Is OpenRouter Down? How to Check OpenRouter Status in Real-Time

Quick Answer: To check if OpenRouter is down, visit apistatuscheck.com/api/openrouter for real-time monitoring, or check status.openrouter.ai for official updates. Common issues include model routing failures, rate limit errors across providers, credit/billing problems, sudden model unavailability, and increased latency spikes affecting multiple LLM backends.

When your AI application suddenly stops responding or returns cryptic errors, determining whether the issue lies with OpenRouter or a specific upstream provider becomes critical. As an LLM API aggregator routing requests to OpenAI, Anthropic, Google, Meta, and dozens of other providers, OpenRouter's complexity means outages can manifest in subtle, provider-specific ways rather than complete failures. Understanding how to quickly diagnose OpenRouter issues—and distinguish them from upstream provider problems—can save you hours of debugging and help you implement the right fallback strategy.

How to Check OpenRouter Status in Real-Time

1. API Status Check (Fastest Method)

The most reliable way to verify OpenRouter's operational status is through apistatuscheck.com/api/openrouter. This real-time monitoring service:

• Tests actual API endpoints with live model requests every 60 seconds
• Monitors multiple model providers (OpenAI, Anthropic, Google, Meta models)
• Tracks routing latency and response times across providers
• Shows historical uptime and incident patterns over 30/60/90 days
• Provides instant alerts when routing failures or timeouts occur
• Detects provider-specific issues (e.g., only Claude models failing)

Unlike status pages that may lag during fast-moving incidents, API Status Check performs active health checks against OpenRouter's production routing infrastructure, testing the full path from request to provider response.

Monitor OpenRouter in real-time →

2. Official OpenRouter Status Page

OpenRouter maintains status.openrouter.ai as their official incident communication channel. The page displays:

• Current operational status for routing infrastructure
• Active incidents and investigations
• Scheduled maintenance windows
• Provider-specific service disruptions
• Historical incident reports with root cause analysis

Pro tip: Subscribe to status updates via email or RSS feed to receive immediate notifications when incidents occur. OpenRouter's team is generally transparent about which upstream providers are affected during partial outages.

3. Check Provider-Specific Health

Since OpenRouter routes to multiple LLM providers, issues may be isolated to specific backends:

• Is OpenAI Down? - Check OpenAI models (GPT-4, GPT-3.5)
• Is Anthropic Down? - Check Claude models
• Is Together AI Down? - Check open-source models
• Is Groq Down? - Check ultra-fast inference models

If only requests to specific models (e.g., anthropic/claude-3-opus) are failing while others work, the issue likely lies with the upstream provider rather than OpenRouter's routing layer.

4. Test the API Directly

For developers, making a test API call can quickly confirm connectivity and routing:

import openai

# OpenRouter is OpenAI SDK-compatible
client = openai.OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key="YOUR_OPENROUTER_API_KEY"
)

try:
    response = client.chat.completions.create(
        model="openai/gpt-3.5-turbo",
        messages=[{"role": "user", "content": "Test"}],
        timeout=10
    )
    print(f"✓ OpenRouter responding normally: {response.model}")
except openai.APIConnectionError as e:
    print(f"✗ Connection failed: {e}")
except openai.APITimeoutError as e:
    print(f"✗ Request timed out: {e}")
except Exception as e:
    print(f"✗ Error: {type(e).__name__}: {e}")

Look for connection errors, timeouts exceeding 30 seconds, HTTP 502/503/504 errors, or model-specific routing failures.

5. Monitor the OpenRouter Discord

OpenRouter's active Discord community often reports issues in real-time before official status pages update:

• Users share specific error messages and affected models
• OpenRouter team members provide rapid incident updates
• Community debugging helps identify upstream vs. routing issues
• Workarounds and temporary fixes get shared quickly

Join the OpenRouter Discord for community-driven status updates and direct communication with the OpenRouter team during incidents.

Common OpenRouter Issues and How to Identify Them

Model Routing Failures

Symptoms:

• Requests to specific models consistently failing (e.g., all Claude requests timing out)
• Error: "Upstream provider is currently unavailable"
• Successful responses from some models but not others
• Routing to fallback models when primary model specified
• HTTP 502/503 errors with model-specific patterns

What it means: OpenRouter's routing layer successfully received your request but couldn't complete the upstream provider call. This differs from authentication or rate limit errors—the routing itself is broken for specific providers.

