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.
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Mixtral 8x7B Instruct is Mistral AI's mixture-of-experts model that uses 8 expert networks of 7 billion parameters each, activating 2 experts per token. It is available through hosted inference providers and on Hugging Face under the Apache 2.0 license.
Mixtral 8x7B matches or exceeds GPT-3.5 Turbo and Llama 2 70B on most benchmarks while using fewer active parameters. Its MoE architecture delivers strong reasoning and coding performance at efficient compute cost.
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.
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.
Yes, Mixtral 8x7B is released under the Apache 2.0 license for free commercial use. Weights are available on Hugging Face, and hosted inference is available through multiple providers.
Mixtral 8x7B significantly outperforms Mistral 7B on reasoning and complex tasks due to its larger effective parameter count. It activates roughly 13B parameters per token compared to Mistral 7B's 7.3B, available through the same inference platforms.
Mixtral 8x7B requires approximately 90GB of VRAM at full precision, or 24-48GB with quantization. For production use, hosted inference avoids GPU provisioning overhead.
