Meta Llama 4 Maverick
Last Updated on: Sep 12, 2025
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What is Meta Llama 4 Maverick?
Llama 4 Maverick is Meta’s powerful mid-sized model in the Llama 4 series, released April 5, 2025. Built with a mixture-of-experts (MoE) architecture featuring 17 B active parameters (out of 400 B total) and 128 experts, it supports a 1 million-token context window and native multimodality for text and image inputs. It ranks near the top of competitive benchmarks—surpassing GPT‑4o and Gemini 2.0 Flash in reasoning, coding, and visual tasks.
Who can use Meta Llama 4 Maverick & how?
- Developers & Engineers: Build high-performance multimodal applications—code assistants, visual Q&A, document agents.
- Analysts & Researchers: Analyze long documents, charts, or code with deep contextual understanding.
- Enterprises & SMEs: Deploy open-weight AI with flagship-level capabilities for varied tasks.
- Content Creators & Designers: Generate images, captions, and text-based outputs in one pipeline.
- Multilingual Applications: Ideal for global deployments with support across multiple languages.
How to Use Llama 4 Maverick?
- Access via Platforms: Available on Hugging Face, Meta’s Llama.com, Databricks, AWS/Azure/GCP, and other enterprise providers.
- Send Mixed Inputs: Submit text and image prompts together (up to 1 million tokens context) via supported APIs.
- Optimizer Support: Includes fp8/BF16 and quantization for efficient deployment on H100 hardware.
- Deploy Locally or in Cloud: Runs on H100 DGX systems, available in enterprise cloud environments.
- Manage Cost: Approximately $0.19–$0.49 per million tokens, offering high performance at a lower price than closed-source alternatives.
What's so unique or special about Meta Llama 4 Maverick?
- Native Multimodality: Processes text and images jointly using early-fusion training.
- Ultra-Long Context: 1 million-token window supports massive documents and complex workflows.
- Efficient MoE Design: Activates only a subset of experts per token, delivering cost-effective inference.
- Open-Weight & Flexible: Released under community license with cloud and local deployment support.
Things We Like
- Top-tier multimodal reasoning, coding, and visual understanding
- Massive 1 M-token context window ideal for large tasks
- Cost-effective token pricing compared to closed models
- Supports cloud and on-premise deployment with efficiency
- Open-weight availability fosters transparency and flexibility
Things We Don't Like
- Released "experimental" benchmark variant not publicly available—raised transparency concerns
- Mixed user reviews—some underperformance in code and general chat vs legacy models
- MoE complexity may complicate deployment and tuning
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FAQs
A 17 B active‑parameter, MoE-powered multimodal model with 1 M-token context, offering top-tier reasoning, coding, and image understanding at open weight.
Scores 80.5% MMLU Pro, 69.8% GPQA Diamond, 90.0 ChartQA, 94.4 DocVQA, 73.7 MathVista—outperforming GPT‑4o and Gemini 2.0 Flash on benchmarks.
Supports up to 1 million tokens for massive document, code, or multimodal inputs.
Accessible via Hugging Face, Databricks, cloud providers (AWS/Azure/GCP), runable locally on H100 DGX systems.
Yes—open-weight under community license, with usage restrictions for large commercial entities.
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