Example error:

{
  "error": {
    "message": "Upstream provider (anthropic) is currently unavailable",
    "type": "service_unavailable",
    "code": 503
  }
}

Diagnosis: Test multiple models from different providers. If only Anthropic models fail, the issue is with Anthropic's API. If all models across different providers fail, OpenRouter's routing infrastructure itself is likely impacted.

Rate Limiting Across Providers

Common error patterns:

• 429 Too Many Requests errors more frequently than your usage suggests
• Error: "Rate limit exceeded for provider"
• Sudden rate limits despite being within your OpenRouter tier limits
• Provider-specific rate limit errors (different from OpenRouter's own limits)

What's happening: OpenRouter implements both its own rate limits AND passes through upstream provider rate limits. During high-load periods or provider capacity issues, you may hit:

1. OpenRouter's rate limits (requests per minute to OpenRouter's API)
2. Provider rate limits (the upstream API's limits, even if you have capacity)
3. Shared pool limits (multiple OpenRouter users competing for provider capacity)

Example scenario: You're on OpenRouter's $10/month plan with 500 RPM. You send 200 requests to GPT-4. Normally this works fine, but during an OpenAI capacity crunch, OpenRouter's shared OpenAI pool is maxed out, causing rate limit errors despite you being well under your personal limits.

Credit and Billing Issues

Symptoms:

• Requests failing with 402 Payment Required despite having credits
• Inconsistent billing meter readings
• Prepaid credits not reflecting after purchase
• Model costs higher than advertised pricing
• Unexpected "insufficient credits" errors

Common causes:

• Credit sync delays after payment (typically resolves in 5-10 minutes)
• High-cost model usage draining credits faster than expected
• Cached credit balance in your application vs. actual balance
• Rate limit interactions appearing as credit issues
• Failed webhook notifications about low balance

Debugging approach:

import requests

# Check your current credit balance
headers = {"Authorization": f"Bearer {OPENROUTER_API_KEY}"}
response = requests.get(
    "https://openrouter.ai/api/v1/auth/key",
    headers=headers
)

credit_info = response.json()
print(f"Credit balance: ${credit_info['data']['limit']}")
print(f"Usage this month: ${credit_info['data']['usage']}")

Model Availability Changes

Indicators:

• Previously working model suddenly returns 404 Model not found
• Model names changed or deprecated without warning
• New model versions available but not documented
• Pricing changes for existing models
• Free models becoming paid

Recent examples:

• Model name changes: anthropic/claude-2 → anthropic/claude-2.1
• Sunset models: GPT-3.5 variants deprecated
• Provider removals: Models removed when providers shut down APIs
• Temporary unavailability: Models disabled during provider maintenance

Best practice: Always implement fallback model logic and check OpenRouter's model list endpoint programmatically before assuming a model is available.

Latency Spikes and Slow Responses

What to watch for:

• Response times exceeding 60-120 seconds (normal for long completions: 5-30s)
• Timeouts during streaming responses
• First-token latency delays (time to first streamed chunk)
• Geographic routing issues (requests from EU hitting US endpoints)

Causes:

• Upstream provider congestion (OpenAI, Anthropic overloaded)
• OpenRouter routing layer degradation
• Cold start delays for less popular models
• Network path issues between OpenRouter and providers
• Queue backlog during high-demand periods (evening US hours, weekdays)

Monitoring latency:

import time

start = time.time()
response = client.chat.completions.create(
    model="anthropic/claude-3-opus",
    messages=[{"role": "user", "content": "Hello"}],
    max_tokens=10
)
latency = time.time() - start

print(f"Total latency: {latency:.2f}s")
if latency > 30:
    print("⚠️ Unusually high latency detected")

The Real Business Impact When OpenRouter Goes Down

Loss of Unified LLM Access

OpenRouter's core value proposition is single API, multiple providers. When it goes down, businesses lose:

• One integration instead of five: Rather than maintaining separate integrations for OpenAI, Anthropic, Google, Meta, and Cohere, OpenRouter provides a single SDK-compatible endpoint
• Instant model switching: Production applications can switch between gpt-4, claude-3-opus, and llama-3-70b with a single parameter change
• Automatic fallback routing: OpenRouter can route to fallback models when primary models are unavailable

Impact: A 2-hour OpenRouter outage forces developers to either wait or implement emergency direct integrations with individual providers—potentially requiring code deployments and API key management during a crisis.

