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Favicon de meta-llama

Meta: Llama 4 Maverick

meta-llama/llama-4-maverick

Pesos do modelo
Comparar

Llama 4 Maverick 17B Instruct (128E) is a high-capacity multimodal language model from Meta, built on a mixture-of-experts (MoE) architecture with 128 experts and 17 billion active parameters per forward pass (400B total). It supports multilingual text and image input, and produces multilingual text and code output across 12 supported languages. Optimized for vision-language tasks, Maverick is instruction-tuned for assistant-like behavior, image reasoning, and general-purpose multimodal interaction.

Maverick features early fusion for native multimodality and a 1 million token context window. It was trained on a curated mixture of public, licensed, and Meta-platform data, covering ~22 trillion tokens, with a knowledge cutoff in August 2024. Released on April 5, 2025 under the Llama 4 Community License, Maverick is suited for research and commercial applications requiring advanced multimodal understanding and high model throughput.

Modalidades
Preço de entrada / saída
US$ 0,1875 / US$ 0,6525por 1M
Contexto
524K
Lançado
5 de abr. de 2025
Data limite de conhecimento
ago. de 2024
Comparar
ProvedoresPreçosDesempenhoDisponibilidadeAvaliações de desempenhoAplicativosAtividadePerguntas frequentesExplorar

Provedores

Diferentes empresas hospedam o mesmo modelo. O OpenRouter encaminha sua solicitação para uma delas com base no modo de roteamento que você escolhe — Equilibrado (preço + velocidade), Nitro (mais rápido) ou Floor (mais barato).

Preços

O preço médio que os clientes realmente pagam por este modelo, ao lado dos preços publicados pelos provedores. O cache e os descontos fazem com que o preço efetivamente pago seja frequentemente bem inferior ao preço anunciado.

Desempenho

A vazão indica a velocidade com que o modelo escreve (tokens por segundo — quanto maior, melhor). A latência é o tempo total de ida e volta (quanto menor, melhor). TTFT é o tempo até o primeiro token — quanto tempo leva para você ver algo aparecer (quanto menor, melhor).

Disponibilidade

A disponibilidade é a porcentagem dos últimos 3 dias em que pelo menos um provedor estava respondendo às solicitações. A disponibilidade é a porcentagem de tempo em que a inferência foi realizada com sucesso. O OpenRouter monitora continuamente e usa o próximo melhor provedor quando um deles retorna um erro.

Avaliações de desempenho

Pontuações em avaliações padronizadas e as próprias saídas deste modelo para prompts compartilhados de testes de desempenho. Porcentagens maiores são melhores — e cada posição mostra onde este modelo fica entre os modelos avaliados naquela fonte.

Resumo das pontuações de avaliações comparativas para Meta: Llama 4 Maverick (Artificial Analysis e Design Arena)
FonteAvaliação comparativaPontuação
Artificial AnalysisLlama 4 Maverick Coding Index16,3
Artificial AnalysisLlama 4 Maverick Agentic Index0,6
Artificial AnalysisLlama 4 Maverick GPQA Diamond67.1%
Artificial AnalysisLlama 4 Maverick HLE4.9%
Artificial AnalysisLlama 4 Maverick IFBench43.0%
Artificial AnalysisLlama 4 Maverick τ²-Bench Telecom17.8%
Artificial AnalysisLlama 4 Maverick AA-LCR50.0%
Artificial AnalysisLlama 4 Maverick τ-Bench Banking3.7%
Artificial AnalysisLlama 4 Maverick GDPval-AA0.0%
Artificial AnalysisLlama 4 Maverick CritPt0.0%
Artificial AnalysisLlama 4 Maverick LiveCodeBench39.7%
Artificial AnalysisLlama 4 Maverick SciCode31.7%
Artificial AnalysisLlama 4 Maverick Terminal-Bench Hard6.8%
Artificial AnalysisLlama 4 Maverick Terminal-Bench 4.00.0%
Artificial AnalysisLlama 4 Maverick Terminal-Bench 2.17.9%
Artificial AnalysisLlama 4 Maverick MMLU-Pro80.9%
Artificial AnalysisLlama 4 Maverick AIME 202519.3%
Artificial AnalysisLlama 4 Maverick AIME39.0%
Artificial AnalysisLlama 4 Maverick MATH-50088.9%
Design ArenaLlama 4 Maverick Models Arena 3D Elo898
Design ArenaLlama 4 Maverick Models Arena Code Categories Elo878
Design ArenaLlama 4 Maverick Models Arena Data Visualization Elo882
Design ArenaLlama 4 Maverick Models Arena Game Development Elo836
Design ArenaLlama 4 Maverick Models Arena UI Component Elo894
Design ArenaLlama 4 Maverick Models Arena Website Elo869

