fidzu : cms

Cost anxiety

Last month, on a client project, I ran out of my monthly 10,000 Copilot Credits allotment (which costs approximately $100 USD). Limiting developers to 10,000 Copilot Credits per month is probably not enough for enterprise development, yet these limitations forced me to think about how best to use my tokens, specifically which models to use them with.

Token anxiety

Watching these credits being consumed was anxiety-provoking, especially as the context window filled up quickly while I desperately tried to get the AI to fix the creation of the Drupal model that had skidded off the rails. In just 2 hours, I consumed 2,000 credits, which forced me to research how best to get the most out of my credits/tokens. The most immediate mistake I made was spending 2 hours in one session, which created a huge context window that was being compacted as more and more tokens were consumed with each request and response. The biggest mistake I made was choosing the wrong model to begin with and, more specifically, using only one model and only one tier.

Choosing a model

The big three LLMs that I use are: Claude from Anthropic, GPT from OpenAI, and Gemini from Google.

Since Claude Code was the first coding harness to produce acceptable code, it is the default for many developers, but damn, Claude is expensive.

Gemini is said to be the cheapest model, but frankly, my one-off experiment didn't generate acceptable code. Simply put, I can live with an LLM getting a method's parameter order or data typing wrong, but when they start inventing nonexistent methods, I am out.

GPT has become my go-to model because the code is decent, readable, and refactorable.

Last week, OpenAI showed they were aware of the cost challenge when they adjusted their GPT 5.6 pricing, with the publication of advancing the...Read More

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