--- title: Mixtral 8x7B Instruct—High quality meets performance canonical: https://telnyx.com/llm-library/mixtral-8x7b-instruct --- # Mixtral 8x7B Instruct—High quality meets performance [Start building](https://telnyx.com/sign-up) ### Ask AI * [![GPT](https://d2efw2akbx8gaf.cloudfront.net/https://dotcom-payload-assets-prod.s3.us-east-1.amazonaws.com/cms-media/chatgpt_icon-ea55e09d.jpeg?width=92&format=webp)](https://chat.openai.com/?q=What makes Telnyx's global network different, breakdown call quality for normal phone calls and voice AI calls.) * [![Claude](https://d2efw2akbx8gaf.cloudfront.net/https://dotcom-payload-assets-prod.s3.us-east-1.amazonaws.com/cms-media/claude_icon-c7c8eef3.webp?width=92&format=webp)](https://claude.ai/new?q=What makes Telnyx's global network different, breakdown call quality for normal phone calls and voice AI calls.) * [![Perplexity](https://d2efw2akbx8gaf.cloudfront.net/https://dotcom-payload-assets-prod.s3.us-east-1.amazonaws.com/cms-media/perplexity_icon-cd5b74f1.webp?width=92&format=webp)](https://www.perplexity.ai/search?q=What makes Telnyx's global network different, breakdown call quality for normal phone calls and voice AI calls.) * [![Gemini](https://d2efw2akbx8gaf.cloudfront.net/https://dotcom-payload-assets-prod.s3.us-east-1.amazonaws.com/cms-media/gemini_icon-4ff1cbf4.webp?width=92&format=webp)](https://www.google.com/search?udm=50&aep=11&q=What makes Telnyx's global network different, breakdown call quality for normal phone calls and voice AI calls.) * [![Grok](https://d2efw2akbx8gaf.cloudfront.net/https://dotcom-payload-assets-prod.s3.us-east-1.amazonaws.com/cms-media/grok_icon-e588e5bd.webp?width=92&format=webp)](https://x.com/i/grok?text=What makes Telnyx's global network different, breakdown call quality for normal phone calls and voice AI calls.) # 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. [Start building](https://telnyx.com/sign-up)[GET Available Models](https://developers.telnyx.com/docs/inference/models) ## about [Mixtral 8x7B Instruct](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1), 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 ## 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. ### DeepSeek-V4-Flash-0731 OrganizationTelnyx Model NameDeepSeek-V4-Flash-0731 Taskstext generation Languages SupportedEnglish Context Length1,048,576 Parameters284.0B Model Tierunlisted Licensemit ### DeepSeek-V4.1-Flash ### gemma-2b-it ### gemma-4-26B-A4B-it ### gemma-4-31B-it ### Llama-3.3-70B-Instruct ### Meta-Llama-3.1-70B-Instruct ### Meta-Llama-3.1-8B-Instruct ### MiniMax-M2.7 ### MiniMax-M3-MXFP8 ### Kimi-K2.5 ### Kimi-K2.6 ### Kimi-K3 ### NVIDIA-NemotronLabs-VoiceChat-11B ### Laguna-S-2.1 ### Qwen3-235B-A22B ### Qwen3.8-27B ### GLM-5.1-FP8 ### GLM-5.2 ### GLM-5.3 ### GLM-5.3-Flash ### claude-haiku-4-5 ### gemini-2.5-flash ### gemini-3.7-flash ### gpt-oss-120b ### gpt-4.1 ### gpt-4o ### gpt-4o-mini ### gpt-5 ### gpt-5.1 ### gpt-5.2 ### gpt-5.4 ### gpt-5.4-mini ### gpt-5.6-luna ### gpt-5.6-sol ### gpt-6-luna ### gpt-live-1 HOW IT WORKS ## Selecting LLMs for Voice AI [GET Available Models](https://developers.telnyx.com/docs/inference/models) ### Create an account Sign-up to the Telnyx platform to get access to our suite of intuitive AI tools. ### Choose Mixtral 8x7B Instruct v0.1 In the [AI Playground](https://portal.telnyx.com/#/app/next/ai/ai-playground "Telnyx AI Playground") select Mixtral 8x7B Instruct v0.1 from the dropdown. ### Enter your API key If you’re using a proprietary model, you’ll need your API key before prompting the LLM. Our vast selection of OS models allow you to get started right away. ### Prompt the LLM Get responses from an LLM in minutes, and scale with our [OpenAI-compatible APIs](https://developers.telnyx.com/docs/inference/getting-started "Inference API Quickstart"). Step 1/4 **RESOURCES** ## Get started Check out our helpful tools to help get you started. * ### Test in the portal Easily browse and select your preferred model in the AI Playground. [Test today](https://portal.telnyx.com/#/ai/assistants) ## Sign up and start building [Sign up](https://telnyx.com/sign-up)[Contact sales](https://telnyx.com/contact-us) ## faqs ### What is Mixtral 8x7B Instruct? 