Llama

Open LLMs with 10M context and native text+image understanding

Free Technical setup Web · API

platform research #open-weight-llm#multimodal-models#meta-ai

About

Download open models or call the API to handle long documents and images in one prompt. Developers use Llama 4 for multimodal reasoning and Llama 3.3 (70B) for multilingual text tasks across apps and services. Standouts include a 10M‑token context (Maverick/Scout) and safety tooling via Llama Protections.

Editor's Take

We recommend Llama for teams that need multimodal reasoning or very long context windows and are prepared to handle deployment and tuning; expect engineering time to optimize latency and user‑preference handling.

Key Features

Send a prompt with an image → get joint reasoning from native text+image understanding (Llama 4)
Load millions of tokens across long documents → ask questions using the 10M‑token context window (Maverick/Scout)
Deploy on a single H100 GPU → run a 10M‑context model efficiently for long‑document analysis (Scout)
Pick the 70B Llama 3.3 model → run multilingual text tasks with open weights you can fine‑tune
Enable Llama Protections → apply safety guidelines and defenses during generation

Use Cases

An ML engineer building a document QA tool over 5,000‑page manuals using the 10M‑token context
A support automation team routing image‑ and text‑based tickets by prompting a multimodal model
A data platform team fine‑tuning Llama 3.3 (70B) for multilingual synthetic data generation

Try It Like This

1
Multimodal ticket triage
Developer integrates Llama 4 via API → send incoming support text + attached image in one prompt and request a category and confidence score → route ticket based on model label and confidence, fallback to human if below threshold.
2
Document QA over 5,000‑page manuals
Engineer uploads chunked manual data into a vector store or uses the 10M‑token context (Maverick/Scout) → send user question with pointers to relevant chunks or include large context window → return concise answers with citations and source offsets for verification.
3
Deploy multilingual text generator
Dev chooses Llama 3.3 (70B) with open weights and prepares training data for target languages → fine‑tune locally or on a cloud instance, validate outputs on held‑out prompts → serve via the platform API and monitor latency and token usage.
4
Run long‑document analysis on one GPU
Engineer provisions a single H100 instance and installs the Scout/Maverick runtime → load the 10M‑token model and stream documents into the model's context → run summarization, chunked reasoning, or cross‑document search in one prompt to get consolidated answers.
5
Prototype multimodal search in an app
Developer requests an API key and calls the REST endpoint with image+text prompt using Llama 4 → parse structured JSON response (labels, bounding boxes, text understanding) and index results in the app → iterate on prompt templates and safety filters before wider rollout.

Pros & Cons

Pros

Supports native multimodal prompts with joint text+image reasoning via Llama 4, so a single API call can handle images and text together.
Offers very large context models (Maverick/Scout) with a 10M‑token window for long‑document QA and cross‑document reasoning.
Provides open weights for Llama 3.3 (70B) enabling fine‑tuning and local deployment, and can run a 10M‑context model on a single H100 GPU.

Cons

Users report slow responses in some cases, which may affect latency‑sensitive applications.
Users note limited memory of user preferences and that implementing specific end‑to‑end use cases can require significant engineering effort.

Getting Started

1 Visit llama.com/llama-downloads to download models or join the Llama API waitlist
2 Choose Llama 4 Maverick/Scout or Llama 3.3 and load it via the Meta GitHub or Hugging Face repo
3 Run a sample text or text+image prompt and verify the first response locally

Pricing

Access method	Price	Notes
Direct download	$0	Hugging Face, Meta AI, Llama site; license required for commercial use over 700M MAU
Llama API (Meta)	Pay-as-you-go	Hosted access for Llama 4 Scout/Maverick and Llama 3.3
Cloud providers	Variable per provider	Available via AWS Bedrock, Azure, Google Vertex, Replicate, Together AI, Groq, Fireworks