I recently built an ingredient analysis feature for a health app (Meadow Mentor). During the design phase, I tested two architectural approaches to handle the complexity of identifying unsafe ingredients.

The Hypothesis: Could a lower-cost "Lite" model with a highly structured system prompt match the accuracy of a "Thinking" model (Reasoning), but with better unit economics?

*The Experiment: I built and tested two configurations against the same validation set:

Configuration A: The "Brain" Approach

- Stack: Gemini 2.5 Flash (Thinking Mode enabled).

- Logic: Relied on the model's internal reasoning loop to process the image and determine safety.

Configuration B: The "Structure" Approach

- Stack: Gemini 2.5 Flash Lite.

- Logic: Disabled reasoning. Used a 4-step System Prompt to force a linear path (Extract -> Normalize -> Verify -> Format).

The Results: Configuration B (Structured) outperformed Configuration A significantly in efficiency, while maintaining 100% accuracy on the test set.

- Tokens: Reduced by 61% (3,595 -> 1,396). The "Thinking" model generated massive internal token overhead.

- Latency: Reduced by 43% (21s -> 12s).

Conclusion: For defined business logic, "Thinking" models introduce unnecessary cost and latency. "Dumb" models with smart prompts are still the superior engineering choice for production reliability.

I wrote up the full case study on the design process here: https://reidkimball.com/case-studies/cutting-ai-feature-cost...