Meta AI Available Models: All Supported Models, Version Differences, Capabilities Comparison, And Access Requirements

Meta AI’s model ecosystem spans both consumer-facing AI assistants and a comprehensive lineup of Llama models for developers. Model selection, capabilities, and access pathways vary depending on the use case, with new releases focused on context length, modality support, and deployment flexibility.

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Meta AI Powers Consumer Apps And Developer Workflows With Llama Model Families.

Meta AI, as deployed in products like Facebook, Instagram, WhatsApp, Messenger, and the Meta AI app, is primarily built on Meta’s latest Llama 4 models. For developers and researchers, Meta maintains a distribution of Llama models—including Llama 4, Llama 3.3, Llama 3.2, Llama 3.1, and earlier generations—available for download and integration or through hosted APIs.

Llama 4 Scout and Llama 4 Maverick are the headline models, underpinning the consumer Meta AI experience. Llama 3.1, 3.2, and 3.3 continue to be broadly used and distributed across cloud platforms and open model repositories, with specific variants optimized for dialogue, vision, or long-context workloads.

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Meta AI Supported Model Families And Main Releases

Model Family	Key Models	Primary Use Cases
Llama 4	Scout, Maverick	Consumer Meta AI, long-context apps
Llama 3.3	70B, multilingual	Text chat, multilingual, dialogue
Llama 3.2	Vision-capable, small sizes	Multimodal, edge devices
Llama 3.1	8B, 70B, 405B	Long-context, enterprise
Llama 3	Original 3.x baseline	Broad compatibility

Meta AI’s infrastructure leverages the latest generation models for both app features and developer tools.

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Version Differences Emphasize Context Length, Modality, And Model Architecture.

Llama 4 introduces mixture-of-experts (MoE) architecture, supporting extremely large context windows and multimodal reasoning. Scout is positioned for ultra-long-context tasks, while Maverick delivers high performance and coding capability.

Llama 3.3 is focused on multilingual, instruction-tuned, text-only models, optimized for dialogue and strong context. Llama 3.2 is the first Llama family branch to support both vision and text, available in lighter model sizes for edge or mobile deployment. Llama 3.1 is known for its 128K context length, distributed in multiple dense model sizes.

Each release introduces specific improvements in reasoning, vision, context, or dialogue handling, allowing developers to select the most relevant model for their requirements.

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Meta AI Model Version Differences

Model	Key Feature	Typical Deployment
Llama 4 Scout	Ultra-long context, MoE	Deep analysis, enterprise
Llama 4 Maverick	High capability, coding	Consumer, developer APIs
Llama 3.3	Multilingual, dialogue	Text chat, global use
Llama 3.2	Vision, edge-optimized	Multimodal, IoT, mobile
Llama 3.1	Long-context, dense	Enterprise, research

Capabilities improve in line with model family and architectural evolution.

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Capabilities Comparison Covers Reasoning, Vision, Context, And Deployment Flexibility.

Meta AI’s current models can be compared along reasoning power, modality support (text vs. vision), context window size, and cost or latency tradeoffs.

Llama 4 models, particularly Scout and Maverick, offer state-of-the-art long-context reasoning and coding, with Scout focused on the most demanding analytical tasks. Llama 3.3 enables strong dialogue and multilingual support, while Llama 3.2 adds image processing for vision-enabled workflows. Llama 3.1 and earlier models deliver large context support in compact, deployable sizes.

Meta’s ecosystem supports both hosted API access for rapid deployment and open distribution for self-hosted and custom research solutions.

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Meta AI Model Capabilities Comparison

Capability	Llama 4	Llama 3.3	Llama 3.2	Llama 3.1
Reasoning/coding	Best	Strong	Moderate	Good
Multilingual	Yes	Best	Yes	Yes
Vision support	Yes	No	Yes	No
Context window	Ultra-long	Long	Moderate	Long
Model sizes	MoE, large	70B	Small, edge	8B–405B

Choice of model depends on required features, deployment, and application.

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Access Requirements Vary For Consumer And Developer Scenarios.

For consumers, Meta AI is available through in-app experiences on Facebook, Instagram, WhatsApp, Messenger, and the Meta AI standalone app, with regional rollouts and feature differences across countries. Access is usually automatic for eligible accounts, with ongoing expansion.

Developers can access Llama models in two ways: by downloading official weights and deploying models under Meta’s license, or via Meta’s hosted Llama API preview, which offers managed access to the latest model releases. Cloud platforms and open model hubs further broaden deployment options, often supporting a range of Llama model sizes and capabilities.

Open distribution requires agreement to Meta’s license and acceptable use policy. Hosted access typically involves API registration or onboarding through Meta or partner platforms.

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Meta AI Model Access Requirements

Access Path	Who Can Use	Models Offered	Conditions
Consumer Meta AI (apps)	Eligible users, regional rollout	Llama 4 (Scout/Maverick)	In-app entry, region supported
Downloadable weights	Developers, researchers	Llama 4, 3.x	License agreement
Hosted API	Developers, partners	Latest Llama releases	Preview, onboarding
Cloud marketplaces	Enterprise, devs	Multiple Llama versions	Cloud account, licensing

Access is shaped by user type, region, and compliance with Meta’s terms.

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Meta AI’s Model Ecosystem Supports Scalable Reasoning, Vision, And Multilingual Workflows.

Meta AI advances both consumer and developer access through frequent model releases, a diverse set of capabilities, and flexible access methods. Llama 4 powers state-of-the-art applications in both public and enterprise domains, while 3.x models continue to support a wide variety of use cases for research, development, and deployment.

The expanding ecosystem ensures that users can select the optimal model for chat, coding, analysis, vision, or multilingual needs, with deployment options ranging from managed APIs to self-hosted infrastructure.

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