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", "mistral batch".
allowed-tools	Read, Write, Edit
version	1.0.0
license	MIT
author	Jeremy Longshore <[e-mail]>

Mistral AI Performance Tuning

Overview

Optimize Mistral AI API performance with caching, batching, and latency reduction techniques.

Prerequisites

• Mistral AI SDK installed
• Understanding of async patterns
• Redis or in-memory cache available (optional)
• Performance monitoring in place

Latency Benchmarks

Model	P50	P95	P99	Use Case
mistral-small-latest	200ms	500ms	1s	Fast responses
mistral-large-latest	500ms	1.5s	3s	Complex reasoning
mistral-embed	50ms	150ms	300ms	Embeddings

Instructions

Step 1: Response Caching

import { LRUCache } from 'lru-cache';
import crypto from 'crypto';

const cache = new LRUCache<string, any>({
  max: 1000,
  ttl: 5 * 60 * 1000, // 5 minutes
  updateAgeOnGet: true,
});

function getCacheKey(messages: any[], model: string, options?: any): string {
  const data = JSON.stringify({ messages, model, options });
  return crypto.createHash('sha256').update(data).digest('hex');
}

async function cachedChat(
  client: Mistral,
  messages: any[],
  model: string,
  options?: { temperature?: number; maxTokens?: number }
): Promise<> {
  
   isCacheable = (options?. ?? ) === ;

   (isCacheable) {
     key = (messages, model, options);
     cached = cache.(key);
     (cached) {
      .();
       cached;
    }
  }

   response =  client..({
    model,
    messages,
    ...options,
  });

   content = response.?.[]?.?. ?? ;

   (isCacheable) {
     key = (messages, model, options);
    cache.(key, content);
  }

   content;
}

Step 2: Redis Distributed Caching

import Redis from 'ioredis';
import crypto from 'crypto';

const redis = new Redis(process.env.REDIS_URL);

async function cachedWithRedis<T>(
  key: string,
  fetcher: () => Promise<T>,
  ttlSeconds = 300
): Promise<T> {
  const cached = await redis.get(key);
  if (cached) {
    return JSON.parse(cached);
  }

  const result = await fetcher();
  await redis.setex(key, ttlSeconds, JSON.stringify(result));
  return result;
}

// Semantic cache for similar queries
async function semanticCache(
  client: Mistral,
  query: string,
  threshold = 0.95
): Promise<string | null> {
  // Get embedding for query
  const queryEmbed = await client..({
    : ,
    : [query],
  });
   queryVector = queryEmbed.[].;

  
   cachedQueries =  redis.();

   ( key  cachedQueries) {
     cached = .( redis.(key) || );
     similarity = (queryVector, cached.);

     (similarity >= threshold) {
      .();
       cached.;
    }
  }

   ;
}

Step 3: Request Batching

import DataLoader from 'dataloader';

// Batch embedding requests
const embeddingLoader = new DataLoader<string, number[]>(
  async (texts) => {
    const response = await client.embeddings.create({
      model: 'mistral-embed',
      inputs: texts as string[],
    });
    return response.data.map(d => d.embedding);
  },
  {
    maxBatchSize: 100, // Mistral limit
    batchScheduleFn: callback => setTimeout(callback, 10), // 10ms window
  }
);

// Usage - automatically batched
const [embed1, embed2, embed3] = await Promise.all([
  embeddingLoader.load('Text 1'),
  embeddingLoader.load('Text 2'),
  embeddingLoader.load('Text 3'),
]);

Step 4: Connection Optimization

import { Agent } from 'https';
import Mistral from '@mistralai/mistralai';

// Keep-alive connection pooling
const agent = new Agent({
  keepAlive: true,
  maxSockets: 10,
  maxFreeSockets: 5,
  timeout: 60000,
});

// Note: Check if Mistral client supports custom agents
// If not, connection pooling happens at the HTTP level

