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Llama 4

Latest Llama model family by Meta with Mixture-of-Experts architecture.

Open SourceSelf HostedOffline CapableGPU Required (16GB+ VRAM)
0.0 (0)

About

Llama 4 is Meta's open-weight model family built on a Mixture-of-Experts architecture, released in two downloadable variants: Scout, with 17 billion active parameters, 16 experts, and 109 billion total parameters, and Maverick, with 17 billion active parameters, 128 experts, and 400 billion total. Both models are natively multimodal, accepting text and image input, and Scout supports a context window of up to 10 million tokens, which suits long-document analysis and reasoning over large codebases. The family officially covers a dozen languages. Weights are distributed through Meta's site and Hugging Face under the Llama 4 Community License, which permits commercial use with restrictions for very large platforms. Developers typically run the models through inference stacks such as vLLM or Transformers, and they serve as open foundations for chat assistants, agents, and fine-tuned domain systems.

Should you use Llama 4?

Pick it when

Pick this when you need very long inputs, such as whole codebases or large document sets with Scout's window of up to 10M tokens, plus image input, and can host 109B or 400B total weights with only 17B active.

Look elsewhere when

Skip it on single consumer GPUs or if you need Apache terms: total weights start at 109B and the license restricts very large platforms. Gemma 4 or Qwen3.8-27B give multimodal Apache-2.0 models that fit consumer hardware.

Alternatives to Llama 4

  • Gemma 4

    Apache-2.0 multimodal at E2B to 31B with up to 256K context, far easier to host, though nowhere near Scout's 10M window.

  • MiniMax M3

    Native image and video plus 1M context and published SWE-Bench Pro results, at about 428B total under the MiniMax Community license.

  • Llama 3.1

    Dense 8B, 70B, and 405B text models with 128K context and tool calling, a widely used fine-tuning base, but without image input.

  • Qwen 2.5 / Qwen 3

    Apache-2.0 MoE at 30B-A3B and 235B-A22B with thinking modes and 100+ languages, text-only, without Meta's large-platform clause.

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