Aplicativos

Aplicativos públicos que enviam mais tráfego para este modelo. Um bom indicador de como são as cargas de trabalho reais em produção — e uma pista sobre os casos de uso para os quais este modelo é mais adequado.

Atividade

Volume de tokens e tráfego de requisições para este modelo ao longo do tempo.

API

Código pronto para chamar este modelo. A API do OpenRouter é compatível com a OpenAI — a maioria dos SDKs funciona apenas trocando a URL base. A única coisa que muda entre os modelos é o slug do modelo abaixo.

Atualmente indisponível por meio do roteamento intrarregional da UE. Documentação de roteamento na região

Explorar mais modelos

Modelos de IA com visão: LLMs multimodais para compreensão de imagensColeçãoClassificações de modelos de IAClassificação
Latência / taxa de transferência
US$ 0,3500US$ 1,1500------
--
US$ 0,1875US$ 0,6525US$ 0,050,65s14 tps
99,97%
US$ 0,3500US$ 1,0000US$ 0,170,26s77 tps
100,00%
Taxa de transferência
77tok/s

P50, melhor entre os provedores

Latência
0,26s

P50, melhor provedor

Avaliações de desempenho do AutoExacto
GPQA DiamondTAU-BenchDigitalOcean66,7%37,3%
Disponibilidade (3d)O modelo estava acessível. Requisição roteada para um provedor.
100,00%
Disponibilidade (3d)O modelo retornou inferência de qualquer provedor. Erros e respostas vazias contam negativamente para essa métrica.
99,96%

Disponibilidade nos últimos 3 dias

Últimas 72 horas
Disponibilidade 99,96%
Há 3 diasHá 2 diasOntemAgora

Disponibilidade nas últimas 24 horas

OpenRouter Availability
99,96%
Without Routing
92,56%

Quando ocorre um erro em um provedor upstream, podemos nos recuperar roteando para outro provedor saudável, se os filtros da sua solicitação permitirem. Você pode acessar os dados de disponibilidade por provedor de forma programática pela API de endpoints. Saiba mais sobre nossas opções de balanceamento de carga e roteamento de provedores.

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Perguntas frequentes

Llama 4 Maverick 17B Instruct (128E) is a high-capacity multimodal language model from Meta, built on a mixture-of-experts (MoE) architecture with 128 experts and 17 billion active parameters per forward pass (400B total). It supports multilingual text and image input, and produces multilingual text and code output across 12 supported languages.

Llama 4 Maverick custa US$ 0,1875/M de tokens de entrada e US$ 0,6525/M de tokens de saída, com taxas separadas para Cache Read por US$ 0,05/M tokens.

Llama 4 Maverick tem uma janela de contexto de 524.288 tokens. Ele aceita até 16.384 tokens de conclusão.

Sim. Llama 4 Maverick aceita tools e tool_choice para chamadas de função. Também oferece suporte a saídas estruturadas por meio de um esquema JSON em response_format.

Llama 4 Maverick aceita texto e imagens como entrada e retorna texto.

