Meta AI — All Models Available for Consumers and Developers in 2025

Meta’s AI lineup spans the consumer assistant embedded in WhatsApp, Instagram, Messenger, and the web, and a broad developer catalog of Llama models released as open-weight checkpoints. In 2025, that catalog includes the newer Llama 4 family alongside Llama 3.3, 3.2, and 3.1, with sizes and modalities tailored for phones, PCs, and cloud backends. Below is the current landscape and where each model fits.

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What powers the consumer “Meta AI” assistant today.

Meta AI (the chatbot inside WhatsApp/Instagram/Messenger and on the web) is built on the Llama family and has been upgraded through the year. The assistant launched on Llama 3, expanded with Llama 3.1, and has since adopted Llama 4 variants across surfaces.

What this means in practice:

• Your chats in Meta AI are handled by recent Llama generations (region and rollout may vary), with Llama 4 integration announced across Meta’s platforms.

• Expect better long-context reasoning, faster responses, and stronger multilingual support than the early Llama 3 builds.

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Llama 4 series: the 2025 flagship family.

Meta introduced Llama 4 with multiple variants aimed at efficiency and scale. Public reporting highlights Scout and Maverick, with additional models under development. Key points:

Model	Architecture	Notable traits	Where you’ll see it
Llama 4 Scout	Mixture-of-Experts (MoE)	Compact; reported very long context and strong efficiency; designed to run on modest GPU footprints	Meta AI assistant surfaces; developer releases discussed publicly
Llama 4 Maverick	MoE	Larger, higher-accuracy reasoning/coding; multimodal inputs (text+image)	Assistant surfaces and research/benchmarks
(In development) Llama 4 Behemoth / Reasoning	MoE	Heavier expert capacity; research-class scale	Research notes / future rollouts

Meta communicated that Llama 4 models are integrated into Meta AI across WhatsApp, Messenger, Instagram, and the web, with MoE architecture for better cost/performance at scale.

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Llama 3.3, 3.2, 3.1: the open-weight backbone for builders.

While Llama 4 headlines 2025, the 3.x line remains widely deployed in apps, on clouds, and locally.

• Llama 3.3 (70B, text-only instruct). A refinement tier commonly distributed on model hubs for strong chat performance without vision.

• Llama 3.2 (vision + edge + tiny text).

  • Vision LLMs: 11B and 90B (multimodal).

  • Tiny text-only: 1B and 3B for mobile/edge.

• Llama 3.1 (8B, 70B, 405B). A major step-up in 2024 with an openly available 405B parameter model for research/hosting, plus 70B/8B for broad deployment. Available via Meta, Hugging Face, and clouds like AWS Bedrock.

Developer access points

• Meta / Hugging Face model cards for downloads and licenses.

• Cloud marketplaces (e.g., Amazon Bedrock) for managed inference with 3.1 sizes.

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Quick catalog: sizes, modality, and typical use cases.

Family	Sizes (illustrative)	Modality	Typical strengths	Good for
Llama 4 Scout	~17B active (MoE)	Text, Image-to-Text	Long context, efficiency	Assistants at scale, mobile/edge-friendly backends
Llama 4 Maverick	larger MoE (active experts)	Text, Image-to-Text	Reasoning, coding, multilingual	Consumer assistant, coding help, research
Llama 3.3	70B	Text	Polished chat quality	On-prem or cloud chat, RAG
Llama 3.2 Vision	11B, 90B	Vision + Text	Charts/figures/screenshots + language	Product QA, doc understanding
Llama 3.2 Tiny	1B, 3B	Text	Footprint-first, on-device	Mobile apps, quick replies
Llama 3.1	8B, 70B, 405B	Text (multilingual)	High accuracy (405B), broad compatibility	Cloud inference, fine-tune bases

Sources: Meta blogs and releases for 3.1 and 3.2, and public reporting on Llama 4 launches and integration into Meta AI.

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Which models a regular user actually touches.

If you’re chatting in WhatsApp/Instagram/Messenger or on meta.ai, you’re using the assistant tier backed by recent Llama generations: it launched on Llama 3, moved through 3.1, and now advertises Llama 4 integration. You don’t pick the exact checkpoint; Meta routes traffic to models that fit capacity, latency, and feature needs.

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Which models developers can run or fine-tune.

Builders generally choose from Llama 3.1/3.2/3.3 today because they’re broadly available as open weights and supported across tooling (HF Transformers, vLLM, TGI, Ollama). Llama 4 availability to self-host varies by license and rollout; watch Meta’s official channels for model cards and terms.

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Licensing, hosting, and where to find them.

• Licenses: Llama models are released under Meta’s community licenses (open-weight, with acceptable-use restrictions). They’re not OSI-open-source, but they are broadly usable for commercial apps within license terms.

• Hosting: Run locally (GPU/CPU), on managed endpoints (AWS Bedrock, others), or via partner APIs. 3.1 sizes (8B/70B/405B) are widely hosted; vision 3.2 and 3.3 70B are available on hubs.

• Assistant integration: Meta states its consumer assistant is built with Llama and is rolling out globally across apps.

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What’s likely next in Meta’s lineup.

Public reporting points to continued Llama 4 variants and scaling efforts, including large-expert “Behemoth/Reasoning” tiers and deeper integration into the Meta AI assistant across all surfaces. Keep an eye on Meta’s AI blog and model cards for new checkpoints, sizes, and license updates.

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