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Model Details
Domain:
Task:
Model Access:
Open weights (unrestricted)
Introduction
Mixtral 8x22B is our latest open model. It sets a new standard for performance and efficiency within the AI community. It is a sparse Mixture-of-Experts (SMoE) model that uses only 39B active parameters out of 141B, offering unparalleled cost efficiency for its size. Mixtral 8x22B comes with the following strengths: - It is fluent in English, French, Italian, German, and Spanish - It has strong mathematics and coding capabilities - It is natively capable of function calling; along with the constrained output mode implemented on la Plateforme, this enables application development and tech stack modernisation at scale - Its 64K tokens context window allows precise information recall from large documents
Benchmarking
FLOPs
2.34e+24Notes: Assuming the model was trained on ~1-10 trillions of tokens (same OOM as the models from the comparison in Figure 1. Llama 2 was trained on 2T tokens) + Mistral Small 3 was trained on 8T of tokens, we can estimate training compute with "speculative" confidence: 6 FLOP / token / parameter * 39 * 10^9 active parameters * 10*10^12 tokens [speculatively] = 2.34e+24 FLOP
Training
Training Code Accessibility
Apache 2.0 licenseParameters
Parameters
141000000000Notes: 141B params, 39B active: https://mistral.ai/news/mixtral-8x22b/
Authors
Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault,Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Jean-Malo Delignon, Jia Li, Justus Murke, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Nicolas Schuhl, Patrick von Platen, Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibaut Lavril, Timothée Lacroix, Théophile Gervet, Thomas Wang, Valera Nemychnikova, William El Sayed, William Marshall
Multimodal
Vision