Llama 4 Scout — Especificações
Compiled by Mustafa Ihsan from the vendor’s published documentation · Last updated
| Desenvolvedor | Meta |
|---|---|
| Tipo | Multimodal (MoE) |
| Modalidade | Texto, Imagem → Texto |
| Parâmetros | 109B no total / 17B ativos (MoE) |
| Janela de contexto | 10 milhões |
| Licença | Llama 4 Community (restrita à UE) |
| Pesos abertos | Sim |
| Lançado | 2025 |
| Preço da entrada | $0.1 /1M |
| Preço da saída | $0.3 /1M |
| Provedores de API | Meta, Together, OpenRouter |
Execute-o localmente
| VRAM (4 bits) | ~65 GB |
|---|---|
| GPU mínima | H100 80 GB / Mac 128 GB |
What is Llama 4 Scout?
Llama 4 Scout is Meta’s natively multimodal open mixture-of-experts — 109B total
parameters with 17B active across 16 experts, and an industry-leading 10M-token context
window. It fits on a single 80 GB GPU at 4-bit (about 65 GB), or a 128 GB Mac, and ships
under the Llama 4 Community License with the same EU restriction as Maverick.
The 10M context is an order of magnitude beyond the 1M that counts as generous elsewhere,
and it changes what is architecturally possible: entire codebases, full document archives or
long video transcripts can go into a single prompt instead of through a retrieval pipeline.
Whether that is a good idea is a separate question — attention quality across ten million
tokens is not uniform, and retrieval still tends to beat brute force on accuracy and cost —
but for problems where chunking genuinely destroys the signal, Scout is close to unique. That
it does this while fitting on one 80 GB card is the more practical achievement. At $0.10 in /
$0.30 out per million tokens, the API is inexpensive enough to prototype against before
committing to hardware.
Llama 4 Scout pricing: API cost per 1M tokens
| Entrada (por 1 milhão de tokens) | $0.100 |
|---|---|
| Saída (por 1 milhão de tokens) | $0.300 |
| Razão saída/entrada | 3× |
| Misturada (4:1 entrada:saída) | $0.140 por 1 milhão de tokens |
What Llama 4 Scout costs per month
Gasto mensal real com uma proporção de entrada para saída de 4:1 — a razão efetivamente gerada por cargas de trabalho típicas de chat ou RAG.
| Carga de trabalho | Tokens/mês | Custo por mês |
|---|---|---|
| Projeto paralelo | 1 milhão de tokens de entrada / 0,25 milhão de tokens de saída | $0.18 |
| Equipe pequena | 20 milhões de tokens de entrada / 5 milhões de tokens de saída | $3.50 |
| Produção | 200 milhões de tokens de entrada / 50 milhões de tokens de saída | $35 |
Calcule seus próprios números na Calculadora de custos de API de IA.
Cheaper alternatives to Llama 4 Scout
| Modelo | Custo médio ponderado por US$ 1 milhão | Você economiza |
|---|---|---|
| Qwen3 32B aberta | $0.120 | 14% cheaper |
| Gemma 3 27B aberta | $0.0960 | 31% cheaper |
Hospedar localmente ou pagar pela API?
Llama 4 Scout is open-weight, so you can run it yourself. It needs ~65 GB of VRAM at 4-bit (H100 80GB / Mac 128GB). Self-hosting only beats the API once your volume is high enough to keep that hardware busy — the calculadora de autohospedagem versus API calcula o ponto de equilíbrio para seu volume de tokens.
Perguntas frequentes
How much does Llama 4 Scout cost per 1M tokens?
Llama 4 Scout costs $0.100 per 1M input tokens and $0.300 per 1M output tokens. At a typical 4:1 input-to-output mix that blends to about $0.140 per 1M tokens.
How much does Llama 4 Scout cost per month?
A small-team workload of 20M input and 5M output tokens a month costs about $3.50 on Llama 4 Scout. A side project (1M in / 0.25M out) costs roughly $0.18.
What is a cheaper alternative to Llama 4 Scout?
Qwen3 32B is the strongest cheaper option in our database at $0.120 per 1M blended — about 14% less than Llama 4 Scout. It is also open-weight, so self-hosting is an option.
Can I run Llama 4 Scout locally?
Yes. Llama 4 Scout is open-weight and needs about ~65 GB of VRAM at 4-bit quantisation (H100 80GB / Mac 128GB).
Why does Llama 4 Scout charge more for output than input?
Output tokens are generated one at a time and cannot be batched the way a prompt can, so they cost the provider more to serve. Llama 4 Scout charges 3× more for output, which is why prompt-heavy workloads are far cheaper to run than generation-heavy ones.
Os preços correspondem às tarifas listadas oficialmente para a API principal do modelo e são revisados conforme os provedores os atualizam. Descontos por volume, processamento em lote ou entradas em cache não estão incluídos. Compare todos os modelos lado a lado na Banco de dados de modelos de IA ou o Ranking de LLMs.