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Anthropic kept Opus 4.7's rate card identical to 4.6: $5 input, $25 output per million tokens. But the headline masks a change that will hit your bill directly.

Opus 4.7 ships with a new tokenizer that generates up to 35% more tokens from the same text. Prices did not move. Costs can. On agentic or code-heavy workloads, that gap compounds quickly, and it rarely surfaces until you see an unexpected spike.

We ran the math on three realistic workloads, mapped every discount lever (prompt caching, Batch API), and built a migration checklist so your team can measure the real delta before flipping the switch, not after.

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A developer running a charity project just learned an expensive lesson about AI model upgrades.

When Google retired the Gemini 2.0 models, the developer switched to Gemini 2.5 Flash Lite, expecting a smooth transition. Instead, costs exploded and response times slowed to a crawl. The model was using massive amounts of processing power to reason through simple requests that didn't need extra analysis. Different models have wildly different minimum "thinking budgets."

The developer had to rewrite code to control these thinking budgets and prevent runaway costs. The fix was switching to Gemini 2.5 Flash with a hard limit of 50 thinking tokens.Response speeds returned to normal and costs dropped back down.

This is a reminder that model upgrades aren't always improvements for your specific use case, and default settings can quietly drain your budget if you're not watching closely.

Code implementation included to prevent from happening again. Great job!

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