---

name: mistral-rate-limits description: | Implement Mistral AI rate limiting, backoff, and request management. Use when handling rate limit errors, implementing retry logic, or optimizing API request throughput for Mistral AI. Trigger with phrases like "mistral rate limit", "mistral throttling", "mistral 429", "mistral retry", "mistral backoff". allowed-tools: Read, Write, Edit version: 1.0.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io

Mistral AI Rate Limits

Overview

Handle Mistral AI rate limits gracefully with exponential backoff and request management.

Prerequisites

• Mistral AI SDK installed
• Understanding of async/await patterns
• Access to rate limit headers

Instructions

Step 1: Understand Rate Limit Tiers

Tier	Requests/min	Tokens/min	Tokens/month
Free	2	500K	1B
Production	120	1M	10B
Enterprise	Custom	Custom	Custom

Note: Limits vary by model and are subject to change. Check console.mistral.ai for current limits.

Step 2: Implement Exponential Backoff with Jitter

interface RetryConfig {
  maxRetries: number;
  baseDelayMs: number;
  maxDelayMs: number;
  jitterMs: number;
}

async function withExponentialBackoff<T>(
  operation: () => Promise<T>,
  config: RetryConfig = {
    maxRetries: 5,
    baseDelayMs: 1000,
    maxDelayMs: 60000,
    jitterMs: 500
  }
): Promise<T> {
  for (let attempt = 0; attempt <= config.maxRetries; attempt++) {
    try {
      return await operation();
    } catch (error: any) {
      if (attempt === config.maxRetries) throw error;

      const status = error.status;

      // Only retry on rate limits (429) or server errors (5xx)
      if (status !== 429 && (status < 500 || status >= 600)) throw error;

      // Check for Retry-After header
      const retryAfter = error.headers?.['retry-after'];
      let delay: number;

      if (retryAfter) {
        delay = parseInt(retryAfter) * 1000;
      } else {
        // Exponential delay with jitter to prevent thundering herd
        const exponentialDelay = config.baseDelayMs * Math.pow(2, attempt);
        const jitter = Math.random() * config.jitterMs;
        delay = Math.min(exponentialDelay + jitter, config.maxDelayMs);
      }

      console.log(`Attempt ${attempt + 1} failed (${status}). Retrying in ${delay.toFixed(0)}ms...`);
      await new Promise(r => setTimeout(r, delay));
    }
  }
  throw new Error('Unreachable');
}

// Usage
const response = await withExponentialBackoff(() =>
  client.chat.complete({
    model: 'mistral-small-latest',
    messages: [{ role: 'user', content: 'Hello!' }],
  })
);

Step 3: Token-Based Rate Limiting

class TokenRateLimiter {
  private tokensUsed = 0;
  private windowStart = Date.now();
  private readonly tokensPerMinute: number;
  private readonly windowMs = 60000; // 1 minute

  constructor(tokensPerMinute = 500000) {
    this.tokensPerMinute = tokensPerMinute;
  }

  async waitForCapacity(estimatedTokens: number): Promise<void> {
    const now = Date.now();
    const elapsed = now - this.windowStart;

    // Reset window if needed
    if (elapsed >= this.windowMs) {
      this.tokensUsed = 0;
      this.windowStart = now;
    }

    // Check if we need to wait
    if (this.tokensUsed + estimatedTokens > this.tokensPerMinute) {
      const waitTime = this.windowMs - elapsed;
      console.log(`Token limit approaching. Waiting ${waitTime}ms...`);
      await new Promise(r => setTimeout(r, waitTime));
      this.tokensUsed = 0;
      this.windowStart = Date.now();
    }
  }

  recordUsage(tokensUsed: number): void {
    this.tokensUsed += tokensUsed;
  }
}

// Usage
const rateLimiter = new TokenRateLimiter(500000);

async function rateLimitedChat(messages: Message[]): Promise<string> {
  // Estimate tokens (rough: 4 chars per token)
  const estimatedTokens = JSON.stringify(messages).length / 4;

  await rateLimiter.waitForCapacity(estimatedTokens + 500); // +500 for response

  const response = await client.chat.complete({
    model: 'mistral-small-latest',
    messages,
  });

  if (response.usage) {
    rateLimiter.recordUsage(response.usage.totalTokens || 0);
  }

  return response.choices?.[0]?.message?.content ?? '';
}

Step 4: Request Queue with Concurrency Control

import PQueue from 'p-queue';

const requestQueue = new PQueue({
  concurrency: 5,        // Max concurrent requests
  interval: 1000,        // 1 second interval
  intervalCap: 10,       // Max 10 requests per interval
});

async function queuedRequest<T>(operation: () => Promise<T>): Promise<T> {
  return requestQueue.add(async () => {
    return withExponentialBackoff(operation);
  });
}

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

Step 5: Rate Limit Monitor

class RateLimitMonitor {
  private requestCount = 0;
  private lastReset = Date.now();
  private readonly alertThreshold: number;

  constructor(alertThreshold = 0.8) {
    this.alertThreshold = alertThreshold;
  }

  recordRequest(): void {
    const now = Date.now();
    if (now - this.lastReset >= 60000) {
      this.requestCount = 0;
      this.lastReset = now;
    }
    this.requestCount++;
  }

  checkThreshold(maxRequests: number): void {
    if (this.requestCount / maxRequests > this.alertThreshold) {
      console.warn(`Rate limit warning: ${this.requestCount}/${maxRequests} requests used`);
    }
  }

  getStats(): { requestCount: number; windowRemaining: number } {
    return {
      requestCount: this.requestCount,
      windowRemaining: 60000 - (Date.now() - this.lastReset),
    };
  }
}

Output

• Reliable API calls with automatic retry
• Token-based rate limiting
• Request queue with concurrency control
• Rate limit monitoring

Error Handling

Header	Description	Action
Retry-After	Seconds to wait	Honor this value
X-RateLimit-Limit	Max requests	Monitor usage
X-RateLimit-Remaining	Remaining requests	Throttle if low
X-RateLimit-Reset	Reset timestamp	Wait until reset

Examples

Python Rate Limiting

import time
import asyncio
from mistralai import Mistral

async def with_retry(client, max_retries=3):
    for attempt in range(max_retries):
        try:
            return await client.chat.complete(
                model="mistral-small-latest",
                messages=[{"role": "user", "content": "Hello"}]
            )
        except Exception as e:
            if hasattr(e, 'status') and e.status == 429:
                delay = (2 ** attempt) + (random.random() * 0.5)
                print(f"Rate limited. Waiting {delay:.1f}s...")
                await asyncio.sleep(delay)
            else:
                raise
    raise Exception("Max retries exceeded")

Batch Processing with Rate Limiting

async function processBatch<T, R>(
  items: T[],
  processor: (item: T) => Promise<R>,
  batchSize = 5,
  delayMs = 1000
): Promise<R[]> {
  const results: R[] = [];

  for (let i = 0; i < items.length; i += batchSize) {
    const batch = items.slice(i, i + batchSize);

    const batchResults = await Promise.all(
      batch.map(item => withExponentialBackoff(() => processor(item)))
    );

    results.push(...batchResults);

    // Delay between batches
    if (i + batchSize < items.length) {
      await new Promise(r => setTimeout(r, delayMs));
    }
  }

  return results;
}

Resources

• Mistral AI Rate Limits
• Mistral AI Console
• p-queue Documentation

Next Steps

For security configuration, see mistral-security-basics.