Disrupted Cost Optimization Strategies

Many teams use OpenRouter specifically for cost arbitrage and optimization:

• Price comparison: Real-time routing to cheapest available model for each task
• Budget controls: Centralized spend management across all LLM providers
• Token-level pricing: Granular cost tracking impossible with direct provider integrations

Example: A startup routing 80% of requests through cheaper models (mistral-7b-instruct at $0.07/1M tokens) instead of GPT-4 ($30/1M tokens) saves $24,000 monthly. OpenRouter downtime forces fallback to GPT-4 direct, eliminating these savings during the outage.

Failed AI Agent and Autonomous Systems

Modern AI applications rely on multiple LLM calls per user interaction:

• AI agents: Make dozens of LLM calls per task (planning, execution, reflection)
• RAG pipelines: Combine embedding models, reranking, and generation
• Multi-step workflows: Sequential LLM calls where each depends on the previous

Cascade failures: When OpenRouter goes down, entire agent workflows break. A simple customer support chatbot might require:

1. Intent classification (Llama-3-8B)
2. Context retrieval (embedding model)
3. Response generation (Claude-3-Sonnet)
4. Fact-checking (GPT-4)

If step 1 fails, the entire interaction fails—even if the customer's question is simple.

Revenue Loss for AI-Native Applications

For businesses where AI is the product (writing assistants, code generators, creative tools):

• Direct revenue impact: Users cannot use the core product features
• Subscription churn risk: Users canceling after poor reliability experience
• Competitive disadvantage: Competitors using direct provider integrations remain operational
• Trust erosion: "AI isn't reliable" perception spreads

Case study: An AI writing assistant processing 10,000 requests/hour at $0.50 average revenue per session loses $5,000/hour during complete OpenRouter outages—plus the long-term impact of user frustration and negative reviews.

Model Availability Uncertainty

Unlike single-provider outages, OpenRouter issues create uncertainty about which specific capabilities are affected:

• Engineers waste time debugging whether issues are OpenRouter routing or upstream providers
• Product teams can't communicate clearly to users ("some models are working")
• Fallback logic becomes complex (which models are actually available?)
• Testing and validation complicated across multiple provider backends

What to Do When OpenRouter Goes Down: Incident Response Playbook

1. Implement Intelligent Model Fallbacks

The most critical resilience pattern for OpenRouter is graceful model degradation:

import openai
from typing import List, Optional

# Priority-ordered model list: best to fallback options
MODEL_FALLBACK_CHAIN = [
    "anthropic/claude-3-opus",      # Best quality, highest cost
    "openai/gpt-4-turbo",            # Excellent quality, high cost
    "anthropic/claude-3-sonnet",     # Good quality, medium cost
    "openai/gpt-3.5-turbo",          # Decent quality, low cost
    "meta-llama/llama-3-70b",        # Open source, very low cost
]

def call_with_fallback(
    messages: List[dict],
    models: List[str] = MODEL_FALLBACK_CHAIN,
    max_retries: int = 3
) -> Optional[str]:
    """Try models in order until one succeeds."""
    
    client = openai.OpenAI(
        base_url="https://openrouter.ai/api/v1",
        api_key=OPENROUTER_API_KEY
    )
    
    for model in models:
        for attempt in range(max_retries):
            try:
                response = client.chat.completions.create(
                    model=model,
                    messages=messages,
                    timeout=30
                )
                
                # Log successful model for analytics
                print(f"✓ Success with {model} (attempt {attempt + 1})")
                return response.choices[0].message.content
                
            except openai.APITimeoutError:
                print(f"Timeout with {model}, attempt {attempt + 1}/{max_retries}")
                continue
                
            except openai.RateLimitError:
                print(f"Rate limited on {model}, trying next model")
                break  # Skip to next model immediately
                
            except openai.APIStatusError as e:
                if e.status_code >= 500:
                    print(f"Server error {e.status_code} with {model}")
                    break  # Server errors: try next model
                else:
                    raise  # Client errors: don't retry
                    
            except Exception as e:
                print(f"Unexpected error with {model}: {e}")
                break
    
    # All models failed
    raise Exception("All model fallbacks exhausted")

# Usage
try:
    result = call_with_fallback([
        {"role": "user", "content": "Explain quantum computing"}
    ])
    print(result)
except Exception as e:
    # Ultimate fallback: queue for later or show graceful error
    print(f"AI services temporarily unavailable: {e}")

Why this works: If Claude is down, you automatically fall back to GPT-4. If OpenAI is rate-limited, you fall back to Llama. Even during partial OpenRouter outages, you maximize chances of getting some response.

