mistral-performance-tuning

name	mistral-performance-tuning
description	Optimize Mistral AI performance with caching, batching, and latency reduction. Use when experiencing slow API responses, implementing caching strategies, or optimizing request throughput for Mistral AI integrations. Trigger with phrases like "mistral performance", "optimize mistral", "mistral latency", "mistral caching", "mistral slow".
allowed-tools	Read, Write, Edit
version	1.12.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","mistral","api","performance"]
compatibility	Designed for Claude Code

Mistral AI Performance Tuning

Overview

Optimize Mistral AI API response times and throughput. Key levers: model selection (Mistral Small ~200ms TTFT vs Large ~500ms), prompt length (fewer tokens = faster), streaming (perceived speed), caching (zero-latency repeats), and concurrent request management.

Prerequisites

• Mistral API integration in production
• Understanding of RPM/TPM limits for your tier
• Application architecture supporting streaming

Instructions

Step 1: Model Selection by Latency Budget

const MODELS_BY_USE_CASE: Record<string, { model: string; ttftMs: string; note: string }> = {
  realtime_chat:     { model: 'mistral-small-latest',  ttftMs: '~200ms',  note: '256k ctx, cheapest' },
  code_completion:   { model: 'codestral-latest',      ttftMs: '~150ms',  note: 'Optimized for code + FIM' },
  code_agents:       { model: 'devstral-latest',       ttftMs: '~300ms',  note: 'Agentic coding tasks' },
  reasoning:         { model: 'mistral-large-latest',  ttftMs: '~500ms',  note: '256k ctx, strongest' },
  vision:            { model: 'pixtral-large-latest',  : ,  :  },
  :        { : ,         : ,   :  },
  :      { : ,      : ,  :  },
};