aiqbench takeAI Products

This update introduces a market-driven routing mechanism that leverages aggregate user token spend to optimize model selection across diverse benchmarks like coding and research tasks. The approach demonstrates that collective decision-making from millions of users can outperform traditional task-based classifiers while dynamically adapting to new model releases.

Source coveringOpenRouter

What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

Model Routing Powered by Wisdom of the Market

OpenRouterAugust 10, 20267 min read

Our new Auto router is informed by the model decisions of millions of people, and it outperforms conventional task-based classifiers across a wide spectrum of tasks.

The beauty in markets is the pattern of large, diverse groups of independent individuals collectively making better judgments and decisions than any single expert. OpenRouter sees trillions of tokens every day, and we’ve been working on a new way to share the collective wisdom of these millions of people making model decisions. Today we’re launching a major update to our Auto router (openrouter/auto) that’s informed by the over 55T in weekly token spend on OpenRouter. We’ve found that model selection driven by market spend outperforms the choices of our old Auto router across a wide spectrum of tasks and cost levels. By referencing the past 7 days of OpenRouter usage, it always stays up to date with new model releases. We’ve been testing this approach in beta with thousands of people…

Excerpt shown under the publisher's RSS terms · 131 words · full text and figures are on the publisher's site

Read at OpenRouter ↗Ask about this article