2. Direct Provider Failover for Mission-Critical Applications

Enterprise applications should maintain parallel direct integrations with key providers:

import openai
import anthropic

class MultiProviderLLM:
    def __init__(self):
        # OpenRouter as primary
        self.openrouter = openai.OpenAI(
            base_url="https://openrouter.ai/api/v1",
            api_key=OPENROUTER_API_KEY
        )
        
        # Direct providers as backup
        self.anthropic_direct = anthropic.Anthropic(api_key=ANTHROPIC_KEY)
        self.openai_direct = openai.OpenAI(api_key=OPENAI_KEY)
        
    def complete(self, messages: list, prefer_claude: bool = True):
        # Try OpenRouter first (cost savings + model choice)
        try:
            model = "anthropic/claude-3-opus" if prefer_claude else "openai/gpt-4-turbo"
            response = self.openrouter.chat.completions.create(
                model=model,
                messages=messages,
                timeout=20
            )
            return response.choices[0].message.content
        
        except Exception as openrouter_error:
            print(f"OpenRouter failed: {openrouter_error}")
            
            # Failover to direct provider
            try:
                if prefer_claude:
                    # Direct Anthropic call
                    response = self.anthropic_direct.messages.create(
                        model="claude-3-opus-20240229",
                        messages=messages,
                        max_tokens=4096
                    )
                    return response.content[0].text
                else:
                    # Direct OpenAI call
                    response = self.openai_direct.chat.completions.create(
                        model="gpt-4-turbo-preview",
                        messages=messages
                    )
                    return response.choices[0].message.content
                    
            except Exception as direct_error:
                raise Exception(f"Both OpenRouter and direct provider failed: {direct_error}")

# Usage
llm = MultiProviderLLM()
response = llm.complete([{"role": "user", "content": "Hello"}])

Trade-offs:

• ✅ True resilience: If OpenRouter is down, you still operate
• ✅ No single point of failure in your architecture
• ❌ Complexity: Maintaining multiple SDKs and authentication
• ❌ Cost: May pay more when using direct APIs vs. OpenRouter's negotiated rates
• ❌ Maintenance: Need to keep up with multiple provider API changes

3. Implement Request Queuing for Non-Critical Workloads

For batch processing, analytics, or background AI tasks, queue requests during outages:

import redis
import json
from datetime import datetime

class LLMRequestQueue:
    def __init__(self):
        self.redis = redis.Redis(host='localhost', port=6379, db=0)
        self.queue_key = "llm_request_queue"
        
    def enqueue_request(self, messages: list, model: str, callback_url: str = None):
        """Add request to queue for later processing."""
        request = {
            "messages": messages,
            "model": model,
            "callback_url": callback_url,
            "queued_at": datetime.utcnow().isoformat(),
            "retry_count": 0
        }
        
        self.redis.rpush(self.queue_key, json.dumps(request))
        print(f"✓ Request queued. Queue depth: {self.redis.llen(self.queue_key)}")
        
    def process_queue(self, openrouter_client):
        """Process queued requests when service is restored."""
        while self.redis.llen(self.queue_key) > 0:
            request_json = self.redis.lpop(self.queue_key)
            request = json.loads(request_json)
            
            try:
                response = openrouter_client.chat.completions.create(
                    model=request["model"],
                    messages=request["messages"]
                )
                
                # Deliver result via webhook if callback specified
                if request["callback_url"]:
                    import requests
                    requests.post(request["callback_url"], json={
                        "result": response.choices[0].message.content
                    })
                    
                print(f"✓ Processed queued request from {request['queued_at']}")
                
            except Exception as e:
                # Re-queue if still failing
                if request["retry_count"] < 3:
                    request["retry_count"] += 1
                    self.redis.rpush(self.queue_key, json.dumps(request))
                else:
                    print(f"✗ Failed after 3 retries: {e}")

