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Mixtral 8x22B

Mistral's largest open-weights MoE — 141B total / 39B active, Apache 2.0

0
freemiumDR 87Open weights under Apache 2.0 — free to download and self-host from Hugging Face (mistralai/Mixtral-8x22B-Instruct-v0.1). API access via Mistral La Plateforme: ~$2/M input tokens, ~$6/M output tokens at launch. Self-hosting requires multi-GPU infrastructure (~4× A100 80GB minimum).View full pricing →
💰 View Detailed Pricing →Try Mixtral 8x22B →

About Mixtral 8x22B

Mistral AI's largest open-weights mixture-of-experts model, released April 17, 2024. Mixtral 8x22B uses a sparse MoE architecture with 141B total parameters and ~39B active per token (8 groups of 22B, routing 2 experts per token). At launch it was the strongest open-weight model on reasoning, math, and coding benchmarks — outperforming LLaMA 3 70B and GPT-3.5 Turbo on most tasks. Supports 64k token context, natively multilingual (English, French, German, Italian, Spanish), with function calling and JSON mode. Weights released under Apache 2.0 on Hugging Face.

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Key Features

✓141B total parameters, ~39B active per token (8 expert groups of 22B, 2 routed per token)
✓64,536 token context window
✓Function calling and JSON mode support
✓Multilingual: English, French, German, Italian, Spanish
✓Apache 2.0 license — free for commercial use, modification, redistribution
✓State-of-the-art open-weight reasoning at launch — beats LLaMA 3 70B and GPT-3.5 on MATH, HumanEval, MMLU
✓Efficient inference: ~39B active params means faster throughput than a dense 141B model
✓Compatible with vLLM, llama.cpp, TGI, Ollama, and other inference frameworks

Mixtral 8x22B Pros & Cons

✅ Pros

  • +Best open-weight performance at launch — top of open-source leaderboards on reasoning, MATH, and coding
  • +MoE efficiency: 141B total params with only ~39B active means faster tokens-per-second than a comparably-performing dense model
  • +Apache 2.0 license — fully permissive, fine-tune and deploy without restrictions
  • +64k context window covers long-document tasks most 2024 open models couldn't handle
  • +Multilingual out-of-the-box across five European languages

⚠️ Cons

  • −Heavy self-hosting requirements: needs ~4× A100 80GB GPUs or equivalent for inference
  • −Superseded for most use cases by Mistral Small 4 and newer open-weight models
  • −No multimodal / vision support — text-only
  • −Commercial API has been deprecated in favor of newer Mistral models on La Plateforme

Who Is Mixtral 8x22B Best For?

👤Teams running self-hosted inference who need the best open-weight text model from the 2024 era
👤Researchers benchmarking or fine-tuning large MoE architectures
👤Multilingual applications across English, French, German, Italian, and Spanish
👤Cost-sensitive deployments where Apache 2.0 licensing and self-hosting beat API pricing

Tags

mistralmixtralllmapiopen-sourceopen-weightsmoemixture-of-expertsself-hostedmultilingualcodingreasoning
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