Is OpenAI Down? Complete Guide to ChatGPT, GPT-4, DALL-E & API Outages (2026)

by API Status Check

TLDR: Check if OpenAI is down right now at apistatuscheck.com/api/openai. This guide covers how to verify ChatGPT and GPT-4 API status, common causes of OpenAI outages, and how to build fallback logic so your app stays running during downtime.

TLDR: Check if OpenAI is down at apistatuscheck.com/api/openai and status.openai.com. Build resilient AI integrations with GPT-3.5 fallbacks, exponential backoff retries, aggressive caching, and queue-based processing so your features degrade gracefully instead of completely breaking during outages.

Your ChatGPT conversation just stopped mid-sentence. Your app's AI features are throwing 500 errors. Your users are flooding support asking why the chatbot isn't responding. OpenAI might be down — and for products built on GPT-4, DALL-E, or Whisper, this means your core features are suddenly unavailable.

OpenAI outages are particularly challenging because the platform powers everything from consumer-facing ChatGPT to mission-critical API integrations handling customer support, content generation, code assistance, and image creation. Here's how to quickly diagnose OpenAI issues, build resilient AI integrations, and keep your product functional when OpenAI has problems.

Is OpenAI Actually Down Right Now?

Before you debug your prompts or check your API keys, confirm it's an OpenAI-side issue:

  1. API Status Check — OpenAI — Independent monitoring with real-time status and response times
  2. Is OpenAI Down? — Quick status check with 24-hour incident history
  3. OpenAI Official Status — OpenAI's status page covering all services
  4. Downdetector — OpenAI — Community-reported outages with geographic heatmaps
  5. ChatGPT Direct Test — Try ChatGPT web interface directly

Understanding OpenAI's Service Architecture

OpenAI isn't a single service. Different models and endpoints can fail independently, creating partial outages:

Service What It Does When It Fails
ChatGPT Web Consumer chat interface Web app unusable, API may still work
GPT-4 API Most capable model endpoint Apps using GPT-4 fail, GPT-3.5 may work
GPT-3.5 Turbo Faster, cheaper model Budget integrations fail
GPT-4 Turbo Longer context window variant Vision and long-context features fail
DALL-E 3 Image generation Image creation fails, text models unaffected
DALL-E 2 Legacy image generation Older image integrations fail
Whisper API Audio transcription Speech-to-text fails
TTS API Text-to-speech Voice generation fails
Embeddings Vector representations Search/similarity features fail
Moderation Content filtering Safety checks fail
Assistants API Stateful AI assistants Thread-based apps fail
Fine-tuning Custom model training Training jobs stuck
Codex (deprecated) Code generation Legacy integrations fail

Critical insight: OpenAI often has partial outages where GPT-3.5 works but GPT-4 is degraded, or the API works but ChatGPT web is down. Always test your specific model endpoint.

Common OpenAI Error Codes During Outages

Error Meaning Action
429: Rate limit reached Too many requests OR capacity issues Retry with exponential backoff
500: Internal server error OpenAI internal issue Transient, retry after delay
503: Service unavailable OpenAI temporarily overloaded Retry with backoff, fallback to GPT-3.5
502: Bad gateway OpenAI infrastructure issue Wait and retry
524: A timeout occurred Request took too long Reduce prompt size or use streaming
429: You exceeded your current quota Billing issue (not outage) Check billing dashboard
context_length_exceeded Token limit exceeded Reduce prompt or use GPT-4 Turbo
model_not_found Model deprecated or unavailable Check model availability
Connection timeout Network or OpenAI unreachable Check status page

Rate limit vs. capacity issues: A 429 error can mean you hit your quota OR OpenAI is rationing capacity during high demand. Check status.openai.com to differentiate.

Developer Best Practices: Building Resilient OpenAI Integrations

1. Streaming Responses for Better UX

Streaming makes your app feel responsive and continues to work during slowdowns:

async function streamingCompletion(
  messages: Array<{ role: string; content: string }>,
  onChunk: (text: string) => void
) {
  const stream = await openai.chat.completions.create({
    model: 'gpt-4-turbo-preview',
    messages,
    stream: true,
  })
  
  let fullText = ''
  
  for await (const chunk of stream) {
    const content = chunk.choices[0]?.delta?.content || ''
    if (content) {
      fullText += content
      onChunk(content)
    }
  }
  
  return fullText
}

// Express endpoint with SSE
app.get('/stream-chat', async (req, res) => {
  res.setHeader('Content-Type', 'text/event-stream')
  res.setHeader('Cache-Control', 'no-cache')
  res.setHeader('Connection', 'keep-alive')
  
  try {
    await streamingCompletion(
      [{ role: 'user', content: req.query.prompt as string }],
      (chunk) => {
        res.write(`data: ${JSON.stringify({ chunk })}\n\n`)
      }
    )
    
    res.write('data: [DONE]\n\n')
    res.end()
  } catch (error: any) {
    res.write(`data: ${JSON.stringify({ error: error.message })}\n\n`)
    res.end()
  }
})

Why streaming matters during outages: If OpenAI is slow but not down, streaming delivers partial results. Users see progress instead of waiting for a timeout.