# Usage during outage
queue = LLMRequestQueue()

try:
    # Try normal request
    response = openrouter_client.chat.completions.create(...)
except Exception:
    # OpenRouter down, queue for later
    queue.enqueue_request(
        messages=[{"role": "user", "content": "Summarize document"}],
        model="anthropic/claude-3-sonnet",
        callback_url="https://myapp.com/webhook/llm-result"
    )
    print("Request queued, will process when service restored")

# Later, when service restored (can run as background job)
queue.process_queue(openrouter_client)

4. Cache Aggressively for Repeated Requests

Reduce OpenRouter dependency by caching responses:

import hashlib
import json
from functools import lru_cache

class CachedLLM:
    def __init__(self, redis_client):
        self.redis = redis_client
        self.cache_ttl = 3600 * 24  # 24 hours
        
    def _cache_key(self, messages: list, model: str) -> str:
        """Generate deterministic cache key."""
        content = json.dumps({"messages": messages, "model": model}, sort_keys=True)
        return f"llm_cache:{hashlib.sha256(content.encode()).hexdigest()}"
        
    def complete(self, messages: list, model: str):
        # Check cache first
        cache_key = self._cache_key(messages, model)
        cached = self.redis.get(cache_key)
        
        if cached:
            print("✓ Cache hit, avoiding API call")
            return json.loads(cached)
        
        # Cache miss, call OpenRouter
        try:
            response = openrouter_client.chat.completions.create(
                model=model,
                messages=messages
            )
            result = response.choices[0].message.content
            
            # Store in cache
            self.redis.setex(
                cache_key,
                self.cache_ttl,
                json.dumps(result)
            )
            
            return result
            
        except Exception as e:
            # During outage, extend cache TTL for stale data
            print("OpenRouter unavailable, checking for stale cache")
            # Check if we have stale cache (Redis key might exist with no TTL)
            # In production, consider serving stale data during outages
            raise e

Use cases for caching:

• FAQ responses (same questions asked repeatedly)
• Document summaries (document content deterministic)
• Code explanations (same code = same explanation)
• Translation (same source text + target language)

5. Implement Circuit Breaker Pattern

Prevent cascading failures by temporarily stopping requests to failing endpoints:

import time
from enum import Enum

class CircuitState(Enum):
    CLOSED = "closed"  # Normal operation
    OPEN = "open"      # Failing, stop trying
    HALF_OPEN = "half_open"  # Testing if recovered

class CircuitBreaker:
    def __init__(self, failure_threshold=5, timeout=60):
        self.failure_threshold = failure_threshold
        self.timeout = timeout
        self.failure_count = 0
        self.last_failure_time = None
        self.state = CircuitState.CLOSED
        
    def call(self, func, *args, **kwargs):
        if self.state == CircuitState.OPEN:
            # Check if timeout elapsed
            if time.time() - self.last_failure_time > self.timeout:
                print("Circuit breaker: Trying half-open state")
                self.state = CircuitState.HALF_OPEN
            else:
                raise Exception("Circuit breaker OPEN: Too many recent failures")
        
        try:
            result = func(*args, **kwargs)
            
            # Success: reset circuit
            if self.state == CircuitState.HALF_OPEN:
                print("Circuit breaker: Service recovered, closing circuit")
            self.failure_count = 0
            self.state = CircuitState.CLOSED
            return result
            
        except Exception as e:
            self.failure_count += 1
            self.last_failure_time = time.time()
            
            if self.failure_count >= self.failure_threshold:
                print(f"Circuit breaker OPENED after {self.failure_count} failures")
                self.state = CircuitState.OPEN
            
            raise e

# Usage
circuit_breaker = CircuitBreaker(failure_threshold=5, timeout=60)

def make_openrouter_call():
    return openrouter_client.chat.completions.create(
        model="anthropic/claude-3-opus",
        messages=[{"role": "user", "content": "Hello"}]
    )

try:
    response = circuit_breaker.call(make_openrouter_call)
except Exception as e:
    print(f"Call failed: {e}")
    # Use fallback or queuing strategy

Benefits:

• Prevents wasting time on calls that will fail
• Reduces load on struggling service (helps recovery)
• Automatically retries after timeout period
• Logs state transitions for incident analysis

