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Overview

All AI models have usage limits imposed by their providers. These limitations are designed to ensure fair usage and maintain service quality across all users. While we continuously work to upgrade our service tiers and improve token limitations, these constraints are inherent to working with external AI providers.

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Understanding Model Limits

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Provider-Imposed Restrictions

Each AI model comes with limits set by its provider:
  • Rate Limits: Maximum number of requests per minute/hour
  • Token Limits: Maximum tokens per request or time period
  • Concurrent Requests: Maximum simultaneous requests
  • Daily/Monthly Quotas: Total usage limits over time periods

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Why Limits Exist

Model providers implement these limits to:
  • Ensure fair access for all users
  • Maintain service stability and quality
  • Manage computational resources effectively
  • Prevent abuse and excessive usage

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Common Error Messages

When you encounter model limits, you may see these error types:

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“Model Overloaded”

  • Cause: The model is currently experiencing high demand
  • Solution: Wait a moment and try again, or switch to a different model
  • Prevention: Use less popular models during peak hours

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”Rate Limit Exceeded”

  • Cause: Too many requests sent in a short time period
  • Solution: Wait for the rate limit to reset, then try again
  • Prevention: Spread out your requests over time

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”Token Limit Exceeded”

  • Cause: Your request contains too many tokens for the model
  • Solution: Reduce the length of your input or conversation history
  • Prevention: Break large requests into smaller chunks

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”Quota Exceeded”

  • Cause: Daily or monthly usage limits have been reached
  • Solution: Wait for the quota to reset or upgrade your plan
  • Prevention: Monitor your usage and plan accordingly

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”Service Unavailable”

  • Cause: Temporary service issues with the model provider
  • Solution: Try again later or switch to an alternative model
  • Prevention: Have backup model options ready

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Handling Limits Effectively

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Model Switching Strategy

When you encounter limits:
  1. Try an Alternative Model: Switch to a similar model from a different provider
  2. Use Lighter Models: Consider using faster, more efficient models like GPT O3 Mini
  3. Wait and Retry: For temporary limits, wait a few minutes before retrying
  4. Optimize Your Requests: Reduce unnecessary context or break large tasks into smaller ones
If your preferred model is limited:
  • Claude 4 Sonnet unavailable → Try Claude 3.7 Sonnet or GPT-4.1
  • GPT-4.5 Preview unavailable → Try GPT-4.1 or Gemini 2.5 Pro
  • Gemini 2.5 Pro unavailable → Try Gemini 2.5 Flash or Claude 4 Sonnet
  • Grok 3 Latest unavailable → Try Grok 3 Mini or Claude 4 Sonnet

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Best Practices

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Request Optimization

  • Be Concise: Use clear, specific prompts to reduce token usage
  • Manage Context: Keep conversation history relevant and focused
  • Batch Similar Tasks: Group related requests together when possible
  • Use Efficient Models: Choose the right model for your task complexity

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Error Handling

  • Implement Retry Logic: Automatically retry requests after delays
  • Graceful Degradation: Have fallback options when preferred models fail
  • User Communication: Inform users about temporary limitations
  • Monitor Usage: Track your usage patterns to avoid hitting limits

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Our Commitment to Improvement

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Continuous Upgrades

We are actively working to:
  • Upgrade Service Tiers: Negotiate higher limits with model providers
  • Improve Caching: Reduce redundant requests through better caching
  • Optimize Performance: Enhance efficiency to maximize your available tokens
  • Monitor Usage: Track patterns to predict and prevent limit issues

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What We’re Doing

  • Provider Relationships: Building stronger partnerships for better access
  • Infrastructure Scaling: Expanding our infrastructure to handle more requests
  • Smart Routing: Distributing requests across multiple endpoints
  • Usage Analytics: Providing better visibility into your usage patterns

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Future Improvements

  • Predictive Limits: Warning systems before you hit limits
  • Automatic Failover: Seamless switching between models when limits are reached
  • Usage Optimization: AI-powered suggestions to optimize your token usage
  • Enterprise Tiers: Higher limits for business and enterprise users

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Troubleshooting

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When Limits Persist

If you frequently encounter limits:
  1. Check Your Usage: Review your recent activity and usage patterns
  2. Optimize Requests: Reduce unnecessary context and verbose prompts
  3. Spread Usage: Distribute heavy usage across different time periods
  4. Consider Alternatives: Use more efficient models for routine tasks
  5. Contact Support: Reach out if you need help with usage optimization

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Getting Help

  • Error Logs: Check the error message for specific guidance
  • Model Status: Monitor our status page for provider-wide issues
  • Usage Dashboard: Review your usage patterns and remaining quotas
  • Support Team: Contact us for assistance with persistent limit issues

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Model-Specific Limits

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Token Limits by Model

  • Claude 4 Sonnet: 1M tokens per conversation
  • GPT-4.5 Preview: 128K tokens per conversation
  • Gemini 2.5 Pro: 1M tokens per conversation
  • Grok 3 Latest: 128K tokens per conversation

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Rate Limits by Plan

  • Free Plan: 10 requests per minute
  • Pro Plan: 100 requests per minute
  • Team Plan: 500 requests per minute
  • Enterprise Plan: Custom limits based on agreement
Note: Actual limits may vary based on model availability and provider policies
Model limits are an inherent part of working with AI providers. By understanding these limitations and implementing smart strategies, you can maximize your productivity while staying within bounds. We’re continuously working to improve your experience and reduce the impact of these constraints.
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