Mistral Nemo Instruct is an instruction-tuned 12 billion parameter model released July 2024, jointly trained by Mistral AI and NVIDIA. It achieves benchmark scores of 83.5% on HellaSwag, 76.8% on Winogrande, and 68% on MMLU (5-shot), with strong multilingual performance across languages including French, German, Spanish, Italian, Portuguese, Russian, Chinese, and Japanese. Built as a drop-in replacement for Mistral 7B systems, the model supports function calling and was trained with a substantial proportion of multilingual and code data. It uses Tekken tokenization for efficient code handling and is quantization-aware, enabling FP8 inference without performance loss.
Mistral Nemo Instruct (2407) runs on 1 route through the qynio gateway. Compare residency, zero retention, and training posture at a glance — full data-handling detail per route below.
| Provider | Region | Zero data retention | Training | Input | Output |
|---|---|---|---|---|---|
| US | Zero data retention | No | $0.02 | $0.03 |
Mistral Nemo Instruct (2407) runs on a single route through the qynio gateway today, so its availability is that provider's availability.
Name a second model in the same request and the gateway tries it on retriable errors, so a busy hour never has to reach your users. .
Measured over the last 30 days from each provider's official status feed via StatusGator. Refreshed hourly. See uptime for every provider qynio monitors.
Each route hosting Mistral Nemo Instruct (2407) has its own privacy posture, residency, and no-KYC terms. Postures are maintained by qynio with a last-verification timestamp.
Zero data retention is on by default on Pay-as-you-go — no action required. No training on customer data. US; unknown; deposit available.
Call Mistral Nemo Instruct (2407) through the qynio gateway with one API key. Let your coding agent set it up, or call it directly — qynio is drop-in compatible with the OpenAI, Anthropic, and Google AI SDKs.
Copy this and paste it into a coding agent like Claude Code, Cursor or Codex and it'll wire up qynio for you.
import OpenAI from "openai";const client = new OpenAI({apiKey: process.env.QYN_API_KEY,baseURL: "https://api.qyn.io/v3/compat",});const completion = await client.chat.completions.create({model: "deepinfra/mistralai/Mistral-Nemo-Instruct-2407",messages: [{ role: "user", content: "Hello" }],});console.log(completion.choices[0].message.content);
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