AI Model Pricing Increases
What the numbers show, laid out in order, is a pricing curve that bends sharply upward. Back in late April, Deepseek V4 Pro arrived with a cost-to-run figure on the Artificial Analysis benchmark that was 15 times higher than its predecessor V3.2 — putting it above even Gemini 3.1 Pro in operational expense. That alone was notable. Then, in mid-May, the second data point arrived: Gemini 3.5 Flash carries a price tag three times higher than the previous Flash version, and a full 30 times more expensive than Gemini 1.5 Flash. Two major labs, two successive generations, both trending the same direction. A third signal complicates the Gemini picture slightly — Gemini 3.5 Flash is performing worse than it appears on Artificial Analysis when you look past the headline scores, which means users are paying more for a model that may not be delivering proportionally more capability. The "newer is cheaper" assumption that shaped developer economics for the past two years is looking shakier by the week.
If confirmed, here is what this means. The era of deflationary AI pricing — where each model generation cost less per token than the last — may be entering a correction, or ending outright. Developers and enterprises who built cost models around continuous price drops will need to revise those assumptions now. The second-order effect is a shift in the competitive calculus: smaller players who competed on price against frontier models lose that edge if frontier pricing is rising anyway, but larger enterprise customers who locked in volume commitments at older rates suddenly look very smart. The Gemini 3.5 Flash situation is particularly pointed — if you are paying 30 times more than 1.5 Flash and the benchmark performance gap does not justify it, that is not progress, that is inflation dressed as capability. Strategic buyers will start demanding clearer value justification from labs, and procurement conversations that were previously informal will get formal fast.
Watch for whether OpenAI's next model release follows the same upward pricing pattern, and whether any lab publicly addresses the cost-performance ratio question directly rather than letting benchmark headlines carry the story.
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