OpenRouter Rate Limits

OpenRouter Rate Limits

Overview

OpenRouter rate limits are per-key, not per-account. Free tier keys get lower limits; paid keys get higher limits that scale with credit balance. The OpenAI SDK has built-in retry with exponential backoff for 429 responses. Check your current limits via GET /​api/​v1/​auth/​key. Rate limit headers are returned on every response.

Check Your Rate Limits

# Query current rate limit configuration for your key
curl -s https://openrouter.ai/​api/​v1/​auth/​key \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" | jq '{
    label: .data.label,
    rate_limit: .data.rate_limit,
    is_free_tier: .data.is_free_tier,
    credits_used: .data.usage,
    credit_limit: .data.limit
  }'
# Example output:
# {
#   "label": "my-app-prod",
#   "rate_limit": {"requests": 200, "interval": "10s"},
#   "is_free_tier": false,
#   "credits_used": 12.34,
#   "credit_limit": 100
# }

Rate Limit Tiers

Free models have separate limits: 50 req/​day (free users), 1000 req/​day (with $10+ credits).

Read Rate Limit Headers

import os
from openai import OpenAI
import requests as http_requests

# The OpenAI SDK abstracts headers, so use requests for direct access
def check_rate_headers():
    """Make a request and inspect rate limit headers."""
    resp = http_requests.post(
        "https://openrouter.ai/​api/​v1/​chat/​completions",
        headers={
            "Authorization": f"Bearer {os.environ['OPENROUTER_API_KEY']}",
            "Content-Type": "application/​json",
            "HTTP-Referer": "https://my-app.com",
        },
        json={
            "model": "openai/​gpt-4o-mini",
            "messages": [{"role": "user", "content": "hi"}],
            "max_tokens": 1,
        },
    )
    return {
        "status": resp.status_code,
        "x-ratelimit-limit": resp.headers.get("x-ratelimit-limit"),
        "x-ratelimit-remaining": resp.headers.get("x-ratelimit-remaining"),
        "x-ratelimit-reset": resp.headers.get("x-ratelimit-reset"),
        "retry-after": resp.headers.get("retry-after"),
    }

Retry Strategy with OpenAI SDK

from openai import OpenAI

# The SDK handles 429 retries automatically with exponential backoff
client = OpenAI(
    base_url="https://openrouter.ai/​api/​v1",
    api_key=os.environ["OPENROUTER_API_KEY"],
    max_retries=5,           # Default is 2; increase for high-throughput
    timeout=60.0,            # Per-request timeout
    default_headers={"HTTP-Referer": "https://my-app.com", "X-Title": "my-app"},
)

# The SDK will:
# 1. Catch 429 responses
# 2. Read Retry-After header
# 3. Wait with exponential backoff (+ jitter)
# 4. Retry up to max_retries times
response = client.chat.completions.create(
    model="anthropic/​claude-3.5-sonnet",
    messages=[{"role": "user", "content": "Hello"}],
    max_tokens=200,
)

Custom Rate Limiter (Client-Side)

import time, threading
from collections import deque

class TokenBucket:
    """Client-side rate limiter to prevent hitting server limits."""

    def __init__(self, rate: int = 200, interval: float = 10.0):
        self.rate = rate           # Max requests per interval
        self.interval = interval
        self._timestamps = deque()
        self._lock = threading.Lock()

    def acquire(self, timeout: float = 30.0) -> bool:
        """Block until a request slot is available."""
        deadline = time.monotonic() + timeout
        while time.monotonic() < deadline:
            with self._lock:
                now = time.monotonic()
                # Remove timestamps outside the window
                while self._timestamps and now - self._timestamps[0] > self.interval:
                    self._timestamps.popleft()

                if len(self._timestamps) < self.rate:
                    self._timestamps.append(now)
                    return True

            time.sleep(0.1)  # Wait and retry
        return False  # Timed out

limiter = TokenBucket(rate=150, interval=10.0)  # Stay under 200 limit

def rate_limited_completion(messages, **kwargs):
    """Completion with client-side rate limiting."""
    if not limiter.acquire(timeout=30):
        raise TimeoutError("Rate limiter timeout")
    return client.chat.completions.create(messages=messages, **kwargs)

Batch Processing with Rate Awareness

import asyncio
from openai import AsyncOpenAI

async def batch_with_rate_limit(prompts: list[str], model="openai/​gpt-4o-mini",
                                 max_concurrent=10, delay_between=0.05):
    """Process a batch of prompts with rate-aware concurrency."""
    semaphore = asyncio.Semaphore(max_concurrent)
    aclient = AsyncOpenAI(
        base_url="https://openrouter.ai/​api/​v1",
        api_key=os.environ["OPENROUTER_API_KEY"],
        max_retries=5,
        default_headers={"HTTP-Referer": "https://my-app.com", "X-Title": "my-app"},
    )

    async def process(prompt, idx):
        await asyncio.sleep(idx * delay_between)  # Stagger requests
        async with semaphore:
            response = await aclient.chat.completions.create(
                model=model,
                messages=[{"role": "user", "content": prompt}],
                max_tokens=200,
            )
            return response.choices[0].message.content

    return await asyncio.gather(*[process(p, i) for i, p in enumerate(prompts)])

Error Handling

Enterprise Considerations

• Rate limits are per-key: use multiple keys to multiply effective throughput
• The OpenAI SDK handles 429 retries automatically -- configure max_retries (default 2)
• Implement client-side rate limiting to stay under limits proactively (cheaper than retries)
• Free models have daily limits separate from the per-key rate limit
• Monitor x-ratelimit-remaining headers to detect approaching limits before hitting 429
• For batch workloads, use staggered concurrent requests rather than burst patterns

References

• Examples | Errors
• Rate Limits | Auth/​Key API