Model optimization

Mistral compression

Mistral models are popular for self-hosted deployments due to their efficiency. Compression further optimizes inference by reducing prompt prefill time.

By Arjun Shah - Creator of SuperCompress - Updated 2026-07-03

Mistral with compression

from supercompress import Compressor
from mistralai import Mistral

comp = Compressor()
client = Mistral(api_key="...")

def chat_with_compression(messages, query):
    history = "\n".join(m["content"] for m in messages[:-1])
    compressed = comp.compress(history, query)
    messages[-1]["content"] = compressed.compressed_text + "\n" + query
    return client.chat.complete(model="mistral-large", messages=messages)

Frequently asked questions

Does compression work with Mistral's function calling?

Yes. Function definitions are preserved. Only conversation history is compressed.

Can I use it with self-hosted Mistral?

Yes. The compressor runs locally and compresses before sending to your self-hosted instance.

Try it yourself

Paste your long prompt into the playground, ask a question, and see what SuperCompress keeps and removes. Free, no signup needed.

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