2. Context Window Management

Avoid context_length_exceeded errors, especially during degraded performance:

import { encoding_for_model } from '@dqbd/tiktoken'

function countTokens(text: string, model = 'gpt-4'): number {
  const encoding = encoding_for_model(model)
  const tokens = encoding.encode(text)
  encoding.free()
  return tokens.length
}

function truncateToTokenLimit(
  messages: Array<{ role: string; content: string }>,
  model = 'gpt-4-turbo-preview',
  maxTokens = 8000 // Leave room for response
) {
  const limits: Record<string, number> = {
    'gpt-4': 8192,
    'gpt-4-turbo-preview': 128000,
    'gpt-3.5-turbo': 4096,
    'gpt-3.5-turbo-16k': 16384,
  }
  
  const modelLimit = limits[model] || 8192
  const targetLimit = Math.min(maxTokens, modelLimit - 1000) // Reserve for response
  
  // Always keep system message and last user message
  const systemMessages = messages.filter(m => m.role === 'system')
  const lastUserMessage = [...messages].reverse().find(m => m.role === 'user')
  const otherMessages = messages.filter(
    m => m.role !== 'system' && m !== lastUserMessage
  )
  
  let totalTokens = 
    systemMessages.reduce((sum, m) => sum + countTokens(m.content, model), 0) +
    (lastUserMessage ? countTokens(lastUserMessage.content, model) : 0)
  
  const result = [...systemMessages]
  
  // Add other messages from most recent until we hit limit
  for (let i = otherMessages.length - 1; i >= 0; i--) {
    const message = otherMessages[i]
    const messageTokens = countTokens(message.content, model)
    
    if (totalTokens + messageTokens <= targetLimit) {
      result.push(message)
      totalTokens += messageTokens
    } else {
      console.log(`Truncated ${i + 1} old messages to fit context window`)
      break
    }
  }
  
  // Add last user message at the end
  if (lastUserMessage) {
    result.push(lastUserMessage)
  }
  
  return result
}

// Usage
const truncatedMessages = truncateToTokenLimit(conversationHistory, 'gpt-4-turbo-preview')
const completion = await openai.chat.completions.create({
  model: 'gpt-4-turbo-preview',
  messages: truncatedMessages,
})

3. Multi-Provider Fallback (Advanced)

For critical features, implement fallback to alternative AI providers:

import OpenAI from 'openai'
import Anthropic from '@anthropic-ai/sdk'

const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY })
const anthropic = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY })

async function robustCompletion(
  messages: Array<{ role: string; content: string }>,
  options: { preferProvider?: 'openai' | 'anthropic' } = {}
) {
  const providers = options.preferProvider === 'anthropic'
    ? ['anthropic', 'openai']
    : ['openai', 'anthropic']
  
  for (const provider of providers) {
    try {
      if (provider === 'openai') {
        const completion = await openai.chat.completions.create({
          model: 'gpt-4-turbo-preview',
          messages,
        })
        return {
          content: completion.choices[0].message.content!,
          provider: 'openai',
          model: completion.model,
        }
      } else {
        // Convert to Anthropic format
        const systemMessage = messages.find(m => m.role === 'system')?.content
        const anthropicMessages = messages
          .filter(m => m.role !== 'system')
          .map(m => ({
            role: m.role as 'user' | 'assistant',
            content: m.content,
          }))
        
        const completion = await anthropic.messages.create({
          model: 'claude-3-5-sonnet-20241022',
          max_tokens: 4096,
          system: systemMessage,
          messages: anthropicMessages,
        })
        
        const content = completion.content[0].type === 'text'
          ? completion.content[0].text
          : ''
        
        return {
          content,
          provider: 'anthropic',
          model: completion.model,
        }
      }
    } catch (error: any) {
      const isLastProvider = provider === providers[providers.length - 1]
      
      if (isLastProvider) {
        throw new Error(`All AI providers failed. Last error: ${error.message}`)
      }
      
      console.warn(`${provider} failed, trying next provider...`)
      await new Promise(r => setTimeout(r, 1000))
    }
  }
}

// Usage
const result = await robustCompletion([
  { role: 'system', content: 'You are a helpful assistant.' },
  { role: 'user', content: 'Explain machine learning.' },
])

console.log(`Response from ${result.provider} (${result.model}):`, result.content)

Important: Claude (Anthropic) and GPT-4 have different personalities and capabilities. Your prompts may need tuning for consistent results across providers.