Step 5: Streaming for Perceived Performance

// Streaming reduces Time to First Token (TTFT)
async function* streamWithMetrics(
  client: Mistral,
  messages: any[],
  model: string
): AsyncGenerator<{ content: string; metrics: any }> {
  const startTime = Date.now();
  let firstTokenTime: number | null = null;
  let tokenCount = 0;

  const stream = await client.chat.stream({ model, messages });

  for await (const event of stream) {
    const content = event.data?.choices?.[0]?.delta?.content;
    if (content) {
      if (!firstTokenTime) {
        firstTokenTime = Date.now();
      }
      tokenCount++;
      yield {
        content,
        metrics: {
          ttft: firstTokenTime - startTime,
          tokensPerSecond: tokenCount / ((Date.now() - startTime) / 1000),
        },
      };
    }
  }
}


 fullResponse = ;
  ( { content, metrics }  (client, messages, )) {
  fullResponse += content;
  process..(content);
}
.();

Step 6: Model Selection for Speed

type SpeedTier = 'fastest' | 'balanced' | 'quality';

function selectModelForSpeed(tier: SpeedTier, taskComplexity: 'low' | 'medium' | 'high'): string {
  const matrix = {
    fastest: {
      low: 'mistral-small-latest',
      medium: 'mistral-small-latest',
      high: 'mistral-small-latest',
    },
    balanced: {
      low: 'mistral-small-latest',
      medium: 'mistral-small-latest',
      high: 'mistral-large-latest',
    },
    quality: {
      low: 'mistral-small-latest',
      medium: 'mistral-large-latest',
      high: 'mistral-large-latest',
    },
  };

  return matrix[tier][taskComplexity];
}

Step 7: Performance Monitoring

interface PerformanceMetrics {
  model: string;
  latencyMs: number;
  ttftMs?: number;
  tokensPerSecond?: number;
  inputTokens: number;
  outputTokens: number;
  cached: boolean;
}

async function measurePerformance(
  operation: () => Promise<any>,
  metadata: Partial<PerformanceMetrics>
): Promise<{ result: any; metrics: PerformanceMetrics }> {
  const start = Date.now();

  const result = await operation();

  const metrics: PerformanceMetrics = {
    model: metadata.model || 'unknown',
    latencyMs: Date.now() - start,
    inputTokens: result.usage?.promptTokens || 0,
    outputTokens: result.usage?.completionTokens || ,
    : metadata. || ,
    ...metadata,
  };

  
  .(, .(metrics));

   { result, metrics };
}


 { result, metrics } =  (
   client..({ model, messages }),
  { model, :  }
);

Output

• Reduced API latency
• Caching layer implemented
• Request batching enabled
• Performance monitoring active

Error Handling

Issue	Cause	Solution
Cache miss storm	TTL expired	Use stale-while-revalidate
Batch timeout	Too many items	Reduce batch size
Memory pressure	Cache too large	Set max cache entries
Slow TTFT	Large prompts	Reduce prompt size or use smaller model

Examples

Quick Performance Wrapper

const withPerformance = async <T>(
  name: string,
  fn: () => Promise<T>
): Promise<T> => {
  const start = Date.now();
  const result = await fn();
  console.log(`[${name}] ${Date.now() - start}ms`);
  return result;
};

// Usage
const response = await withPerformance('chat', () =>
  client.chat.complete({ model, messages })
);

Parallel Requests with Concurrency Limit

import pLimit from 'p-limit';

const limit = pLimit(5); // Max 5 concurrent requests

const results = await Promise.all(
  prompts.map(prompt =>
    limit(() => client.chat.complete({
      model: 'mistral-small-latest',
      messages: [{ role: 'user', content: prompt }],
    }))
  )
);

Resources

• Mistral AI Models
• LRU Cache Documentation
• DataLoader Documentation

Next Steps

For cost optimization, see mistral-cost-tuning.