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Available now on ModelsLab · Language Model

Mistral: Mixtral 8x7B InstructMixtral Power, Dense Speed

Try Mistral: Mixtral 8x7B InstructAPI Documentation

Run Mixtral Efficiently

Sparse MoE

46B Params, 12.9B Active

Uses two of eight experts per token for 6x faster inference than Llama 2 70B.

Instruction Tuned

Precise Task Following

Fine-tuned with SFT and DPO; scores 8.30 on MT-Bench, matches GPT-3.5.

Multilingual Support

32k Token Context

Handles English, French, German, Italian, Spanish; excels in code and chat.

Examples

See what Mistral: Mixtral 8x7B Instruct can create

Copy any prompt below and try it yourself in the playground.

Code Review

“Review this Python function for bugs and suggest optimizations: def fibonacci(n): if n <= 1: return n else: return fibonacci(n-1) + fibonacci(n-2)”

Text Summary

“Summarize the key benefits of sparse mixture of experts in LLMs, focusing on inference speed and parameter efficiency.”

JSON Generation

“Generate a JSON schema for a task management API with endpoints for creating, listing, and updating tasks.”

Creative Story

“Write a 200-word sci-fi story about an AI exploring abandoned space stations, in third-person narrative.”

For Developers

A few lines of code.
Instruct Mixtral. 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 
import requests


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

FAQ

Common questions about Mistral: Mixtral 8x7B Instruct

Read the docs

Sparse MoE LLM with 46.7B total parameters, activates 12.9B per token. Outperforms Llama 2 70B on benchmarks with 6x faster inference. Instruction-tuned for chat and tasks.

Send formatted prompts via API with user/assistant roles. Supports 32k context. Router selects two experts per token for efficient processing.

Supports English, French, German, Italian, Spanish. Handles code generation well. Context up to 32k tokens.

Apache 2.0 license, open weights. Beats GPT-3.5 on many benchmarks. Cost-effective due to sparse activation.

No built-in moderation mechanisms. Outputs depend on input prompts. Use strict formatting for best results.

Provide code snippets in user prompts. Fine-tuned for completion and generation. Matches top open models in coding benchmarks.

Ready to create?

Start generating with Mistral: Mixtral 8x7B Instruct on ModelsLab.

Try Mistral: Mixtral 8x7B InstructAPI Documentation