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Mixtral 8x7B Instruct v0.1

Mistral AI's sparse mixture-of-experts model with 8x7B parameters, instruction-tuned for multilingual dialogue, code generation, and complex reasoning tasks.

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about

Mixtral 8x7B Instruct, licensed under Apache 2.0, is a powerful language model with a large context window. It's great at simulated dialogues and general language understanding, making it perfect for customer service chatbots and interactive storytelling. However, it might struggle with more specialized tasks.

Licenseapache-2.0
Context window(in thousands)32768

Use cases for Mixtral 8x7B Instruct v0.1

  1. Cost-efficient multilingual generation: With 46.7B total parameters but only 12.9B active per token, it matches Llama 2 70B quality across benchmarks while using roughly 5x less compute per inference.
  2. High-quality open-source chat: Scoring 8.30 on MT-Bench and 1121 on Arena ELO at release, it outperformed Claude 2.1 and GPT-3.5 Turbo on human preference evaluations.
  3. Long-context dialogue and analysis: The 32K context window with sparse expert routing enables extended multi-turn conversations and document analysis without the memory overhead of dense 70B models.

Quality

Arena Elo1114
MMLU70.6
MT Bench8.3

Mixtral 8x7B Instruct scores 70.6% on MMLU and 8.30 on MT-Bench, surpassing GPT-3.5 Turbo (70.0% MMLU, 7.94 MT-Bench) on both measures on the same sheet. With only 12.9B of its 46.7B parameters active per token, it achieves this quality at roughly one-fifth the compute cost of a dense 70B model. Its Arena ELO of 1,114 places it above GPT-3.5 Turbo (1,105) as well.

Claude-Sonnet-4-20250514

1138

GPT-3.5 Turbo-0613

1117

Mixtral 8x7B Instruct v0.1

1114

GPT-3.5 Turbo-0125

1106

GPT-3.5 Turbo

1105

pricing

The cost per 1,000 tokens for running the model with Telnyx Inference is $0.0003. For instance, analyzing 1,000,000 customer chats, assuming each chat is 1,000 tokens long, would cost $300.

What's Twitter saying?

  • Developers praise Mixtral 8x7B Instruct for outperforming Llama 2 70B and matching GPT-3.5 in benchmarks like math, code, and MT-Bench, with 6x faster inference.
  • Tech commentators highlight its Sparse Mixture-of-Experts efficiency, activating only 13B of 47B parameters for low VRAM use and strong multilingual performance.
  • Community users on forums note quantized versions (e.g., 3-bit) run well on consumer hardware like M-series Macs, though 2-bit needs improvements.

Explore Our LLM Library

Discover the power and diversity of large language models available with Telnyx. Explore the options below to find the perfect model for your project.

Organizationdeepseek-ai
Model NameDeepSeek-R1-Distill-Qwen-14B
Taskstext generation
Languages SupportedEnglish
Context Length43,000
Parameters14.8B
Model Tiermedium
Licensedeepseek

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faqs

What is Mixtral 8x7B Instruct?

Mixtral 8x7B Instruct is Mistral AI's sparse mixture-of-experts model with 56 billion total parameters, activating only 2 of 8 experts per token for efficient inference. It is instruction-tuned via SFT and DPO for multilingual dialogue and code generation with a 32K context window.

What is the difference between Mixtral 8x7B and Mistral 7B?

Mixtral 8x7B uses a mixture-of-experts architecture that routes each token to 2 of 8 expert networks, while Mistral 7B is a single dense model. Mixtral outperforms Llama 2 70B on most benchmarks with 6x faster inference while only activating about 13B parameters per token.

How good is Mixtral 8x7B?

Mixtral 8x7B scores 8.30 on MT-Bench, making it comparable to GPT-3.5 Turbo at the time of release. It handles English, French, Italian, German, and Spanish, with particularly strong code generation capabilities.

Are Mistral and Mixtral the same?

Mistral and Mixtral are both from Mistral AI but use different architectures. Mistral 7B is a dense model (all parameters active), while Mixtral 8x7B is a sparse mixture-of-experts model that selectively activates subsets of its parameters for each token.

Does Mixtral 8x7B support tool calling?

The base Mixtral 8x7B Instruct v0.1 has limited native tool calling support. For stronger function calling, the Nous Hermes 2 Mixtral variant or Hermes 2 Pro models offer specialized training for structured output and tool use.

Mixtral 8x7B Instruct—High quality meets performance