4. Monitoring and Alerting

Track OpenAI API health proactively:

interface OpenAIMetrics {
  latency: number
  tokens: number
  cost: number
  model: string
  success: boolean
  errorCode?: string
}

async function trackOpenAICall(fn: () => Promise<any>): Promise<any> {
  const start = Date.now()
  
  try {
    const result = await fn()
    const latency = Date.now() - start
    
    await metrics.histogram('openai.latency_ms', latency, {
      model: result.model,
      success: 'true',
    })
    
    if (result.usage) {
      await metrics.counter('openai.tokens', result.usage.total_tokens, {
        model: result.model,
      })
      
      // Approximate cost tracking
      const cost = calculateCost(result.model, result.usage)
      await metrics.counter('openai.cost_usd', cost, {
        model: result.model,
      })
    }
    
    return result
  } catch (error: any) {
    const latency = Date.now() - start
    
    await metrics.histogram('openai.latency_ms', latency, {
      model: 'unknown',
      success: 'false',
    })
    
    await metrics.counter('openai.errors', 1, {
      code: error.status || 'network',
      type: error.type || 'unknown',
    })
    
    throw error
  }
}

// Alert rules to configure:
// - openai.errors{code=500} > 5 in 1 min → page on-call
// - openai.errors{code=429} > 10 in 1 min → warning (capacity issue)
// - openai.latency_ms p95 > 30000 → warning (slow API)
// - openai.errors rate > 20% for 3 min → critical

Common OpenAI Issues and Solutions

Issue 1: 429 Rate Limit Errors

Symptoms:

  • Rate limit reached for requests
  • You exceeded your current quota, please check your plan and billing details

Causes:

  • Hit your requests-per-minute (RPM) or tokens-per-minute (TPM) limit
  • OpenAI rationing capacity during high demand (less common)
  • Billing issue (unpaid invoice, card declined)

Solutions:

  1. Check your limits:

  2. Implement proper rate limiting:

    import Bottleneck from 'bottleneck'

// Tier 1: 500 RPM for GPT-4
const limiter = new Bottleneck({
  minTime: 120, // 120ms between requests = ~500 per minute
  maxConcurrent: 10,
})

const rateLimitedCompletion = limiter.wrap(async (messages: any[]) => {
  return await openai.chat.completions.create({
    model: 'gpt-4-turbo-preview',
    messages,
  })
})

  3. Request a limit increase:

    • Platform > Limits > Request increase
    • Provide use case details
    • Usually approved within 24 hours

  4. Check billing:

    • Platform > Billing
    • Verify card is valid and payment succeeded
    • Add credits if using prepaid

Issue 2: 500/503 Server Errors

Symptoms:

  • The server had an error while processing your request
  • Requests timing out
  • Intermittent failures

Solutions:

  1. Implement retries (see code examples above)
  2. Check status.openai.com — likely an outage
  3. Fallback to GPT-3.5 temporarily
  4. Enable caching to serve recent queries
  5. Queue non-urgent requests for later processing

Issue 3: Context Length Exceeded

Symptoms:

  • This model's maximum context length is 8192 tokens
  • Requests failing for long conversations

Solutions:

  1. Use GPT-4 Turbo (128k context vs 8k)

  2. Truncate conversation history (see code example above)

  3. Summarize old messages:

    async function summarizeConversation(messages: any[]) {
  const summary = await openai.chat.completions.create({
    model: 'gpt-3.5-turbo',
    messages: [
      {
        role: 'system',
        content: 'Summarize this conversation in 2-3 sentences.',
      },
      {
        role: 'user',
        content: JSON.stringify(messages),
      },
    ],
  })
  
  return summary.choices[0].message.content
}

  4. Use embeddings + retrieval for long documents instead of stuffing everything in context

Issue 4: ChatGPT Web Works, But API Fails (or Vice Versa)

Cause: Different infrastructure for web vs API

Solution:

  • Check status.openai.com for specific service status
  • Test directly: curl https://api.openai.com/v1/models -H "Authorization: Bearer YOUR_KEY"
  • Clear API key cache (some libraries cache auth failures)

The "OpenAI Is Down" Troubleshooting Checklist

Work through this systematically:

Step 1: Confirm It's OpenAI

  1. Check apistatuscheck.com/api/openai — Real-time monitoring
  2. Check status.openai.com — Official status
  3. Test ChatGPT web — chat.openai.com
  4. Check Twitter/Reddit — Search "OpenAI down"

Step 2: Test Direct API Call

curl https://api.openai.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "gpt-3.5-turbo",
    "messages": [{"role": "user", "content": "test"}]
  }'

If this fails, OpenAI is definitely down for you.

