LLM📅 Released: 2026-04-01

Llama 4 Maverick

Llama 4 Maverick is Meta's latest frontier open-weight model.

Overview

Llama 4 Maverick is Meta's 2026 open-weight flagship, designed for the 'Agentic Era.' It features native vision and a new architecture optimized for autonomous planning and multi-step tool use, all while maintaining the open-source spirit that Meta is known for.

Unique Factor

The most capable open-weight model with integrated vision and agentic planning capabilities.

Key Capabilities

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Native vision

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Agentic optimization

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Open weight

Benchmarks

MMLU Score

90.2%

HumanEval (Coding)

91%

GPQA Diamond

85%

MATH Benchmark

86.5%

Top Use Cases

Autonomous Coding Agent

Building and deploying full-stack apps from a single natural language prompt.

Example: “Design a marketplace app for second-hand furniture. Write the backend in Go and the frontend in Next.js.”

Detailed Features

Native Vision Integration: Processes images and videos directly without a separate vision encoder.

Agentic Planner: A specialized neural sub-system for self-correction and multi-step goal completion.

256K Context Window: Doubled from Llama 3.1, enabling deeper document understanding.

Quantization-Native: Designed to maintain high performance even when compressed for consumer hardware.

15+ Language Proficiency: Expanded native support for Asian and Middle Eastern languages.

Llama Stack Compatibility: Seamlessly integrates with Meta's official agentic framework.

✓ Strengths & Pros

• Fully open weights for maximum control
• State-of-the-art vision and agency
• Large community ecosystem for LoRAs

✕ Limitations & Cons

• The 405B version requires industrial-scale hardware
• Training data is slightly more English-centric than GLM

Ideal Usage & Target Audience

Best For

AI researchers, open-source developers, and enterprises needing full data residency.

Not Recommended For

Individual developers without access to high-end cloud GPUs.

API Implementation

python

# Llama 4 Maverick inference
from llama_stack import LlamaAgent
agent = LlamaAgent(model='llama-4-maverick')
agent.run('Plan and execute a security audit of this directory.')

Check the official documentation for full SDK details.