--- title: Mistral 7B Instruct v0.2—Quality and performance canonical: https://telnyx.com/llm-library/mistral-7b-instruct-v0-2 --- # Mistral 7B Instruct v0.2—Quality and 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.) Mistral 7B Instruct v0.2—Quality and performance # Mistral 7B Instruct v0.2 An improved version of Mistral 7B Instruct with a 32k context window, full attention, and stronger performance on longer sequences and conversations. [Start building](https://telnyx.com/sign-up)[GET Available Models](https://developers.telnyx.com/docs/inference/models) ## about The v0.2 update changed the RoPE base frequency from 10,000 to 1,000,000, a technique that dramatically improved long-context performance and was [subsequently adopted by Llama 3, Qwen, and other model families](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) as the standard approach to extending context in RoPE-based architectures. It also removed the default system prompt enforcement, giving developers full control over instruction formatting. Licenseapache-2.0 Context window ## 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 ### gpt-oss-120b ### 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 Mistral 7B Instruct v0.2 In the [AI Playground](https://portal.telnyx.com/#/app/next/ai/ai-playground "Telnyx AI Playground") select Mistral 7B Instruct v0.2 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 Mistral 7B Instruct v0.2? Mistral 7B Instruct v0.2 is a fine-tuned instruction-following model built on [Mistral AI's 7.3 billion parameter architecture](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2). The v0.2 release expanded the context window to 32K tokens and improved instruction adherence over the original v0.1. ### Is Mistral 7B better than GPT-3? Mistral 7B outperforms GPT-3 and many larger models on key benchmarks despite having far fewer parameters. It achieves competitive results on while being small enough to run on consumer hardware. (in thousands) 32768 ## Use cases for Mistral 7B Instruct v0.2 1. **Long-context document summarization:** The expanded 32K true context window with RoPE theta of 1,000,000 enables reliable summarization of lengthy reports and research papers in a single pass. 2. **Multi-turn technical support:** Full attention (no sliding window) preserves conversation history across extended troubleshooting sessions without the information loss present in v0.1. 3. **Fine-tuning base for domain models:** Its permissive Apache 2.0 license and improved long-context architecture make it a strong starting point for custom instruction-tuned models in specialized fields. ## Quality Arena Elo1072 MMLU55.4 MT Bench7.6 Mistral 7B Instruct v0.2 scores 60.78% on MMLU (5-shot), a 4.5-point improvement over v0.1 (56.3%) on the same sheet. The upgrade to a 32k context window with RoPE theta of 1,000,000 improved long-context performance without sacrificing short-sequence quality. It trails Gemma 7B IT (64.3% MMLU) but remains competitive among 7B-class instruction-tuned models. **Nous Hermes 2 Mixtral 8x7B** 1084 **Hermes 2 Pro Mistral 7B** 1074 **Mistral 7B Instruct v0.2** 1072 **GPT-3.5 Turbo-1106** 1068 **Llama 2 Chat 13B** 1063 ## pricing The cost of running the model with Telnyx Inference is $0.0002 per 1,000 tokens. For instance, to analyze 1,000,000 customer chats, assuming each chat is 1,000 tokens long, the total cost would be $200. ## What's Twitter saying? * Developers praise Mistral 7B Instruct v0.2 for its impressive efficiency, running flawlessly on hardware like RTX 3090 and M1 MBP, making it "mind-blowing" for fine-tuned products. * Users highlight its fast response times, accuracy, and lightweight deployment, ideal for experimentation without heavy hardware or high API costs. * Reviewers note strong benchmark performance like MMLU 55.4 and MT-Bench 7.6, with superior long-context handling via 32k window, though it may struggle with complex reasoning. | 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 | | openai | [gpt-oss-120b](https://telnyx.com/llm-library/gpt-oss-120b) | text generation | Multilingual | 131,072 | 117.0B | small | apache-2.0 | | 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) reasoning and code generation tasks ### What is Mistral 7B Instruct good for? Mistral 7B Instruct v0.2 excels at chat, summarization, and code assistance tasks where low latency and cost efficiency matter. It is well suited for [production deployments](https://telnyx.com/llm-library/mistral-7b-instruct-v0-1) that need a capable model with minimal infrastructure requirements. ### What are the limitations of Mistral 7B? Mistral 7B's main limitations are its relatively small parameter count, which constrains complex multi-step reasoning and broad factual knowledge compared to larger models. It can also struggle with [highly specialized domain tasks](https://telnyx.com/llm-library/llama-2-7b-chat-hf) that benefit from models trained on domain-specific data. ### Why is Mistral 7B so good? Mistral 7B uses grouped-query attention and sliding window attention to achieve high throughput and long context support from a compact architecture. These [architectural innovations](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) let it punch above its weight class, delivering performance competitive with models two to three times its size. ### Is Mistral as good as ChatGPT? Mistral 7B is not directly comparable to ChatGPT (GPT-4 or GPT-4o), which are significantly larger and more capable models. However, for specific tasks like code generation and summarization, Mistral 7B offers a [cost-efficient alternative](https://telnyx.com/llm-library/mistral-7b-instruct-v0-2) that performs well at a fraction of the compute cost.