Mistral-Nemo is a state-of-the-art large language model designed for advanced natural language processing tasks, including text generation, summarization, translation, and sentiment analysis. It features a large context window of up to 128k tokens, making it suitable for handling extensive inputs and complex tasks.
Mistral-Nemo is designed for applications requiring high-quality text generation, such as chatbots, content creation tools, document summarization, and multilingual communication solutions.
The model supports multiple languages, making it versatile for global applications.
Mistral-Nemo is built on a Transformer architecture with the following specifications:
The model was trained on a diverse dataset that includes extensive multilingual text and code data. This training set comprises billions of tokens from various domains, ensuring a broad understanding of language nuances.
Mistral-Nemo has demonstrated strong performance on various benchmarks:
The Mistral NeMo model demonstrates strong performance across a range of tasks compared to models like Gemma 2 9B and Llama 3 8B. With a significantly larger context window of 128k, Mistral NeMo outperforms in several areas, especially in HellaSwag (0-shot) with 83.5% accuracy, Winogrande (0-shot) with 76.8%, and TriviaQA (5-shot) with 73.8%. In contrast, Gemma 2 9B and Llama 3 8B have smaller 8k context windows and achieve slightly lower performance, with Gemma 2 9B scoring 80.1% on HellaSwag and 71.3% on TriviaQA, while Llama 3 8B scores 80.6% and 61.0%, respectively. Mistral NeMo also leads in other tasks like OpenBookQA (0-shot) at 60.6% and CommonSense QA (0-shot) at 70.4%, highlighting its effectiveness in handling a wide range of language-based benchmarks.
The model is available on the AI/ML API platform as "mistralai/mistral-nemo" .
Detailed API Documentation is available here.
Mistral AI emphasizes ethical considerations in AI development. The organization promotes transparency about model capabilities and encourages responsible usage to avoid misuse or unintended consequences.
License Type: Mistral-Nemo is released under the Apache 2.0 license, allowing both commercial and non-commercial usage rights. This open licensing fosters innovation and accessibility within the developer community
Get Mistral Nemo API here.
Measured on the AI/ML API gateway across live traffic. Figures are medians unless stated. Last updated 2026-10-12.
import requests r = requests.post( "https://api.aimlapi.com/v1/chat/completions", headers={"Authorization": "Bearer " + AIMLAPI_KEY}, json={ "model": "mistralai/mistral-nemo", "messages": [ { "role": "user", "content": "Hello!" } ] }, ) print(r.json())
const r = await fetch("https://api.aimlapi.com/v1/chat/completions", { method: "POST", headers: { Authorization: `Bearer ${process.env.AIMLAPI_KEY}`, "Content-Type": "application/json", }, body: JSON.stringify({ "model": "mistralai/mistral-nemo", "messages": [ { "role": "user", "content": "Hello!" } ] }), }); console.log(await r.json());
curl -X POST https://api.aimlapi.com/v1/chat/completions \ -H "Authorization: Bearer $AIMLAPI_KEY" \ -H "Content-Type: application/json" \ -d '{"model":"mistralai/mistral-nemo","messages":[{"role":"user","content":"Hello!"}]}'
OpenAI-compatible — swap the base URL and it works with your existing SDK.
| Type | Price |
|---|---|
| Input | |
| Output | |
| Cached input |
| Model | Input | Output | Context | Best for |
|---|---|---|---|---|
Mistral Nemo This page | Coding + agents | |||
| Reasoning + agents | ||||
| Balanced coding + agents | ||||
| Long-context, multimodal & agentic workflows | ||||
| Reasoning + agents |
Mistral Nemo has a 128,000 tokens context window and can return up to 131,072 tokens.
Mistral Nemo takes text as input and returns text.
Use mistralai/mistral-nemo as the model id. Requests go to https://api.aimlapi.com/v1/chat/completions.
Mistral Nemo became available on September 9, 2025.
Mistral Nemo is priced at input $0.20631 / 1M tokens, output $0.233818 / 1M tokens, cached input $0.20631 / 1M tokens.
Yes, Mistral Nemo can stream responses as they are generated.
Mistral Nemo was built by Mistral AI.