Llama 4 Maverick é disponibilizado por 3 provedores no OpenRouter: DigitalOcean, Parasail e Google Vertex. As solicitações são roteadas para o melhor provedor disponível, com alternância automática para os demais em caso de falha, e você pode fixar ou excluir provedores com o roteamento de provedores.

Llama 4 Maverick foi lançado em 5 de abril de 2025. Seu conhecimento vai até 31 de agosto de 2024.

Mais modelos de Meta Llama

Llama 3.3 8B Instruct

A lightweight and ultra-fast variant of Llama 3.3 70B, for use when quick response times are needed most.

TextoContexto de 128K
Llama Guard 4 12B

Llama Guard 4 is a Llama 4 Scout-derived multimodal pretrained model, fine-tuned for content safety classification. Similar to previous versions, it can be used to classify content in both LLM inputs (prompt classification) and in LLM responses (response classification). It acts as an LLM—generating text in its output that indicates whether a given prompt or response is safe or unsafe, and if unsafe, it also lists the content categories violated.

Llama Guard 4 was aligned to safeguard against the standardized MLCommons hazards taxonomy and designed to support multimodal Llama 4 capabilities. Specifically, it combines features from previous Llama Guard models, providing content moderation for English and multiple supported languages, along with enhanced capabilities to handle mixed text-and-image prompts, including multiple images. Additionally, Llama Guard 4 is integrated into the Llama Moderations API, extending robust safety classification to text and images.

TextoContexto de 164KUS$ 0,18 / US$ 0,18
Llama 4 Maverick

Llama 4 Maverick 17B Instruct (128E) is a high-capacity multimodal language model from Meta, built on a mixture-of-experts (MoE) architecture with 128 experts and 17 billion active parameters per forward pass (400B total). It supports multilingual text and image input, and produces multilingual text and code output across 12 supported languages. Optimized for vision-language tasks, Maverick is instruction-tuned for assistant-like behavior, image reasoning, and general-purpose multimodal interaction.

Maverick features early fusion for native multimodality and a 1 million token context window. It was trained on a curated mixture of public, licensed, and Meta-platform data, covering ~22 trillion tokens, with a knowledge cutoff in August 2024. Released on April 5, 2025 under the Llama 4 Community License, Maverick is suited for research and commercial applications requiring advanced multimodal understanding and high model throughput.

TextoContexto de 524KUS$ 0,1875 / US$ 0,6525
Llama 4 Scout

Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input (text and image) and multilingual output (text and code) across 12 supported languages. Designed for assistant-style interaction and visual reasoning, Scout uses 16 experts per forward pass and features a context length of 10 million tokens, with a training corpus of ~40 trillion tokens.

Built for high efficiency and local or commercial deployment, Llama 4 Scout incorporates early fusion for seamless modality integration. It is instruction-tuned for use in multilingual chat, captioning, and image understanding tasks. Released under the Llama 4 Community License, it was last trained on data up to August 2024 and launched publicly on April 5, 2025.

TextoContexto de 1,3MUS$ 0,10 / US$ 0,30
Llama Guard 3 8B

Llama Guard 3 is a Llama-3.1-8B pretrained model, fine-tuned for content safety classification. Similar to previous versions, it can be used to classify content in both LLM inputs (prompt classification) and in LLM responses (response classification). It acts as an LLM – it generates text in its output that indicates whether a given prompt or response is safe or unsafe, and if unsafe, it also lists the content categories violated.

Llama Guard 3 was aligned to safeguard against the MLCommons standardized hazards taxonomy and designed to support Llama 3.1 capabilities. Specifically, it provides content moderation in 8 languages, and was optimized to support safety and security for search and code interpreter tool calls.

Texto
Llama 3.3 70B Instruct

The Meta Llama 3.3 multilingual large language model (LLM) is a pretrained and instruction tuned generative model in 70B (text in/text out). The Llama 3.3 instruction tuned text only model is optimized for multilingual dialogue use cases and outperforms many of the available open source and closed chat models on common industry benchmarks.

