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Llama 4 Maverick

Llama 4 Maverick

4 Maverick (17B-128E)ย ยทย Family: Llama
Meta's natively multimodal model (Llama 4) with an MoE architecture: 17B active and 400B total parameters (128 experts), 1M context, early fusion of text and image; open weights (Llama 4 Community License).
โœ“ Activeโœ“ Public accessโš– Open weightsLLMMultimodalTool-using model๐Ÿ“ Llama
Context window
1M
tokens
Parameters
400B total / 17B active (MoE: 128 ekspertow)
parameters
Release date
5 April 2025
Access:DownloadAPIHostedDeployment:๐Ÿ’ป Localโ˜ Cloud

Overview

Llama 4 Maverick is Meta's natively multimodal language model, introduced on April 5, 2025 as part of the Llama 4 family (alongside Llama 4 Scout). It is the first Llama generation built on a Mixture-of-Experts (MoE) architecture, with alternating dense and MoE layers. It has 17B active and 400B total parameters, with 128 experts.

The model uses early fusion, combining text and image tokens into a single backbone (native multimodality). It supports up to 1M tokens of context. It was built in part through distillation from the larger Llama 4 Behemoth model (288B active, 16 experts). According to Meta, Maverick is the best-in-class multimodal model, beating GPT-4o and Gemini 2.0 Flash on a range of benchmarks, and its experimental chat version scored an ELO of 1417 on LMArena.

Weights are released under the Llama 4 Community License (open weights with restrictions, e.g. for entities with >700M monthly active users), allowing self-hosting. The model is available via Hugging Face and provider APIs (e.g. OpenRouter).

Classification
LLMMultimodalTool-using model
Family: Llama
Access & deployment
DownloadAPIHosted
LocalCloud
Weights: Open weights
Key parameters
๐Ÿ“ Context: 1M
๐Ÿงฉ Parameters: 400B total / 17B active (MoE: 128 ekspertow)
โœ“ Toolsย ยทย โœ“ Fine-tuning
๐Ÿ“ฅ Input: text, image

Technical specification

Context window
1M
tokens
Parameters
400B total / 17B active (MoE: 128 ekspertow)
parameters
Knowledge cutoff
1 Aug 2024
Knowledge boundary
License
Llama 4 Community License
Hardware requirements
Large MoE model (400B weights) - requires a multi-accelerator server (e.g. a GPU node) or quantization for self-hosting; 17B active parameters lower inference cost. Supported: Transformers, vLLM.
Features:โœ“ Tool useโœ“ Fine-tuning
Modalities
โฌ‡ Input
textimage
โฌ† Output
textcode

Capabilities and applications

Native model capabilities
Reasoning
The model's ability to reason logically and solve complex problems.
Category: reasoning
Multi-step reasoning
Carrying out multi-step chains of reasoning across long, complex tasks.
Category: reasoning
Long context
Support for large context windows โ€” tens to hundreds of thousands (or millions) of input tokens. Enables analysis of entire codebases, long documents, and many parallel conversations without losing earlier information. GPT-5.1 supports 400,000 tokens.
Category: language
Coding
Generating, analysing and modifying code in many programming languages. Covers writing functions, debugging, refactoring, code review, and creating tests. Measured by benchmarks such as HumanEval and SWE-bench.
Category: coding
Function Calling
Category: planning
Structured output
Producing data in structured formats such as JSON.
Category: structured_generation
Image understanding
Analysing and interpreting the content of images.
Category: vision
Chart understanding
Reading and interpreting charts, tables and diagrams.
Category: vision
OCR
Recognising text within images and documents.
Category: vision
Multilingual
Competence in many natural languages (from a few to over a hundred): understanding, generation, translation, and code-switching within a single conversation. Frontier models support a wide range of languages with comparable quality.
Category: language
Planning
Forming and executing action plans for complex tasks.
Category: planning

Benchmark results

1 benchmark
LMArena (Chatbot Arena) - wersja eksperymentalna
Elo ยท An experimental chat version of Llama 4 Maverick scored an ELO of 1417 on LMArena (per Meta).
1417Elo
๐Ÿ“… 5 Apr 2025
Source: Meta blog (2025-04-05). Score refers to the experimental version, not the released weights.

Pricing

Technical architecture

Training Techniques
Related Technologies

Deployment and security

๐Ÿ”’ Security / Enterprise
โœ“ Verified enterprise information

Open weights (Llama 4 Community License) - self-hosting / on-premises possible with full data control. The license imposes restrictions (e.g. entities with >700M MAU require a separate grant). Supports function calling and tool integration.

Updated: 31 Jul 2026โ†— Security documentation