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Model · Meta · 2026

Llama 4

Llama 4 is Meta's family of open-weight models: you can download the weights for free and run the model on your own servers. You reach for it when you want to keep your data in-house, process volume without a per-request bill, or fine-tune it on your own documents, with a context window of up to 10 million tokens.

Model 5 min read Updated 2026-05-25
— What it is

Llama 4, in plain words

In plain words: Meta puts the model up for free download. Anyone can grab it, run it on their own machines, and tweak it.

The Llama 4 family, released in 2025-2026, comes in several sizes: Scout (small, fast), Maverick (medium, all-purpose), Behemoth (big, reasoning). The technical standout: a 10-million-token context window, something you never see in a consumer product.

Watch out for the word "open": the weights are free, but the Meta license isn't fully open. Past 700 million active users, you need a commercial license. That applies to 0.01% of companies, but it's worth knowing.

What it's good for

  • Sensitive data: hosting Llama 4 on my own servers means my data never leaves for OpenAI or Anthropic. Lawyer, doctor, accountant — that's a real selling point.
  • Volume without a bill: once the machine is paid for (~€5 to €50/month in the cloud), I can run thousands of requests a day with no surprises.
  • Fine-tune on my data: fine-tune Llama 4 on my own documents so it knows my field inside and out. You can't do that with GPT or Claude.
  • Giant context: 10M tokens means I can hand it an entire library in one go.
  • Learn how it works: study the weights of a real model and understand what it's doing under the hood.

How it stacks up

vs Claude Sonnet 4.6 / GPT-5.5: Llama 4 is a notch below on raw intelligence, but the weights are free and you can host it at home. Pick Llama when sovereignty or volume comes first, Claude/GPT when absolute quality comes first.

vs DeepSeek V3.2: DeepSeek is also open-weight and often stronger on benchmarks. Llama's edge is a more mature ecosystem (Hugging Face, llama.cpp) and a clearer license for commercial use.

vs Mistral Large 3: Mistral is under the Apache 2.0 license (truly open), Llama under the Meta license (open up to 700M users). Mistral is purer, Llama more capable.

What it costs

Pricing as of May 25, 2026:

  • Model weights: free on huggingface.co/meta-llama
  • Hosted with a provider (Groq, Together, Fireworks, Lambda): Llama 4 Maverick at $0.19-0.49 / million tokens for input/output combined
  • Self-hosting: an H100 GPU runs ~€2-3/hour in the cloud, more cost-effective than the APIs once you pass ~1M tokens/day

My take

I'm not a dev, so I don't run Llama 4 on my own servers. But I do use it through Groq when I want ultra-fast answers at 5× less than Claude.

What I love: the 10M context that leaves everyone else in the dust, the open-source ecosystem around it, and the peace of mind of knowing I can always switch to local if Meta changes the license.

What bugs me: the quality in French still trails Sonnet or GPT-5.5, especially for nuances of tone or long-form writing.

Quick questions

Is Llama 4 really open-source?

Llama 4 is Meta's family of open-weight models: weights you can download for free, host on your own servers, with a context window of up to 10 million tokens. Scout, Maverick and Behemoth sizes, under the Meta license.

How do I try it without installing anything?

Through Groq, Together AI, or meta.ai inside Messenger/WhatsApp.

Does Llama 4 read images?

Yes, it's multimodal on most of the sizes.

Checked on 2026-05-25 · next review 2026-11-25

Prices checked on Together AI and Groq. Personal use: occasional tests through Groq for the speed, not my main model.

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