r/Superframeworks
GPT-6 cut prices 50%. DeepSeek raised prices 4x. Both grew. Here's the pricing lesson for AI founders.
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TL;DR: Don't pass API price cuts to your customers. DeepSeek doubled revenue after a 4x price hike. Bank the margin, price on value, keep models swappable. What happened this week: OpenAI shipped GPT-6 Sol and Luna at ~50% below the models they replace: GPT-6 Luna: $0.10/$0.50 per million tokens (was $0.20/$1.20) GPT-6 Sol: $2/$10 per million tokens (was $4/$20) Meanwhile, DeepSeek raised API prices 2.3x–4.5x in August and their revenue run rate doubled to $1 billion. Customers didn't churn. The real lesson - model prices are volatile inputs: In five weeks, the cheapest API for a typical document-processing workload changed from DeepSeek Flash (10% of a $49/mo plan) to GPT-6 Luna (9%), while DeepSeek peak pricing hit 35% of that same plan. Nothing changed in the product. The model cost swung 4x. If your pricing is built around a provider's rate card, every lab decision moves your margins - in either direction. What to actually do: Don't pass cuts to customers - a lower model bill is a margin gain, not a signal to reprice Test if you're underpriced - low churn + customer dependency = pricing power you haven't used yet Keep providers swappable - abstract behind one interface, keep a short eval set of real production tasks Price on outcomes - per report, per resolved ticket, per workflow. Not token consumption. Move batch work off-peak - if you use DeepSeek...
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The frame shift: OpenAI cut because it needs market share. DeepSeek hiked because customers depended on it.
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