Llama 4 Maverick — Especificações
Compiled by Mustafa Ihsan from the vendor’s published documentation · Last updated
| Desenvolvedor | Meta |
|---|---|
| Tipo | Multimodal (MoE) |
| Modalidade | Texto, Imagem → Texto |
| Parâmetros | 400 bilhões no total / 17 bilhões ativos (MoE) |
| Janela de contexto | 1 milhão |
| Licença | Llama 4 Community (restrita à UE) |
| Pesos abertos | Sim |
| Lançado | 2025 |
| Preço da entrada | $0.2 /1M |
| Preço da saída | $0.8 /1M |
| Provedores de API | Meta, Together, OpenRouter |
Execute-o localmente
| VRAM (4 bits) | ~240 GB |
|---|---|
| GPU mínima | Servidor multi-GPU |
What is Llama 4 Maverick?
Llama 4 Maverick is the larger Llama 4 — 400B total parameters with 17B active across 128
experts, natively multimodal, with a 1M-token context. It is fully open under the Llama 4
Community License, with the important caveat that the licence restricts use in the EU.
That restriction is not a footnote for European teams; it is the deciding factor. The
Llama 4 Community License places conditions on EU-based use that many organisations cannot
accept, which means Maverick is effectively unavailable to a large part of the market
regardless of its technical merits. Where it is usable, the profile is strong: native
multimodality, a 1M context, and $0.15 in / $0.60 out per million tokens — cheap for a
model of this class. Self-hosting requires roughly 240 GB at 4-bit, so a multi-GPU server.
If you need permissively licensed open weights without geographic conditions, Qwen3 235B-A22B
(Apache 2.0) and Mistral Large 3 (Apache 2.0) occupy similar ground with cleaner terms.
Llama 4 Maverick pricing: API cost per 1M tokens
| Entrada (por 1 milhão de tokens) | $0.200 |
|---|---|
| Saída (por 1 milhão de tokens) | $0.800 |
| Razão saída/entrada | 4× |
| Misturada (4:1 entrada:saída) | $0.320 por 1 milhão de tokens |
What Llama 4 Maverick 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 | $0.40 |
| Equipe pequena | 20 milhões de tokens de entrada / 5 milhões de tokens de saída | $8.00 |
| Produção | 200 milhões de tokens de entrada / 50 milhões de tokens de saída | $80 |
Calcule seus próprios números na Calculadora de custos de API de IA.
Cheaper alternatives to Llama 4 Maverick
| Modelo | Custo médio ponderado por US$ 1 milhão | Você economiza |
|---|---|---|
| DeepSeek V4-Flash aberta | $0.168 | 48% cheaper |
| Qwen3 32B aberta | $0.120 | 63% mais barato |
| Llama 4 Scout aberta | $0.140 | 56% cheaper |
Hospedar localmente ou pagar pela API?
Llama 4 Maverick is open-weight, so you can run it yourself. It needs ~240 GB of VRAM at 4-bit (Multi-GPU server). 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 Maverick cost per 1M tokens?
Llama 4 Maverick costs $0.200 per 1M input tokens and $0.800 per 1M output tokens. At a typical 4:1 input-to-output mix that blends to about $0.320 per 1M tokens.
How much does Llama 4 Maverick cost per month?
A small-team workload of 20M input and 5M output tokens a month costs about $8.00 on Llama 4 Maverick. A side project (1M in / 0.25M out) costs roughly $0.40.
What is a cheaper alternative to Llama 4 Maverick?
DeepSeek V4-Flash is the strongest cheaper option in our database at $0.168 per 1M blended — about 48% less than Llama 4 Maverick. It is also open-weight, so self-hosting is an option.
Can I run Llama 4 Maverick locally?
Yes. Llama 4 Maverick is open-weight and needs about ~240 GB of VRAM at 4-bit quantisation (Multi-GPU server).
Why does Llama 4 Maverick 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 Maverick charges 4× 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.