Llama 4 Maverick: Meta’s Balanced Powerhouse in the New AI Landscape
Meta’s release of the Llama 4 suite marks a new chapter in open-weight AI, and while Scout grabs attention with its massive context window, it’s Llama 4 Maverick that stands out as the true all-rounder. Maverick is a generalist model, carefully tuned for strong performance across reasoning, coding, multimodal understanding, and long-context tasks. In many ways, it represents Meta’s most practical and versatile offering to date.
Designed for Balance and Quality
Maverick is built on the same mixture-of-experts (MoE) foundation as Scout, but scaled up for broader capabilities:
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Active Parameters: 17 billion
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Total Parameters: 400 billion
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Experts: 128 (with 2 active per token)
This design keeps inference costs manageable while giving Maverick the depth needed for advanced reasoning and nuanced responses. Unlike Behemoth, which exists mainly as a teacher model, or Scout, which specializes in long context, Maverick is crafted to deliver consistently high-quality results across all domains.
Smarter Post-Training
One of Maverick’s biggest differentiators is its post-training strategy. Meta avoided overloading the model with “easy” prompts and instead built a curriculum of harder reasoning, coding, and multimodal tasks. The training pipeline included:
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Lightweight supervised fine-tuning
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Online reinforcement learning
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Direct preference optimization (DPO)
On top of this, Maverick was co-distilled from Llama 4 Behemoth, Meta’s still-unreleased giant. This distillation helped Maverick inherit Behemoth’s reasoning strengths without requiring Behemoth-level compute for inference. The result is a model that feels sharper, more robust, and better at handling real-world prompts.
Benchmark Highlights
Maverick has quickly established itself as one of the strongest generalist open-weight models available. Some of its standout results include:
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Reasoning & Knowledge
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80.5 on MMLU Pro
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69.8 on GPQA Diamond
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Outperforming Gemini Flash and GPT-4o in both categories
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Coding
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43.4 on LiveCodeBench
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Higher than GPT-4o (32.3), Gemini Flash (34.5), and nearly on par with DeepSeek v3.1 (45.8)
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Multimodal Understanding
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90.0 on ChartQA and 94.4 on DocVQA
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Competitive with Scout, and ahead of GPT-4o (85.7, 92.8)
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Image Reasoning
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73.4 on MMMU and 73.7 on MathVista
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Outperforming GPT-4o (69.1 and 63.8) and Gemini 2.0 Flash
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Multilingual Performance
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84.6 on Multilingual MMLU
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Beating Gemini (81.5) and ideal for cross-language applications
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Long-Context Retention
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54.0/46.4 on the half-book test, 50.8/46.7 on the full-book MTOB
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Well above Gemini Flash’s 45.5/39.6
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These numbers reinforce Maverick’s identity as the best-rounded performer in Meta’s lineup—handling nearly every category at a high level rather than excelling in only one.
Why Maverick Matters
Maverick isn’t about chasing records in one specific area; it’s about being a workhorse model that can adapt to any environment. Here’s why it stands out:
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All-Around Strength – Excels in reasoning, coding, multimodal understanding, and multilingual processing.
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Distillation from Behemoth – Gains performance from Meta’s largest model without requiring massive infrastructure.
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Efficient Scaling – With its MoE design, it delivers power without runaway inference costs.
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Real-World Utility – Strong benchmarks across knowledge, language, and multimodal tasks make it suitable for enterprise adoption.
In short, Maverick is the model you’d pick if you want one AI system that can do it all reliably.
Potential Use Cases
Given its balanced performance, Maverick has broad applicability across industries:
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Business Intelligence: Summarizing and reasoning over large sets of reports and financial data.
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Software Engineering: Generating, reviewing, and debugging code across multiple languages.
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Multilingual Applications: Powering global chatbots, translation tools, and customer support.
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Creative Workflows: Combining text and images for marketing, design, and educational content.
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Scientific Research: Handling complex datasets, multilingual literature, and multimodal analysis.
Accessing Llama 4 Maverick
Like Scout, Maverick is available now under Meta’s open-weight license. Developers can download it through Meta’s official channels or Hugging Face, or interact with it on Meta platforms like WhatsApp, Messenger, Instagram, and Facebook.
And if you want to skip the setup, you can try it instantly—Llama 4 Maverick is available on our all-in-one AI platform UltraGPT. This makes it easy to test its reasoning, coding, and multimodal power without worrying about infrastructure.
Final Thoughts
If Scout is Meta’s experiment in pushing context length and Behemoth is its giant teacher model, Maverick is the sweet spot—a model designed for balance, reliability, and broad use cases. It doesn’t just excel in one dimension; it performs well across the board, making it a strong competitor to GPT-4o, Gemini 2.0 Flash, and DeepSeek V3.
For developers, researchers, and businesses looking for a versatile open-weight model, Maverick may be the most practical release Meta has offered so far. And with open availability, it’s a powerful tool anyone can start experimenting with today—especially through UltraGPT, where it’s already live and ready to use.