TL;DR

• Mixtral 8x22B is a larger-scale MoE model (more total parameters, smaller fraction of active parameters), targeting better capability-to-cost ratio.
• Key selling points: long context window, multilingual, math reasoning, code generation, and native function calling / constrained outputs.
• Production advice: validate long document recall and format stability (JSON/structured output) on your tasks, and build regression evaluation.

Key Focus Areas

For engineering model selection, focus on:

1. Context window (64K) and its value for long document/multi-turn tasks
2. Whether function calling and constrained output work reliably
3. Hallucination/factual performance on your business data (consider adding RAG)

Original (English)

Mixtral 8x22B is a new open large language model (LLM) released by Mistral AI. Mixtral 8x22B is characterized as a sparse mixture-of-experts model with 39B active parameters out of a total of 141B parameters.

Capabilities

Mixtral 8x22B is trained to be a cost-efficient model with capabilities that include multilingual understanding, math reasoning, code generation, native function calling support, and constrained output support. The model supports a context window size of 64K tokens which enables high-performing information recall on large documents.

Mistral AI claims that Mixtral 8x22B delivers one of the best performance-to-cost ratio community models and it is significantly fast due to its sparse activations.

Source: Mistral AI Blog

Results

According to the official reported results, Mixtral 8x22B (with 39B active parameters) outperforms state-of-the-art open models like Command R+ and Llama 2 70B on several reasoning and knowledge benchmarks like MMLU, HellaS, TriQA, NaturalQA, among others.

Source: Mistral AI Blog

Mixtral 8x22B outperforms all open models on coding and math tasks when evaluated on benchmarks such as GSM8K, HumanEval, and Math. It's reported that Mixtral 8x22B Instruct achieves a score of 90% on GSM8K (maj@8).

Source: Mistral AI Blog

More information on Mixtral 8x22B and how to use it here: https://docs.mistral.ai/getting-started/open_weight_models/#operation/listModels

The model is released under an Apache 2.0 license.