llama 4 Scout accepts text and images and returns text. Through ModelFold you can send text and images (OpenAI-style image_url content); in the Playground you can also attach documents, which are converted to text.
How long is the context window of Llama 4 Scout?⌄
Up to 1M tokens, shared between your input and the model’s output.
Does Llama 4 Scout support function calling?⌄
Yes. Llama 4 Scout supports tools / function calling through the standard tools parameter of the chat completions API.
How is Llama 4 Scout billed on ModelFold?⌄
Pay as you go by tokens: input and output tokens are charged at the per-million-token prices shown on this page. There is no monthly fee, and failed requests are not charged.
How do I call Llama 4 Scout through the API?⌄
Send a POST request to https://modelfold.ai/v1/chat/completions with "model": "llama-4-scout", using your ModelFold API key as the Bearer token.
| Input | 20 credits 1M tokens≈ $0.10 |
| Output | 60 credits 1M tokens≈ $0.30 |
Billed per token: input and output are priced separately, and thinking tokens count as output. The Playground charges exactly the same way.
Read API docs →AI Agents · Claude Code / Cursor / Trae →curl https://modelfold.ai/v1/chat/completions \
-H "Authorization: Bearer $MODELFOLD_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"llama-4-scout","messages":[{"role":"user","content":"Hello"}]}'