Llama 4 Scout
meta/llama-4-scoutTen million tokens of context, the largest window on the market, open weights, commodity prices.
Model overview
Context window
10M
tokens
Input price
—
quoted on request
Output price
—
quoted on request
Weekly volume
—
tokens / week
Open weights
Yes
self-hostable
Capabilities
4
of 11 tags
Description
Llama 4 Scout is a language model from Meta. Webparam does not stock this model yet and can source it on request — tell us what you need it for and we will come back with availability and a price. Meta’s stated focus is open weights at the frontier (Llama). Its mean it can also be self-hosted under its licence.
Capabilities
- Tool calling
- Calls functions you define and returns their arguments as structured data, the basis of agents.
- Streaming
- Emits tokens as they are generated, so answers appear progressively rather than all at once.
- Open weights
- The parameters are published, so the model can be inspected, fine-tuned and self-hosted under its licence.
- Long context
- Built for inputs well beyond the mainstream window, whole repositories, archives or corpora.
Strengths & weaknesses
Strengths
- 10M-token context: market-largest
- Open weights
- Very low pricing
Weaknesses
- Recall degrades toward extreme depths
- No JSON mode
Pricing
Free| Rate | Price | Unit |
|---|---|---|
| Input | $0.00 | per 1M tokens |
| Output | — not charged | — |
Free at the point of use; fair-use rate limits apply to the free tier. Illustrative placeholder pricing.
Context window
10Mtokens
covers prompt and response together, so a long input leaves less room for the answer.
Supported features
| Feature | Support |
|---|---|
| Tool calling | Supported |
| JSON mode | Not supported |
| Streaming | Supported |
| Vision input | Not supported |
| Audio input | Not supported |
| Long context | Supported |
| Open weights | Supported |
| Reasoning | Not supported |
| Fine-tunable | Not supported |
| Batch | Not supported |
| Caching | Not supported |
Example use cases
Entire-corpus prompts
10M-token context: market-largest
Archive analysis
Open weights
Extreme-context research
Very low pricing
Comparison
| Attribute | Llama 4 Scout | MiniMax Text 01 | Grok 4 Fast |
|---|---|---|---|
| Context window | 10M | 1M | 2M |
| Input price | $0.00 | $0.00 | $0.00 |
| Output price | — not charged | — not charged | — not charged |
| Tool calling | Supported | Supported | Supported |
| JSON mode | Not supported | Not supported | Supported |
| Vision input | Not supported | Not supported | Not supported |
| Open weights | Supported | Supported | Not supported |
| Prompt caching | Not supported | Not supported | Not supported |
Best value in each row is highlighted. Illustrative placeholder data.
Provider information
View Meta →Meta · Menlo Park, United States · est. 2004 (FAIR/Llama programme from 2023)
Meta made open weights a frontier strategy. Llama downloads number in the hundreds of millions, and the Llama 4 generation pushed open models to 10M-token context, the largest window on the market.
Recent releases
Documentation
Frequently asked questions
Input is billed at $0.00 per 1M tokens, and there is no separate output charge. Every figure on this page is an illustrative placeholder.