Step 3: Check Your Account

  • Platform > Billing — Any unpaid invoices?
  • Platform > API keys — Is your key valid?
  • Platform > Usage — Have you hit quota limits?
  • Platform > Rate limits — Exceeded RPM/TPM?

Step 4: Model-Specific Issues

  • Try GPT-3.5 if GPT-4 fails
  • Try GPT-4 if GPT-4 Turbo fails
  • Check model availability: curl https://api.openai.com/v1/models -H "Authorization: Bearer YOUR_KEY"

Step 5: Network/Infrastructure

  • Can you reach api.openai.com?
    ping api.openai.com
curl -I https://api.openai.com
  • Check your server's outbound firewall rules
  • Verify DNS resolution
  • Try from a different network/server

Alternative AI Providers (If You Need Redundancy)

If OpenAI outages are impacting your business, consider multi-provider architecture:

Provider Best Models Strengths Pricing vs GPT-4
Anthropic (Claude) Claude 3.5 Sonnet, Opus Long context, reasoning, safety Similar
Google (Gemini) Gemini 1.5 Pro Multimodal, long context (1M tokens) Cheaper
Mistral AI Mistral Large European hosting, fast Cheaper
Cohere Command R+ Enterprise, RAG-optimized Cheaper
OpenRouter All models Aggregator with automatic fallback Markup fee
Local (Ollama) Llama 3, Mixtral Free, private, offline Free (your hardware)

Quick Multi-Provider Example with OpenRouter

OpenRouter provides a unified API for multiple providers with automatic fallback:

import OpenAI from 'openai'

const openrouter = new OpenAI({
  baseURL: 'https://openrouter.ai/api/v1',
  apiKey: process.env.OPENROUTER_API_KEY,
  defaultHeaders: {
    'HTTP-Referer': 'https://yourapp.com',
    'X-Title': 'Your App Name',
  },
})

const completion = await openrouter.chat.completions.create({
  // OpenRouter will try these in order, falling back if one fails
  model: 'openai/gpt-4-turbo-preview',
  // or: 'anthropic/claude-3-5-sonnet'
  // or: 'google/gemini-pro-1.5'
  messages: [{ role: 'user', content: 'Hello!' }],
})

OpenRouter handles fallback, rate limiting, and cost optimization for you.

Local Models with Ollama (Offline Fallback)

For truly critical features, run a local model as last resort:

# Install Ollama
curl https://ollama.ai/install.sh | sh

# Pull a model
ollama pull llama3:8b

# Run server
ollama serve

// Use Ollama as fallback
async function completionWithLocalFallback(messages: any[]) {
  try {
    // Try OpenAI first
    return await openai.chat.completions.create({
      model: 'gpt-4-turbo-preview',
      messages,
    })
  } catch (error) {
    console.warn('OpenAI failed, falling back to local Llama 3')
    
    // Fallback to local Ollama
    const response = await fetch('http://localhost:11434/api/chat', {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify({
        model: 'llama3:8b',
        messages,
        stream: false,
      }),
    })
    
    const data = await response.json()
    return {
      choices: [{
        message: {
          content: data.message.content,
          role: 'assistant',
        },
      }],
      model: 'llama3:8b',
    }
  }
}

Quality tradeoff: Local models like Llama 3 are significantly less capable than GPT-4, but they work offline and are free.

What NOT to Do During an OpenAI Outage

  • ❌ Don't hammer the API with retries — Makes outages worse for everyone
  • ❌ Don't switch providers mid-conversation — Context doesn't transfer cleanly
  • ❌ Don't disable AI features entirely — Use fallbacks and queuing instead
  • ❌ Don't ignore context limits — Causes failures even when OpenAI is up
  • ❌ Don't cache responses indefinitely — Stale AI responses can be harmful
  • ❌ Don't panic-refund users — Most outages resolve within 30 minutes

Get Notified Before Your Users Complain

Every minute of an OpenAI outage means degraded user experience. Set up monitoring now:

  1. Bookmark apistatuscheck.com/api/openai for real-time status
  2. Set up instant alerts via API Status Check integrations — Discord, Slack, email, webhooks
  3. Subscribe to status.openai.com for official updates
  4. Follow @OpenAIStatus on Twitter
  5. Instrument your code — track latency, error rates, token usage
  6. Test your fallback logic — actually try it before you need it in production
  7. Set up queue-based processing — so tasks retry automatically
  8. Implement caching — reduce API dependency for repeated queries

The best AI architecture isn't one that never fails — it's one that degrades gracefully, falls back to alternatives, and keeps your product functional when OpenAI has problems.

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