Meta AI All Models Available: Llama‑4, Llama‑3, and Deployment Options
- Graziano Stefanelli
- 4 hours ago
- 3 min read
Meta AI now offers one of the broadest and most versatile model lineups in the LLM landscape, spanning the Llama‑4 flagship family, the open-weight Llama‑3 series, and tailored deployment paths for both end users and developers.
Understanding which Meta models are available, their platform scope, and how each can be used is critical for choosing the right AI for conversational, research, or enterprise applications in late 2025/2026.
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Llama‑4 is Meta’s current flagship, powering multimodal assistants on web, mobile, and social platforms.
Meta launched Llama‑4 in spring 2025, introducing high‑performance variants such as Llama 4 Scout (lightweight, fast) and Llama 4 Maverick (large, expert count).
Llama‑4 brings native support for text and image input, improved context handling, and robust reasoning — becoming the core of the Meta AI assistant embedded in Facebook, WhatsApp, Instagram, Messenger, and meta.ai web.
This multimodal capability enables workflows like reading documents, understanding images, summarizing conversations, and answering visual questions directly in chat.
Llama‑4 is not distributed as an open model but is available to users through Meta’s assistant interface and, increasingly, as an API through managed cloud partners.
···············Llama‑4 Overview
Model Variant | Key Features | Primary Use |
Llama 4 Scout | Fast, efficient, for chat | Consumer assistants |
Llama 4 Maverick | Larger, high-expert | Research, advanced reasoning |
Llama 4 API | Cloud-hosted, managed | Enterprise, partner integrations |
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Llama‑3 series remains available for developers, self‑hosting, and open-source research.
The Llama‑3 family continues to be widely adopted as Meta’s open-weight, community‑driven suite of LLMs.
Versions such as Llama 3.1, 3.2, and 3.3Â are available in sizes from 8BÂ to 70BÂ parameters, balancing output quality with resource efficiency.
These models run natively on consumer hardware, cloud servers, and AI research clusters — providing a foundation for prototyping, custom apps, academic work, and on-premises deployments.
Llama‑3 models are licensed with permissive terms and can be found on major model hubs such as Hugging Face and partner platforms.
They are used extensively for cost‑effective chatbots, data analysis, language tasks, multilingual apps, and experimentation.
···············Llama‑3 Family: Sizes and Use Cases
Model | Parameters | Best Use |
Llama 3.1 | 8B, 70B | Custom chat, research |
Llama 3.2 | 8B, 70B | Enhanced reasoning, RAG |
Llama 3.3 | 8B, 70B | Community fine‑tuned apps |
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Model selection depends on use case: consumer assistants, cloud APIs, or local developer deployment.
Meta AI’s own assistant (web and mobile) always uses the latest Llama‑4 variant, with users unable to select the model directly.
For developers, Llama‑3 models can be downloaded, self‑hosted, or deployed in custom environments with full access to weights and documentation.
Enterprise customers and large‑scale projects access Llama‑4 through API partners or managed cloud providers (e.g., AWS Bedrock), which handle scaling, compliance, and service levels.
This tiered approach enables Meta to deliver both best‑in‑class user experiences and a stable foundation for open research and innovation.
···············Meta AI Model Access Table
User Type | Available Model(s) | How to Access |
Consumer (Meta AI assistant) | Llama‑4 | Web, mobile, social apps |
Developer (open source) | Llama‑3 (all variants) | Hugging Face, GitHub, partner clouds |
Enterprise (managed API) | Llama‑4 (API) | AWS Bedrock, cloud partners |
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Strengths, limitations, and deployment considerations for Meta’s models.
Llama‑4 excels at multimodal reasoning, image‑text fusion, and broad context handling, but requires cloud infrastructure and is not available for self‑hosting.
Llama‑3 offers the best mix of transparency, customizability, and ease of deployment — with lower hardware requirements and strong community support — but lacks full multimodal capabilities.
Cloud APIs for Llama‑4 provide managed scaling, security, and compliance, but may incur higher costs or require vendor integration.
The choice depends on project needs: rapid prototyping, academic research, end‑user assistants, or enterprise AI integration.
···············Strengths and Trade-Offs Table
Model | Main Strengths | Limitations |
Llama‑4 | Multimodal, high performance, latest features | Not open, cloud-only |
Llama‑3 | Open-weight, self-hostable, flexible | No native multimodality |
Llama‑4 API | Enterprise features, managed | API cost, vendor lock-in |
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Meta’s roadmap prioritizes multimodality, open research, and broad platform access.
Meta’s Llama‑4 release reaffirms its focus on multimodal intelligence, bringing powerful vision and language reasoning to billions of users via embedded assistants.
At the same time, Meta maintains Llama‑3 as a stable, community-accessible platform for open AI research, custom product development, and educational use.
The coexistence of Llama‑4 and Llama‑3 ensures both high-quality consumer experiences and freedom for researchers, startups, and organizations to innovate with transparent models.
With ongoing investment in cloud APIs, partner integrations, and open-source collaboration, Meta’s model family remains central to the evolving AI landscape of 2025/2026.
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