Optimize AI Model Usage by Phase

Use frontier models for analysis and planning, smaller models for execution

When leveraging AI models for software development, match the model's capabilities to the task phase — use the strongest frontier models for analysis and planning, then switch to smaller models for execution.

Analysis and planning phase

Frontier models like GPT-5, Claude 4.1 Opus, or Gemini 2.5 Pro excel at complex tasks that require deep understanding:

• Codebase analysis — comprehending architectural patterns, dependencies, and system design

• Instruction parsing — understanding nuanced requirements and edge cases

• Imeplementation planning — generating comprehensive, step-by-step implementation plans

While these models deliver exceptional quality, they come with higher costs and slower response times. This trade-off is worthwhile during the critical planning phase where accuracy and thoroughness determine project success.

Execution phase

Once you have a detailed plan, transition to smaller, faster models like GPT-5 mini, Claude Sonnet, Gemini Flash, or one of the high-quality open alternatives. These models can:

• Follow the pre-established plan faithfully

• Execute tool calls and file modifications

• Complete well-defined implementation tasks

• Provide rapid iterations with lower latency

This approach delivers significant cost savings while maintaining execution quality, as the complex reasoning has already been completed.

When to use each approach

Stick with frontier models throughout when:

• The task requires continuous complex reasoning

• Requirements are ambiguous or evolving

• Working with novel architectures or technologies

• Debugging intricate, systemic issues

Switch to smaller models when:

• Executing a clear, detailed plan

• Performing routine code modifications

• Running repetitive tasks with established patterns

• Speed and cost efficiency are priorities

By strategically deploying models based on task requirements, you achieve the optimal balance of intelligence, speed, and cost-effectiveness in your development workflow. Experiment with different model combinations to find what works best for your specific use cases.