---
title: Mixtral-8x7B Instruct v0.1 — Open LLM | ModelsLab
description: Access Mixtral-8x7B Instruct v0.1 API for multilingual chat, code generation, and reasoning. Generate high-quality responses with 32K context now.
url: https://modelslab-frontend-v2-927501783998.us-east4.run.app/mixtral-8x7b-instruct-v01
canonical: https://modelslab-frontend-v2-927501783998.us-east4.run.app/mixtral-8x7b-instruct-v01
type: website
component: Seo/ModelPage
generated_at: 2026-05-13T10:30:01.972547Z
---

Available now on ModelsLab · Language Model

Mixtral-8x7B Instruct v0.1
Sparse Experts, Dense Power
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[Try Mixtral-8x7B Instruct v0.1](/models/together_ai/mistralai-Mixtral-8x7B-Instruct-v0.1) [API Documentation](https://docs.modelslab.com)

![Mixtral-8x7B Instruct v0.1](https://assets.modelslab.ai/generations/67dabe3d-a00a-4cc4-a158-136c8f189137.png)

Run Mixtral Efficiently
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Cost Efficient

### 12.9B Active Params

Mixtral-8x7B Instruct v0.1 uses 46.7B total parameters, activates 12.9B per token for 5x less compute than dense 70B models.

Top Benchmarks

### Beats GPT-3.5 Turbo

Scores 8.30 on MT-Bench, 70.6% MMLU, 1114 Arena ELO, outperforming GPT-3.5 and Claude 2.1.

Long Context

### 32K Token Window

Handles extended dialogues and document analysis via sparse expert routing without dense model memory costs.

Examples

See what Mixtral-8x7B Instruct v0.1 can create
---

Copy any prompt below and try it yourself in the [playground](/models/together_ai/mistralai-Mixtral-8x7B-Instruct-v0.1).

Code Debug

“<s>\[INST\] Debug this Python function that calculates Fibonacci numbers inefficiently: def fib(n): if n <= 1: return n else: return fib(n-1) + fib(n-2) \[/INST\]”

Multilingual Chat

“<s>\[INST\] Explain quantum entanglement in French, then translate to English. Keep it simple for beginners. \[/INST\]”

Reasoning Task

“<s>\[INST\] Step-by-step: If a train leaves at 3 PM traveling 60 mph, and another at 4 PM at 80 mph, when does the second catch the first if 200 miles apart? \[/INST\]”

Story Continuation

“<s>\[INST\] Continue this sci-fi story: The last human awoke in a derelict spaceship, stars unfamiliar. Alarms blared as... \[/INST\]”

For Developers

A few lines of code.
Instruct chat. One call.
---

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-8x7B Instruct v0.1
---

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

### What is Mixtral-8x7B Instruct v0.1?

Mixtral-8x7B Instruct v0.1 is Mistral AI's sparse mixture-of-experts LLM with 8x7B parameters, instruction-tuned for dialogue, code, and reasoning. It activates 12.9B params per token. Licensed Apache 2.0.

### How does mixtral 8x7b instruct v0 1 API compare to GPT-3.5?

Mixtral-8x7B Instruct v0.1 API beats GPT-3.5 Turbo on MT-Bench (8.30 vs 7.94), MMLU (70.6% vs 70.0%), and Arena ELO (1114 vs 1105). Uses less compute.

### What is the context window for Mixtral-8x7B Instruct v0.1 model?

Supports 32K token context for long dialogues and analysis. Some providers limit to 16K input and output.

### Is Mixtral-8x7B Instruct v0.1 alternative to Llama 70B good?

Matches Llama 2 70B quality across benchmarks at 5x less inference compute. Ideal for multilingual tasks and chatbots.

### Mixtral-8x7B Instruct v0.1 LLM for code generation?

Fine-tuned for code tasks via supervised fine-tuning and DPO. Use chat template for precise outputs.

### Where to access mixtral 8x7b instruct v0 1 api?

Available via ModelsLab endpoints for text generation. Supports JSON requests with instruction format.

Ready to create?
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Start generating with Mixtral-8x7B Instruct v0.1 on ModelsLab.

[Try Mixtral-8x7B Instruct v0.1](/models/together_ai/mistralai-Mixtral-8x7B-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*