Meta AI All Models Available: assistant, open weights, and enterprise access

Meta AI is powered by the latest generation of Llama models while also offering open-weight versions for developers and managed access through partner clouds. In 2025, Meta updated its consumer assistant to Llama 4, released multiple Llama 4 variants as open weights, and continued to support the Llama 3.2 and Llama 3.1 families. These models differ in scale, modality, and context capacity, but together they define the complete model catalog available for Meta AI across consumer, developer, and enterprise settings.

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How the Meta AI assistant is powered.

The Meta AI assistant embedded in WhatsApp, Instagram, Facebook, Messenger, and the standalone Meta AI app runs on Llama 4. This deployment introduced native multimodality and improved reasoning into the consumer experience. Within the app, Meta also offers generative media features such as image and video generation, as well as the “Vibes” creative feed, which use Meta’s own pipelines supplemented by partner models.

For users, this means that everyday interactions with Meta AI are backed by Llama 4’s capabilities, while creative media features may rely on specialized generative systems alongside the core model.

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The Llama 4 family of models.

Meta’s 2025 release of Llama 4 introduced new architectures and open-weight checkpoints for developers. Two models are publicly available:

• Llama 4 Scout: an efficient mixture-of-experts model optimized for long context windows and balanced inference cost. Scout is positioned as a lightweight but capable multimodal model.

• Llama 4 Maverick: a higher-capacity mixture-of-experts model designed for reasoning, multimodal tasks, and very large contexts. It is positioned for use in managed clouds and enterprise-scale applications.

A third model, Llama 4 Behemoth, has been previewed but not yet released. It is expected to serve as a frontier research model once made available.

Llama 4 models are natively multimodal, processing both text and vision, and are designed for extremely large context windows. Reported limits range from one million tokens for Maverick up to ten million for Scout in some vendor implementations, depending on infrastructure.

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The Llama 3.2 lineup.

In 2024, Meta expanded its portfolio with Llama 3.2, which added vision capabilities and lightweight text models:

• Llama 3.2 Vision 11B and 90B: multimodal models capable of interpreting images, diagrams, and documents alongside text.

• Llama 3.2 1B and 3B: small-scale text models designed for edge devices and mobile deployment, offering efficient inference with limited hardware.

These models remain available as open weights, providing options for vision-enabled research or resource-constrained deployments.

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The Llama 3.1 family.

Meta’s Llama 3.1 release in 2024 included large-scale text models with a broad range of sizes:

• Llama 3.1 405B: a large open-weight text model released for research and enterprise inference.

• Llama 3.1 70B and 8B: text models widely available in managed cloud environments and suitable for fine-tuning or hosted inference.

The 405B variant is the largest Llama model made publicly available to date, positioned for high-end server inference.

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Table — Meta AI model catalog (2025 snapshot).

Family	Model	Modality	Scale	Positioning
Llama 4	Scout	Text + Vision	Mixture-of-Experts (active ~17B)	Efficient long context; scalable and cost-effective
Llama 4	Maverick	Text + Vision	Mixture-of-Experts (active ~17B/128 experts)	Higher-end reasoning; very large context capacity
Llama 4	Behemoth (preview)	Text + Vision	Not released	Frontier model; release pending
Llama 3.2	Vision 11B/90B	Text + Vision	11B / 90B	Image and document interpretation
Llama 3.2	1B/3B	Text	1B / 3B	Edge and mobile scenarios
Llama 3.1	405B	Text	405B	Large open-weight model for server inference
Llama 3.1	70B / 8B	Text	70B / 8B	Mid-scale text models, widely deployed in clouds

This table consolidates the current lineup of models released or supported by Meta for developers and enterprises.

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How the models are accessed.

Meta’s models are distributed through several access points:

• Meta AI consumer assistant: runs Llama 4 in apps and the standalone Meta AI interface, supplemented by generative pipelines.

• Open-weight downloads: available from model hubs such as Llama.com and Hugging Face for Scout, Maverick, and previous families.

• Cloud partners: Llama models are deployed on platforms including IBM watsonx, Oracle Cloud, and SambaNova, providing managed inference and enterprise-grade support.

• On-device integrations: Meta AI features in Ray-Ban Meta glasses rely on custom Llama variants tailored for hardware constraints.

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Operational recommendations.

For individual developers, Llama 4 Scout offers a balance between efficiency and long context capabilities, while Llama 3.2 Vision is appropriate for multimodal research. For enterprise workloads, Llama 4 Maverick deployed through partner clouds provides the scale and governance needed for production. Resource-constrained applications can adopt Llama 3.2 1B and 3B models on device.

Organizations considering Meta AI as a platform should distinguish between the assistant experience powered by Llama 4 and the open-weight ecosystem that enables fine-tuning and deployment in custom workflows. By combining these layers, Meta has created a model family that spans consumer, developer, and enterprise needs, ensuring flexibility in how its AI is accessed and deployed.

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