Cheap Batch Classification
Classify large volumes of text at the lowest possible cost per request
Process high volumes (10K-1M items/day) of classification tasks — sentiment analysis, topic categorization, spam detection, content moderation — while keeping cost below $0.01 per request.
The job to be done
Classify text inputs into predefined categories with consistent accuracy. Handle 10K-1M items daily. Output structured labels with confidence scores. Maintain >90% accuracy on standard categories.
Key tradeoffs
Cost is the dominant constraint. A frontier model costs 10-50x more per request than a budget model. For well-defined categories, a budget model often achieves 90%+ accuracy. Quality only matters at the margins.
When to switch models
Start with the cheapest model that hits your accuracy threshold. Only upgrade for categories with high error rates. Consider fine-tuning a small model if your use case is stable.
Recommended models
Related guides
Frequently asked questions
What accuracy can I expect from budget models?
For standard sentiment analysis, budget models typically achieve 88-93% accuracy. For domain-specific classification, accuracy may be 80-85% without fine-tuning.
Should I use batch APIs?
Yes. Most providers offer 50% discounts on batch API calls. If latency isn't critical, batch processing can halve your costs.
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