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](https://telnyx.com/llm-library/mixtral-8x7b-instruct) and on Hugging Face under the Apache 2.0 license. ### How good is Mixtral 8x7B? Mixtral 8x7B matches or exceeds GPT-3.5 Turbo and Llama 2 70B on most benchmarks while using fewer active parameters. Its [MoE architecture](https://mistral.ai/news/mixtral-of-experts) delivers strong reasoning and coding performance at efficient compute cost. ## 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. | Organization | Model Name | Tasks | Languages Supported | Context Length | Parameters | Model Tier | License | | ------------ | ----------------------------------------------------------------------- | ---------------- | ------------------- | -------------- | ---------- | ---------- | ----------------- | | Telnyx | DeepSeek-V4-Flash-0731 | text generation | English | 1,048,576 | 284.0B | unlisted | mit | | Telnyx | DeepSeek-V4.1-Flash | text generation | English | 1,048,576 | 552.0B | small | mit | | google | [gemma-2b-it](https://telnyx.com/llm-library/gemma-2b-it) | text generation | English | 8,192 | 2.5B | small | gemma | | google | gemma-4-26B-A4B-it | text to-text | Multilingual | 262,144 | 25.8B | small | apache-2.0 | | google | gemma-4-31B-it | text generation | Multilingual | 131,072 | 30.7B | small | apache-2.0 | | meta-llama | [Llama-3.3-70B-Instruct](https://telnyx.com/llm-library/llama-3.3-70b-instruct) | text generation | Multilingual | 99,000 | 70.6B | large | llama3.3 | | meta-llama | [Meta-Llama-3.1-70B-Instruct](https://telnyx.com/llm-library/meta-llama-3-1-70b-instruct) | text generation | Multilingual | 99,000 | 70.6B | large | llama3.1 | | meta-llama | [Meta-Llama-3.1-8B-Instruct](https://telnyx.com/llm-library/meta-llama-3.1-8b-instruct) | text generation | Multilingual | 131,072 | 8.0B | small | llama3.1 | | minimaxai | MiniMax-M2.7 | text generation | Multilingual | 200,000 | 0 | large | minimaxai | | minimaxai | MiniMax-M3-MXFP8 | text generation | Multilingual | 1,000,000 | 428.0B | small | minimax-community | | moonshotai | [Kimi-K2.5](https://telnyx.com/llm-library/kimi-k2.5) | text generation | Multilingual | 256,000 | 1.0T | large | modified-mit | | moonshotai | Kimi-K2.6 | text generation | Multilingual | 262,144 | 1.0T | medium | modified-mit | | moonshotai | Kimi-K3 | text generation | English | 1,000,000 | 2.8M | large | MIT Modified | | nvidia | NVIDIA-NemotronLabs-VoiceChat-11B | speech to-speech | English | 8,192 | 11.1B | medium | openmdw-1.1 | | poolside | Laguna-S-2.1 | text generation | English | 1,048,576 | 117.6B | unlisted | OpenMDW-1.1 | | Qwen | [Qwen3-235B-A22B](https://telnyx.com/llm-library/qwen3-235b-a22b) | text generation | English | 32,768 | 235.1B | large | apache-2.0 | | Telnyx | Qwen3.8-27B | text generation | English | 262,144 | 27.0B | small | Apache 2.0 | | zai-org | GLM-5.1-FP8 | text generation | Multilingual | 202,752 | 753.9B | large | mit | | zai-org | GLM-5.2 | text generation | English | 1,000,000 | 753.9B | medium | MIT | | Telnyx | GLM-5.3 | text generation | English | 1,048,576 | 753.0B | medium | glm-5.3 | | zai-org | GLM-5.3-Flash | text generation | English | 1,048,576 | 320.0B | small | mit | | anthropic | [claude-haiku-4-5](https://telnyx.com/llm-library/claude-haiku-4-5) | text generation | Multilingual | 400,000 | 0 | large | openai | | google | [gemini-2.5-flash](https://telnyx.com/llm-library/gemini-2.5-flash) | text generation | Multilingual | 400,000 | 0 | large | openai | | google | gemini-3.7-flash | text generation | Multilingual | 400,000 | 0 | large | openai | | groq | [gpt-oss-120b](https://telnyx.com/llm-library/gpt-oss-120b) | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-4.1 | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | [gpt-4o](https://telnyx.com/llm-library/gpt-4-omni) | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | [gpt-4o-mini](https://telnyx.com/llm-library/gpt-4o-mini) | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-5 | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-5.1 | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | [gpt-5.2](https://telnyx.com/llm-library/gpt-5.2) | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-5.4 | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-5.4-mini | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-5.6-luna | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-5.6-sol | text generation | Multilingual | 400,000 | 0 | large | openai | | openai | gpt-6-luna | text generation | Multilingual | 1,050,000 | 0 | large | openai | | openai | gpt-live-1 | speech to-speech | Multilingual | 400,000 | 0 | large | openai | * ### Explore the docs Don’t wait to scale, start today with our public API endpoints. [Get started](https://developers.telnyx.com/api-reference/assistants/list-assistants) * ### Stay up to date Keep an eye on our AI changelog so you don't miss a beat. [See updates](https://telnyx.com/release-notes) ### Is Mixtral 8x7B free? Yes, Mixtral 8x7B is released under the Apache 2.0 license for free commercial use. Weights are available on [Hugging Face](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1), and hosted inference is available through multiple providers. ### How does Mixtral compare to Mistral 7B? 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](https://telnyx.com/llm-library/nous-hermes-2-mixtral-8x7b). ### What hardware does Mixtral 8x7B need? Mixtral 8x7B requires approximately 90GB of VRAM at full precision, or 24-48GB with quantization. For production use, [hosted inference](https://telnyx.com/llm-library/mixtral-8x7b-instruct) avoids GPU provisioning overhead. Mixtral 8x7B Instruct—High quality meets performance