Real production cost data from the Benchwright /compare calculator across 12 LLMs — input/output ratios, latency tradeoffs, and 3 decisions you should make differently today. Everyone knows the sticker price. Nobody knows the bill. You see "$5 per million tokens" and do mental math: that's cheap, this will cost almost nothing. Then you ship to production, context windows bloat with conversation history, your retry logic fires on 3% of calls, and the response tokens are 4× your estimates because you underestimated how verbose the model is. Three months later your AI feature is costing you $800/month instead of $80. This isn't a niche problem. It's the default outcome for teams that benchmark cost in a notebook and deploy to production without re-measuring. We built the Benchwright /compare calculator to make the gap between sticker price and real production cost visible — and to keep it visible as models update. After running 12 models through it, here's what the data actually shows.…