Cut your AI cost IN HALF (EASY)

2026-07-07 · Source: Matthew Berman · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, long

Summary

Model routing offers a straightforward method to reduce AI operational costs by 60-90%, primarily by distinguishing between "planning" and "execution" phases in AI workflows. For planning, which demands high model performance, expensive frontier models like Fable are recommended. Conversely, for execution tasks such as code generation, cheaper, "good enough" models like GPT-5.5 or Composer 2.5 suffice. A detailed workflow involves a frontier model for research and spec writing, followed by a less expensive model for code generation, and an optional frontier model review. This strategy can reduce the cost of building a feature from \$9.50 to \$3.02, representing a 68% saving. Implementation can be manual via copy-pasting, automated using agent skills (e.g., Claude calling Codex), or through third-party harnesses like Cursor or Not Diamond, which often feature integrated auto-routing. The principle extends beyond coding to general knowledge work, advocating for selecting models based on task complexity and adjusting "thinking levels" to optimize cost and performance, a strategy successfully employed by enterprises like Coinbase.

Key takeaway

For AI Engineers managing development costs, implementing model routing is crucial. Segment your workflow, using premium frontier models like Fable for complex planning and specification. Then, offload code generation to more cost-effective models such as GPT-5.5 or Composer 2.5. This strategy can reduce your project expenses by over 60%, allowing you to stretch your budget significantly further. Explore third-party harnesses or automate model handoffs to streamline this process and optimize resource allocation.

Key insights

Model routing optimizes AI costs by matching task complexity to model capability, using expensive models for planning and cheaper ones for execution.

Principles

• Match model capability to task complexity.
• Prioritize expensive models for planning, cheaper for execution.
• Understand model families and thinking levels.

Method

Use a frontier model for research and spec generation. Delegate code writing to a cheaper model. Optionally, use the frontier model for PR review and the cheaper model for edits/deployment.

In practice

• Manually copy-paste specs between models.
• Automate routing with agent skills or third-party harnesses.
• Adjust model "thinking levels" for simple tasks.

Topics

• Model Routing
• AI Cost Optimization
• Large Language Models
• AI Workflow Automation
• Code Generation
• Enterprise AI

Best for: AI Engineer, MLOps Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Matthew Berman.