Mistral-Small-3.2-24B-Instruct-2506

You can use Mistral-Small-3.2-24B-Instruct-2506 as a general-purpose chat and coding assistant with strong long-context reasoning (≈128K–131K tokens) and efficient latency for real-time applications. It fits well as the core LLM in API backends, enterprise copilots, and agentic systems that rely on structured tool calling and reliable instruction following at scale.

How to get started

pip install requests

import requests


api_url = "https://api.regolo.ai/v1/chat/completions"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer YOUR_REGOLO_KEY"
}
data = {
  "model": "mistral-small-4-119b",
  "messages": [
    {
      "role": "user",
      "content": "If a train travels 60 km/h for 2 hours and then 80 km/h for 1.5 hours, what is the total distance covered?"
    }
  ],
  "reasoning_effort": "high"
}

response = requests.post(api_url, headers=headers, json=data)
print(response.json())
Code language: JavaScript (javascript)

Applications & Use Cases

• Multilingual chat assistants for customer support, internal help desks, and product-facing chatbots that need precise instruction following and low repetition.

• Coding copilots that generate, refactor, and explain code, integrated into IDEs or text-based developer workflows using the Instruct variant as the backbone.

• Tool and function-calling agents for workflows such as booking, operations dashboards, and data pipelines, using the improved, more robust function-calling template.

• Long-context summarization and document Q&A over contracts, logs, and reports, leveraging the ~128K–131K token context window without sacrificing quality.

• Enterprise copilots that orchestrate retrieval-augmented generation, decision support, and workflow automation across CRM, ERP, and BI systems.

• Evaluation, red-teaming, and alignment pipelines that benefit from the model’s reduced infinite generations and more stable behavior on challenging prompts.

• Cost-efficient API or on-prem deployments where you need a high-quality 24B model that rivals much larger LLMs while remaining affordable to scale