Meta Llama
Free (Open Weight)Open-weight LLM family from Meta — free to download, fine-tune, and self-host under a permissive community license
What is Meta Llama?
Meta Llama is the most widely used family of open-weight large language models in the world, with the Llama 3 and Llama 4 generations now forming the backbone of thousands of production AI apps, research projects, and fine-tuned derivatives. Unlike proprietary models from OpenAI, Anthropic, or Google, Llama weights are freely downloadable from Hugging Face and llama.meta.com under the Meta Llama Community License, which permits commercial use and modification as long as your product has fewer than 700 million monthly active users and you display a Built with Llama attribution. The current 2026 lineup spans Llama 3.1 (8B, 70B, 405B), Llama 3.2 (1B, 3B text, 11B and 90B vision), Llama 3.3 70B (the most cost-efficient frontier-class text model), and the Llama 4 family released in April 2025 — Scout (10M token context, 17B active / 109B total) and Maverick (17B active / 400B total) with native multimodal capabilities. Because you can download the weights, Llama supports deployment scenarios that closed-weight APIs simply cannot: fully offline inference, on-device mobile models, air-gapped enterprise environments, custom fine-tuning on proprietary data, and third-party inference providers like Groq, Together AI, and Fireworks that compete on speed and price. Llama is the de facto standard for any team that wants flagship-class LLM performance without vendor lock-in.
⚡ Quick Verdict
Developers and enterprises who want open-weight flagship LLMs with full deployment control and fine-tuning rights
Non-technical users who just want a ChatGPT-style chat interface without hosting
Free to download · Inference via Groq from $0.05/M tokens · Together AI from $0.18/M
Yes — weights are free, only pay for compute
Frontier-class performance with full weights ownership and zero vendor lock-in
No official Meta-hosted API — you must self-host or use a third-party provider
Bottom line: Llama scores 4.5/5 — the default open-weight choice for any team building serious AI products. Pick Llama 3.3 70B for cost-efficient production, Llama 4 Scout for long context, Llama 3.2 1B/3B for on-device.
Pricing
Model weights — Free: Download any Llama model from llama.meta.com or Hugging Face under the Meta Llama Community License. Commercial use permitted for products under 700M monthly active users.
Inference providers (typical 2026 pricing for Llama 3.3 70B): Groq from $0.59 input / $0.79 output per million tokens · Together AI around $0.88/M blended · Fireworks from $0.90/M · AWS Bedrock $0.72 input / $0.72 output · Anyscale and Novita from $0.02/M for smaller 8B models.
Self-hosting — Hardware only: Llama 3.1 8B runs on a single consumer GPU (RTX 4090). Llama 3.3 70B needs roughly 4x A100 80GB or equivalent. Llama 3.1 405B requires H100 clusters. Tools like vLLM, TGI, and Ollama make deployment straightforward.
Key Features
- Open model weights under the Meta Llama Community License
- Llama 3.1 (8B, 70B, 405B), 3.2 (1B, 3B, 11B-V, 90B-V), 3.3 70B, Llama 4 Scout and Maverick
- Llama 4 Scout supports up to 10M token context windows
- Native multimodal vision models in Llama 3.2 and Llama 4
- Fine-tune with LoRA, QLoRA, or full parameter tuning on your own data
- Inference ready on Groq, Together AI, Fireworks, OpenRouter, AWS, Azure, GCP
- Official support in Hugging Face Transformers, vLLM, Ollama, LM Studio, llama.cpp
- Official 12-language multilingual support in Llama 4
Pros & Cons
Pros
- Free commercial use for the vast majority of businesses
- Frontier-class performance competitive with GPT-4o and Claude Sonnet
- Massive ecosystem — every inference provider serves Llama
- Full fine-tuning and deployment control — no vendor lock-in
- Llama 4 Scout 10M context is unmatched in open models
Cons
- No official Meta-hosted API — deployment is your responsibility
- Large models (405B, Maverick) need expensive GPU infrastructure
- 700M MAU restriction excludes hyperscale competitors
FAQ
Is Meta Llama really free for commercial use?
Yes, for virtually everyone. Llama 3 and Llama 4 are released under the Meta Llama Community License, which allows free commercial use, modification, and redistribution. The one caveat is a 700 million monthly active user threshold — if your product has more than 700M MAUs (think Google, Microsoft, ByteDance scale), you must negotiate a separate license with Meta. For startups, SMBs, and even most large enterprises, this restriction is purely theoretical and the models are effectively free.
Which Llama model should I use in 2026?
For most production use cases, Llama 3.3 70B hits the best balance of capability and cost. Llama 4 Maverick and Scout (released April 2025) are the flagship multimodal models and compete with GPT-4o and Claude Sonnet on reasoning benchmarks. Llama 3.2 1B and 3B are ideal for on-device inference and edge deployment. If you need a long-context model for document analysis, Llama 4 Scout supports up to 10M token context — the longest of any open model.
Where can I run Llama models for production?
You have three main options. First, API inference providers like Groq, Together AI, Fireworks, and OpenRouter serve Llama at $0.05-$0.90 per million tokens depending on size. Second, self-host on your own GPUs using vLLM, Ollama, or LM Studio — free beyond hardware costs. Third, managed cloud deployments on AWS Bedrock, Azure AI Studio, or Google Vertex AI offer enterprise-grade SLAs with standard cloud pricing.
How does Llama 4 compare to GPT-4 and Claude?
Llama 4 Maverick matches GPT-4o and Claude 3.5 Sonnet on most reasoning benchmarks (MMLU, HumanEval, GPQA) while costing roughly 10x less via inference APIs. Where GPT-4 and Claude still lead: complex multi-step agent tasks, very long-form writing coherence, and tool use. Where Llama wins: cost, deployment flexibility, and the ability to fine-tune on your own data without paying per token.
Do I need to say Built with Llama?
Yes. The Meta Llama Community License requires a prominent Built with Llama attribution in your product or on a related website. You must also include a copy of the license with any distribution. For most apps, adding a small footnote in your about page or API documentation satisfies this requirement. Meta provides official Built with Llama badges you can download from llama.meta.com.
Can I fine-tune Llama on my own data?
Absolutely — this is one of Llama's biggest advantages over closed models. You can fine-tune any Llama model with LoRA, QLoRA, or full parameter tuning using Hugging Face TRL, Axolotl, Unsloth, or Torchtune. Managed fine-tuning is available on Together AI, Fireworks, and AWS Bedrock. The resulting fine-tuned weights belong to you, are subject to the same community license, and can be self-hosted without paying Meta anything.
Is Llama good for non-English languages?
Llama 4 significantly improved multilingual performance and officially supports 12 languages including Spanish, French, German, Italian, Portuguese, Hindi, Thai, Vietnamese, Arabic, and Indonesian. For languages outside that list, Llama still works but performance varies. For dedicated multilingual use, Mistral Large 2, Qwen, and Cohere Aya are often stronger in specific regions like Chinese, Japanese, and Korean.
📋 Good to know
Download weights from llama.meta.com or Hugging Face, accept the license, and deploy via Ollama, vLLM, or any inference provider.
Fully controllable — self-host for air-gapped privacy, or pick any inference provider's data policy you trust.
Move from Llama 3.1 8B to 3.3 70B when you need frontier reasoning. Upgrade to Llama 4 Scout for 10M+ token contexts.
Low via API providers, moderate for self-hosting. Ollama and LM Studio get you started in 5 minutes.