6. Monitor and Alert Proactively

Set up comprehensive monitoring before outages occur:

import requests
import time

def health_check_openrouter():
    """Lightweight health check for OpenRouter."""
    start = time.time()
    
    try:
        response = requests.post(
            "https://openrouter.ai/api/v1/chat/completions",
            headers={
                "Authorization": f"Bearer {OPENROUTER_API_KEY}",
                "Content-Type": "application/json"
            },
            json={
                "model": "openai/gpt-3.5-turbo",
                "messages": [{"role": "user", "content": "test"}],
                "max_tokens": 5
            },
            timeout=10
        )
        
        latency = time.time() - start
        
        if response.status_code == 200:
            return {
                "status": "healthy",
                "latency_ms": latency * 1000,
                "status_code": 200
            }
        else:
            return {
                "status": "degraded",
                "latency_ms": latency * 1000,
                "status_code": response.status_code,
                "error": response.text
            }
            
    except requests.Timeout:
        return {"status": "timeout", "error": "Request exceeded 10s timeout"}
    except Exception as e:
        return {"status": "error", "error": str(e)}

# Run every 60 seconds (cron job or background worker)
def monitoring_loop():
    while True:
        result = health_check_openrouter()
        
        if result["status"] != "healthy":
            # Send alert via Slack, PagerDuty, etc.
            send_alert(f"OpenRouter health check failed: {result}")
            
        if result.get("latency_ms", 0) > 5000:  # 5 second threshold
            send_alert(f"OpenRouter high latency: {result['latency_ms']}ms")
        
        time.sleep(60)

Recommended alerts:

• API Status Check - Automated monitoring with instant notifications
• Custom health checks (shown above)
• Error rate monitoring in application logs
• Latency percentile alerts (p95, p99)
• Cost anomaly detection (sudden price spikes may indicate routing issues)

7. Post-Outage Analysis Checklist

After service restoration:

1. Review error logs - Which models failed? Which errors occurred most?
2. Calculate impact - How many requests failed? Revenue impact?
3. Test all fallback logic - Did circuit breakers work? Did queue processing succeed?
4. Validate data consistency - Did partial failures create incomplete transactions?
5. Check upstream providers - Was this OpenRouter or a provider (OpenAI/Anthropic) issue?
6. Update runbooks - Document what worked and what didn't
7. Review monitoring - Did you detect the issue quickly enough?
8. Consider architecture changes - Do you need direct provider integrations?

Frequently Asked Questions

How often does OpenRouter go down?

OpenRouter maintains strong uptime (typically 99.9%+), with most "outages" actually being upstream provider issues (OpenAI, Anthropic, etc. having problems). Complete OpenRouter routing failures are rare (2-4 times per year), while partial outages affecting specific models or providers occur more frequently. Most users experience a few hours of degraded service per year, usually tied to provider-side incidents rather than OpenRouter infrastructure.

What's the difference between an OpenRouter outage and a provider outage?

OpenRouter outage: The routing layer itself fails—you can't reach OpenRouter's API, get 502/503 errors regardless of model, or see authentication/billing system failures. All models from all providers are affected.

Provider outage: A specific upstream provider (OpenAI, Anthropic, etc.) is down. OpenRouter's API responds normally, but requests to that provider's models fail with "upstream unavailable" errors. Other providers' models continue working fine. Example: Claude models fail but GPT-4 works.

Use apistatuscheck.com/api/openrouter alongside provider-specific checks (OpenAI, Anthropic) to distinguish quickly.

Should I use OpenRouter or direct provider APIs for production?

OpenRouter advantages:

• Single integration for 200+ models across 20+ providers
• Cost optimization (automatic routing to cheapest suitable model)
• Built-in fallback routing
• Unified billing and usage tracking
• No vendor lock-in

Direct API advantages:

• One less layer of potential failure
• Slightly lower latency (no routing overhead)
• Direct access to newest features immediately
• Clearer SLA and support channels
• Potentially better rate limits (especially for enterprise customers)

Best practice: Use OpenRouter as primary for flexibility and cost savings, but maintain direct provider API keys and fallback code for mission-critical applications. Hybrid approach gives you OpenRouter's benefits with resilience to routing layer issues.

Can I get refunded for losses during OpenRouter outages?

OpenRouter's terms include uptime targets but typically exclude liability for consequential damages like lost revenue. Refunds or credits are evaluated case-by-case for significant outages. Check your specific plan's SLA (free tier has no SLA, paid tiers may include credits). For enterprise contracts with custom SLAs, contact OpenRouter directly about incident credits.

How do I prevent duplicate requests during timeouts?

