Model Library/Llama 4 Maverick Instruct
Llama

Llama 4 Maverick Instruct

meta-llama/llama-4-maverick-17b-128e-instruct-fp8
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.

Recursos

API serverless

Documentação

meta-llama/llama-4-maverick-17b-128e-instruct-fp8 is available via Novita's serverless API, where you pay per token. There are several ways to call the API, including OpenAI-compatible endpoints with exceptional reasoning performance.

Serverless disponível

Execute consultas imediatamente, pague apenas pelo uso

Entrada$0.27 / M Tokens
Saída$0.85 / M Tokens

Use os exemplos de código a seguir para integrar com nossa API:

1from openai import OpenAI
2
3client = OpenAI(
4    api_key="<Your API Key>",
5    base_url="https://api.novita.ai/openai"
6)
7
8response = client.chat.completions.create(
9    model="meta-llama/llama-4-maverick-17b-128e-instruct-fp8",
10    messages=[
11        {"role": "system", "content": "You are a helpful assistant."},
12        {"role": "user", "content": "Hello, how are you?"}
13    ],
14    max_tokens=8192,
15    temperature=0.7
16)
17
18print(response.choices[0].message.content)

Informações

Provedor
Llama
Quantização
fp8

Funcionalidades compatíveis

Comprimento do contexto
1048576
Saída máxima
8192
Serverless
Compatível
Structured Output
Compatível
Capacidades de entrada
text, image
Capacidades de saída
text

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