Screening cost-effective AI models with portfolio theory
Understand this story
An analysis based on Artificial Analysis and OpenRouter data borrows portfolio theory to screen models along the two dimensions of intelligence and cost, identifying 8 Pareto-efficient models among 26—options that are cheaper at equal intelligence or more intelligent at equal cost. The analysis says that under a pure token-cost basis, GPT-6 Luna is the most efficient. The conclusion addresses the trade-off between intelligence and token cost and does not cover other expenses or use cases.
AI overview based on source reports · Updated 16 hr ago
Coverage timeline
Follow the coverage to explore different perspectives.
- Hacker News AIThe efficient frontier of AI models
The post applies portfolio theory to AI model selection, treating intelligence as return and cost per task as risk, to identify a Pareto frontier of 8 models from 26 that are not dominated on both dimensions. It ranks frontier models by "points per dollar" above the best free model (K2 Horizon), finding GPT-6 Luna most efficient on token cost alone, Beam 1.0 or GPT-6.1 Terra when a well-paid person is waiting, and MiMo-V2.6-Pro under a minimum-score constraint. It also flags where the analogy breaks: you cannot hold a fraction of a model on a single task or lever up, and cost is not known in advance since long agent sessions can cost 6 to 11 times a typical one.
Activity for this story
- Current comparable activity
- 6
- Peak comparable activity
- 10Nov 5 23:00
- Change in the past 24 hours
- –
Trends compare the same fully observed participants, which may cover fewer participants than current activity. Move the pointer or tap the chart to inspect hourly activity; use the left and right arrow keys with a keyboard.