API Model Beat Custom-Trained AI at Same Cost, Prompting Team to Ditch Distillation

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A development team initially estimated they could save roughly $86,000 by training a smaller Qwen2.5-7B model instead of using Claude Sonnet to process 6.5 million financial text units. Early benchmarking showed DeepSeek V3.2 underperforming Sonnet on hard test cases, which initially supported the case for building a distilled student model. However, after refining their extraction rubric to allow 'Unknown' as a valid output, a newer DeepSeek model passed all 46 cases across three held-out test sets. Optimized API costs then converged with the estimated $1,000 GPU cost of training and running their own model. With correction cycles far faster via API than through retraining and redeployment, the team decided renting a model was the more practical choice for this workload.

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