#cost

Show HN: A tool to optimize AI model cost without benchmark score trade-offs

I created this page to apply data envelopment analysis to find the Pareto frontier of cost and benchmark scores. The goal is to identify the cheapest model or a theoretical blend of models applied to tasks that meets the same benchmark scores as your selected model. This is based on a 2022 paper I coauthored. Benchmark sources include Artificial Analysis, ToneBench, Hermes3000 and Slidely, from September–October 2026. Comments URL: https://news.ycombinator.com/item?id=50351277 Points: 2 # Comments: 0

Show HN · EN

AI models cost more when you don’t use English

Some languages require far more tokens for AI models to process, raising costs and reducing context for the people who use them.

Rest of World · EN

Are Harnesses Worth the Cost?

Naresh Jain compares coding agents building the same API service with and without a behavioral harness. A frontier model, GPT-6 Sol, passed every external evaluation either way. But a cost-optimized model, GPT-6 Luna, succeeded in only 3 of 10 unharnessed runs and all 10 with the harness. While adding the harness increased token usage and runtime for Luna, the harnessed Luna runs achieved the same 10-for-10 reliability as GPT-6 Sol while costing 33% less per run ($0.273 vs $0.406). This experiment suggests a behavioral harness might be a practical way to get dependable results from smaller, co

Martin Fowler · EN