Model Routing Powered by Wisdom of the Market — OpenRouter BlogModel Routing Powered by Wisdom of the Market<br>OpenRouter &middot;8/10/2026

We used benchmarks across domains to verify the new Auto router’s performanceWe used benchmarks across domains to verify the new Auto router’s performanceCost comparisonHow the new Auto router worksTry the new Auto router nowUse openrouter/auto-beta to be the first to get new improvements

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 over the past few weeks and today we deployed it for everyone using the Auto router. Try it now by sending "model": "openrouter/auto" to any of our inference endpoints, or read the docs for details.

To power this router, we transformed the task classifications we run on a representative sampling of prompts into a pareto-optimal routing curve. This is the same underlying data that’s shown on our model rankings by task spend. You can specify your desired cost level by sending a cost_tier parameter and it will respect any guardrails/privacy settings from your account when selecting a model.

We used benchmarks across domains to verify the new Auto router’s performance

We designed the router for versatility, so we evaluated it against a deliberately diverse set of benchmarks: MMLU Pro (Knowledge), τ³-bench Banking (Agents), WideSearch (Search), DSQA (Research), and SWE-Atlas QnA (Coding).

To be confident in the new router’s selections at the default cost level, we needed to see it achieve the same level of performance as the old router while reducing costs. We saw this in most domains, with a few exceptions where the old Auto router was underperforming modern budget models. At the max cost level, our bar was frontier performance even if it ended up costing more than the old router.

Default on the new router is cost_tier=low and cost_quality_tradeoff=7 on the old router. Max compares cost_tier=max to cost_quality_tradeoff=0.

BenchmarkNew<br>DefaultOld<br>DefaultNew<br>MaxOld<br>MaxMMLU Pro (Knowledge)85.2% ±0.3 86.6% ±0.191.4% ±0.3 88.8% ±0.3τ³-bench Banking (Agents)20.6% ±1.0 21.0% ±1.031.6% ±1.6 7.2% ±2.7WideSearch (Search)61.6% ±2.6 53.1% ±2.661.9% ±2.4 54.8% ±2.6DSQA (Research)62.9% ±1.6 43.2% ±1.763.0% ±1.6 42.3% ±1.7SWE-Atlas QnA (Coding)30.4% ±2.0 30.4% ±2.360.7% ±1.7 2.4% ±0.0<br>These results capture a moment in time and will shift as community preferences change over time. We’re using benchmarking to understand relative performance between routing strategies to build confidence that the wisdom of the crowd truly is getting you improved results. Our hypothesis is that efficient model selection will improve with time as more people adopt OpenRouter.

Cost comparison

The new router’s default cost tier will be cost-efficient across a range of workloads, while max allows a broader range of high-cost models for when performance matters more than spend.

BenchmarkNew<br>DefaultOld<br>DefaultNew<br>MaxOld<br>MaxMMLU Pro (Knowledge)$140.93 $393.34$255.71 $449.61τ³-bench Banking (Agents)$155.89 $320.04$168.41 $99.36WideSearch (Search)$30.75 $31.83$36.89 $31.60DSQA (Research)$276.00 $147.11$248.83 $144.18SWE-Atlas QnA (Coding)$297.23 $463.73$1,325.08 $205.52<br>Any routing strategy that switches between multiple models will incur some increased costs when input cache is rebuilt. Often this cost is worthwhile in order to move to a model better suited to a new task, but can be wasteful if the model switches too frequently. The Auto router implements “sticky” behaviors to keep multi-turn conversations on one model until the model being used is no longer a leading choice for your task.

How the new Auto router works

The new Auto router makes decisions based on what the people using OpenRouter, in aggregate, are using for exactly the kind of task your prompt represents. Think of it like a market index that always stays up to date and gets more efficient the more people use OpenRouter.

The model mixture below shows how the new Auto router’s routing curve maps each task type to the cost_tier settings: low, medium, high, xhigh, and max. It is a point-in-time view: the routing curve shifts as new models launch and the OpenRouter community’s usage patterns change.

This snapshot is current as of August 10. See the rankings page for the...