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Free AI Visibility AuditLlama 4 Scout is an open-weight multimodal model from Meta, built as a mixture-of-experts system that activates a small fraction of its total parameters on each pass. It takes both text and images as input and carries one of the largest context windows available in an openly released model.
MetaVerified 24/09/2026Released 05/04/202501 / decision
Each figure sits next to the middle of the field, so it can be read as dear or cheap, wide or narrow, rather than floating on its own. Compared against the 126 models we track, not against an absolute standard.
Capability
Not measured
No published benchmark scores yet
Input / 1M tokens
$0.10
field median $0.30
Output / 1M tokens
$0.30
field median $1.25
Context
1,311K
field median 524K
02 / overview
Four questions, answered separately, because somebody arrives at one of them rather than at the top of the page.
The job it was built for, and the job it is not for.
Llama 4 Scout is an instruction-tuned language model with native image input, designed for high-volume work over very long inputs rather than for hard reasoning. The mixture-of-experts design means only part of the model runs per token, which is what keeps it cheap to serve at scale. It is not a frontier reasoning model, and it is the wrong tool if your task needs long generated answers or careful multi-step logic.
Where it sits in its line, and when a sibling is the better pick.
Llama 4 Scout arrived in April 2025 as the smaller, faster member of Meta's Llama 4 family. Reach for it when the job is reading, classifying, extracting or summarising across a large body of text or documents with images mixed in, and cost per call matters more than depth. Pick a larger sibling or a reasoning-focused model when the work is genuinely difficult rather than merely long.
The API, the apps it powers, and what its limits let you do.
Llama 4 Scout is reached through the usual hosted inference providers and, because the weights are open, through self-hosted deployments as well. The very large context window lets you put an entire document set, codebase or long transcript into a single call instead of building a retrieval layer around it, while image input means screenshots, scans and charts can go in alongside the text. The output ceiling is modest, so it suits jobs that read a lot and write a little.
What changes because this exists, or why it does not.
Llama 4 Scout makes long-context work cheap enough to run as routine infrastructure, so pipelines that previously needed chunking, embedding and retrieval plumbing can often just pass the whole thing in. For teams processing large volumes of mixed text and image documents, that removes a layer of engineering. Nothing here is novel in capability terms, what changed is the price at which the capability is available on open weights.
Follows Llama 4 Maverick, and is the newest in its line. See the whole line.
03 / evidence
Split by category, so a strong number never hides a thin evidence base. Verified means we read it on the benchmark's own published results; a provider's claim about its own model is shown and labelled rather than dropped.
No published benchmark scores for this model yet.
We publish a score only where we can link the result to where it was published. Until a benchmark result for this model exists in a source we read, this section stays empty rather than being filled with a provider's marketing figure.
How we decide what counts as evidence04 / ledger
Every published row, grouped by category, each compared with the best published score on the same benchmark. 'Is 64% good' is a question nobody can answer; '26 points behind the leader' is one anybody can.
Nothing in the ledger yet.
Each row here carries a score, the benchmark it came from, what the leading model scored on the same test, and a link to the published result. Rows appear as results are published and read.
How we decide what counts as evidence05 / capability
Where this model is strong, and against how many peers. Ranks are against models with evidence in that category, not against everything we track: ranking against models nobody tested would rank who published, not who is better.
No category scores to shape yet.
A category score is the weighted mean of the benchmarks published for it. With no published rows there is nothing to average, and an empty chart drawn at zero would say something false.
How we decide what counts as evidence06 / cost
List API rates as last read from the provider, with the source on every row, plus every change we have recorded since we started tracking it.
| Charge | Price | Unit | Read on | Source |
|---|---|---|---|---|
| Input | $0.10 | per 1M tokens | 2026-09-24 | Check |
| Output | $0.30 | per 1M tokens | 2026-09-24 | Check |
Llama 4 Scout sits at the low end of the market, cheap enough that running it across large document volumes is a reasonable default rather than a budget decision. Output costs a few times more than input, which matters little here since the model is built for reading far more than writing, and self-hosting the open weights removes per-token cost entirely if you have the infrastructure.
| Workload | Input / month | Output / month | Cost |
|---|---|---|---|
| A small product team | 20M tokens | 5M tokens | $3.50 |
| A busy support assistant | 200M tokens | 40M tokens | $32.00 |
| A document pipeline | 1000M tokens | 100M tokens | $130.00 |
List API rates, no caching and no batch discount, which both providers offer and which change the answer a great deal. Treat these as the ceiling, not the bill.
07 / specs
As listed by the provider's own catalogue and re-read every few hours. Anything absent is absent there too.
| Context window | 1,310,720 tokens |
|---|---|
| Maximum output | 16,384 tokens |
| Modalities | text, image |
| Released | 05/04/2025 |
| Status | Current |
| Catalogue identifier | meta-llama/llama-4-scout |
08 / lineage
What this model replaced, what replaced it, and what else its provider has in the field.
Also from Meta
09 / line
Every model in this family in release order, so a page from eight months ago says in one glance that two newer ones exist.
Came before
← Llama 4 MaverickCame after
Nothing newer in this line yet.
Ordered by release date and worked out from the naming, so a new member slots in as soon as its page exists. A retired model keeps its page and its place in the line.
10 / notes
What this model changes for a brand trying to be cited in AI answers, and every change we have logged since it launched.
What it changes for you
Open-weight models like Llama 4 Scout get embedded in other companies' products, so your brand can be summarised by it without any of the usual AI search surfaces being involved. Because it is cheap enough to run over very large inputs, whatever it reads about you is likely to be read in full rather than in snippets, which rewards clear, complete, self-contained pages over keyword-tuned fragments. Assume your documentation and comparison pages are being ingested wholesale, and write them so a machine reading everything at once still comes away with the right positioning.
Buyers rarely meet Llama 4 Scout by name. They meet it inside products built on it, as the model quietly handling document ingestion, support triage or summarisation behind someone else's interface, and through Meta's own assistant surfaces. Developers meet it directly through hosted inference APIs or by running the weights themselves.
Nothing published here yet. Changes appear within hours of a provider announcing them.
11 / questions
The things people ask about this model, answered from what is on this page rather than from anywhere else.
Models change what gets cited. We measure whether AI answers name your brand or your competitors across every assistant, and show you what to change.