Supported languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.

Model Card

TextoContexto de 131KUS$ 0,10 / US$ 0,32
Llama 3.2 90B Vision Instruct

The Llama 90B Vision model is a top-tier, 90-billion-parameter multimodal model designed for the most challenging visual reasoning and language tasks. It offers unparalleled accuracy in image captioning, visual question answering, and advanced image-text comprehension. Pre-trained on vast multimodal datasets and fine-tuned with human feedback, the Llama 90B Vision is engineered to handle the most demanding image-based AI tasks.

This model is perfect for industries requiring cutting-edge multimodal AI capabilities, particularly those dealing with complex, real-time visual and textual analysis.

Click here for the original model card.

Usage of this model is subject to Meta's Acceptable Use Policy.

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Llama 3.2 3B Instruct

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Trained on 9 trillion tokens, the Llama 3.2 3B model excels in instruction-following, complex reasoning, and tool use. Its balanced performance makes it ideal for applications needing accuracy and efficiency in text generation across multilingual settings.

Click here for the original model card.

Usage of this model is subject to Meta's Acceptable Use Policy.

TextoContexto de 131KUS$ 0,05 / US$ 0,33
Llama 3.2 1B Instruct

Llama 3.2 1B is a 1-billion-parameter language model focused on efficiently performing natural language tasks, such as summarization, dialogue, and multilingual text analysis. Its smaller size allows it to operate efficiently in low-resource environments while maintaining strong task performance.

Supporting eight core languages and fine-tunable for more, Llama 1.3B is ideal for businesses or developers seeking lightweight yet powerful AI solutions that can operate in diverse multilingual settings without the high computational demand of larger models.

Click here for the original model card.

Usage of this model is subject to Meta's Acceptable Use Policy.

TextoContexto de 60KUS$ 0,027 / US$ 0,201
Llama 3.2 11B Vision Instruct

Llama 3.2 11B Vision is a multimodal model with 11 billion parameters, designed to handle tasks combining visual and textual data. It excels in tasks such as image captioning and visual question answering, bridging the gap between language generation and visual reasoning. Pre-trained on a massive dataset of image-text pairs, it performs well in complex, high-accuracy image analysis.

Its ability to integrate visual understanding with language processing makes it an ideal solution for industries requiring comprehensive visual-linguistic AI applications, such as content creation, AI-driven customer service, and research.

Click here for the original model card.

Usage of this model is subject to Meta's Acceptable Use Policy.

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It has demonstrated strong performance compared to leading closed-source models in human evaluations.

To read more about the model release, click here. Usage of this model is subject to Meta's Acceptable Use Policy.

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To read more about the model release, click here. Usage of this model is subject to Meta's Acceptable Use Policy.

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It has demonstrated strong performance compared to leading closed-source models in human evaluations.

To read more about the model release, click here. Usage of this model is subject to Meta's Acceptable Use Policy.

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LlamaGuard 2 8B

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For best results, please use raw prompt input or the /completions endpoint, instead of the chat API.

It has demonstrated strong performance compared to leading closed-source models in human evaluations.

To read more about the model release, click here. Usage of this model is subject to Meta's Acceptable Use Policy.

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Meta's latest class of model (Llama 3) launched with a variety of sizes & flavors. This 70B instruct-tuned version was optimized for high quality dialogue usecases.

It has demonstrated strong performance compared to leading closed-source models in human evaluations.

To read more about the model release, click here. Usage of this model is subject to Meta's Acceptable Use Policy.

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Meta's latest class of model (Llama 3) launched with a variety of sizes & flavors. This 8B instruct-tuned version was optimized for high quality dialogue usecases.

It has demonstrated strong performance compared to leading closed-source models in human evaluations.

To read more about the model release, click here. Usage of this model is subject to Meta's Acceptable Use Policy.

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