Cost Optimization
Master strategies to reduce AI agent costs while maintaining performance quality
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0 / 5 completedChoosing the Right Model
Model selection is the highest-leverage cost optimization. GPT-4 costs 20x more than GPT-3.5-turbo, but many tasks don't need GPT-4's capabilities. The key is matching model capability to task complexity—use the cheapest model that meets quality requirements.
Model Selection Strategy
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Simple tasks (classification, extraction, formatting): Use GPT-3.5-turbo, Claude Haiku, or Gemini Pro
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Moderate complexity (summarization, QA, basic reasoning): Use Claude Sonnet or GPT-3.5-turbo
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Complex reasoning (multi-step analysis, code generation, creative writing): Use GPT-4 or Claude Opus
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Cascade pattern: Try cheap model first, escalate to expensive only if quality insufficient
Interactive: Model Cost Calculator
Compare costs across different models for your use case:
Select up to 3 models to compare:
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Implement Model Cascading
Start with GPT-3.5-turbo for all requests. If quality is insufficient (detected via output validation or user feedback), retry with GPT-4. This "fast and cheap by default, smart when needed" approach reduces costs by 60-80% while maintaining high quality for complex tasks.