---
title: Mixtral 8x22B Instruct — Powerful LLM | ModelsLab
description: Access Mixtral 8X22b Instruct V0.1 for multilingual chat, math, coding, and function calling with 64K context. Generate smarter responses via API now.
url: https://modelslab-frontend-v2-927501783998.us-east4.run.app/mixtral-8x22b-instruct-v01
canonical: https://modelslab-frontend-v2-927501783998.us-east4.run.app/mixtral-8x22b-instruct-v01
type: website
component: Seo/ModelPage
generated_at: 2026-05-13T10:30:10.939464Z
---

Available now on ModelsLab · Language Model

Mixtral 8X22b Instruct V0.1
Sparse Power, Dense Results
---

[Try Mixtral 8X22b Instruct V0.1](/models/together_ai/mistralai-Mixtral-8x22B-Instruct-v0.1) [API Documentation](https://docs.modelslab.com)

Deploy Mixtral Capabilities Fast
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SMoE Architecture

### 39B Active Parameters

Uses 39B of 141B params for dense 70B speed at lower cost.

Multilingual Chat

### English to Spanish Native

Handles en, fr, it, de, es with 64K context for precise recall.

Function Calling

### Native Tool Integration

Supports function calls for app development and tech stack updates.

Examples

See what Mixtral 8X22b Instruct V0.1 can create
---

Copy any prompt below and try it yourself in the [playground](/models/together_ai/mistralai-Mixtral-8x22B-Instruct-v0.1).

Math Proof

“Prove the Pythagorean theorem step-by-step using geometric arguments, then verify with coordinates.”

Code Debugger

“Debug this Python function for sorting linked lists and optimize for O(n log n) time.”

Multilingual Summary

“Summarize quantum computing advances in French, then translate key terms to German.”

Function Call

“Get weather in Paris using get\_current\_weather tool with celsius format.”

For Developers

A few lines of code.
Instruct model. Few lines.
---

ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.

- **Serverless:** scales to zero, scales to millions
- **Pay per token,** no minimums
- **Python and JavaScript SDKs,** plus REST API

[API Documentation ](https://docs.modelslab.com)

PythonJavaScriptcURL

Copy

```
<code>import requests

response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())</code>
```

FAQ

Common questions about Mixtral 8X22b Instruct V0.1
---

[Read the docs ](https://docs.modelslab.com)

### What is Mixtral 8X22b Instruct V0.1?

Instruct fine-tuned version of Mixtral-8x22B-v0.1 for chat. SMoE model with 141B total params, 39B active. Supports 64K context.

### How does mixtral 8x22b instruct v0 1 API work?

Access via vLLM or Transformers engines. Launch with xinference or Hugging Face. Handles chat, function calling natively.

### What languages does Mixtral 8X22b Instruct V0.1 model support?

Fluent in English, French, Italian, German, Spanish. Strong in math, coding tasks.

### Is Mixtral 8X22b Instruct V0.1 alternative to dense models?

Faster than 70B dense models via sparse activation. Cost-efficient for large-scale apps.

### Mixtral 8x22b instruct v0.1 context length?

64K tokens for document recall. Instruct format requires user/assistant alternation.

### mixtral 8x22b instruct v0 1 LLM quantization options?

Supports AWQ Int4, GPTQ Int4. Use PyTorch format for full precision.

Ready to create?
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Start generating with Mixtral 8X22b Instruct V0.1 on ModelsLab.

[Try Mixtral 8X22b Instruct V0.1](/models/together_ai/mistralai-Mixtral-8x22B-Instruct-v0.1) [API Documentation](https://docs.modelslab.com)

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*This markdown version is optimized for AI agents and LLMs.*

**Links:**
- [Website](https://modelslab.com)
- [API Documentation](https://docs.modelslab.com)
- [Blog](https://modelslab.com/blog)

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*Generated by ModelsLab - 2026-05-13*