Implement idempotency using request IDs:

import uuid

request_id = str(uuid.uuid4())

response = openrouter_client.chat.completions.create(
    model="anthropic/claude-3-opus",
    messages=[{"role": "user", "content": "Process this payment"}],
    extra_headers={"X-Request-ID": request_id}
)

Store request IDs in your database. If a timeout occurs and you retry, use the same request ID. OpenRouter (and most providers) will deduplicate based on this ID, preventing double-processing.

What models should I use as fallbacks?

Design fallback chains based on your use case:

Quality-first (content generation, analysis):

1. anthropic/claude-3-opus (best quality)
2. openai/gpt-4-turbo (excellent quality)
3. anthropic/claude-3-sonnet (good quality)
4. openai/gpt-3.5-turbo (acceptable quality)

Cost-first (high volume, simple tasks):

1. meta-llama/llama-3-8b (cheapest)
2. openai/gpt-3.5-turbo (low cost)
3. anthropic/claude-3-sonnet (mid cost)
4. openai/gpt-4-turbo (high cost, last resort)

Speed-first (real-time chat, low latency):

1. groq/llama-3-70b (ultra-fast via Groq)
2. openai/gpt-3.5-turbo (fast)
3. anthropic/claude-3-haiku (fast + quality)

Provider-diversity (maximum resilience):

1. openai/gpt-4-turbo (OpenAI)
2. anthropic/claude-3-opus (Anthropic)
3. google/gemini-pro (Google)
4. meta-llama/llama-3-70b (open source, multiple providers)

Does OpenRouter support streaming responses?

Yes, OpenRouter fully supports streaming (SSE - Server-Sent Events):

response = openrouter_client.chat.completions.create(
    model="anthropic/claude-3-opus",
    messages=[{"role": "user", "content": "Write a story"}],
    stream=True
)

for chunk in response:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

During outages: Streaming may fail mid-response. Implement timeout logic and be prepared to restart streaming requests. Consider non-streaming for critical reliability.

How do I monitor OpenRouter costs in real-time?

Check your usage programmatically:

import requests

headers = {"Authorization": f"Bearer {OPENROUTER_API_KEY}"}
response = requests.get(
    "https://openrouter.ai/api/v1/auth/key",
    headers=headers
)

data = response.json()["data"]
print(f"Credit limit: ${data['limit']}")
print(f"Used this month: ${data['usage']}")
print(f"Remaining: ${data['limit'] - data['usage']}")

Set up alerts when usage exceeds thresholds to prevent unexpected billing or service interruptions due to credit depletion.

What should I do if only specific models are failing?

This indicates a provider-specific issue rather than OpenRouter-wide:

1. Identify the provider: Check which provider hosts the failing model (e.g., anthropic/* models = Anthropic issue)
2. Check provider status: Visit apistatuscheck.com/api/anthropic or the provider's official status page
3. Switch providers temporarily: Use equivalent models from other providers:
   • anthropic/claude-3-opus → openai/gpt-4-turbo
   • openai/gpt-4 → anthropic/claude-3-opus
   • meta-llama/llama-3-70b → mistralai/mixtral-8x7b
4. Report to OpenRouter: If the provider is operational but OpenRouter can't route, report in Discord or via support

Stay Ahead of OpenRouter Outages

Don't let LLM routing issues break your AI application. Subscribe to real-time OpenRouter monitoring and get instant alerts when routing failures, provider outages, or latency spikes occur—before your users report problems.

API Status Check monitors OpenRouter 24/7:

• ✅ Multi-model health checks across OpenAI, Anthropic, Google, Meta, and open-source providers
• ✅ 60-second polling for instant outage detection
• ✅ Provider-specific alerts (know which backend is failing)
• ✅ Latency tracking and performance degradation alerts
• ✅ Historical uptime and incident analysis
• ✅ Integration with Slack, Discord, email, and webhooks

Plus monitor your entire AI stack:

• Monitor OpenAI Status - GPT-4, ChatGPT, embeddings
• Monitor Anthropic Status - Claude 3 models
• Monitor Together AI Status - Open-source models
• Monitor Groq Status - Ultra-fast LLM inference

Start monitoring your AI infrastructure now →

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Last updated: February 4, 2026. OpenRouter status information is provided based on real-time monitoring. For official incident reports, refer to status.openrouter.ai.