Using expensive model for simple task
Why using GPT-4 for tasks that GPT-3.5 can handle wastes money and increases latency
What is this issue?
Some workflows use expensive AI models (GPT-4, Claude 3 Opus) for simple tasks like text classification, data extraction, or formatting that could be handled by cheaper, faster models. This wastes money without improving results.
Tasks that don't need premium models:
•Simple text classification (positive/negative)•Data extraction from structured text•Format conversion or text reformatting•Simple translation or summarization
Why is this dangerous?
Excessive costs
GPT-4 costs 10-30x more than GPT-3.5 per token—this adds up quickly at scale.
Slower responses
Premium models are often slower, increasing total workflow execution time.
Rate limit pressure
Premium models often have stricter rate limits, causing bottlenecks.
Overengineering
Using sledgehammers for nails creates unnecessary complexity and cost.
How to fix it
- 1
Evaluate task complexity
Ask: Does this task truly need the reasoning power of GPT-4, or is it formulaic?
- 2
Test with cheaper models
Try GPT-3.5-turbo or Claude 3 Haiku first—they often perform equally well for simple tasks.
- 3
Use specialized models
For specific tasks like translation or embeddings, use purpose-built models.
- 4
Implement A/B testing
Compare model outputs for your specific use case before committing to expensive models.
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