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OpenRouter has changed its default routing for AI inference requests from cheapest-provider-first to a blend of price, latency, throughput, and error rate, measured in real time. The company reports gains in time-to-first-token, throughput, and capacity errors, with a small cost increase, but these are self-reported results from before providers have tuned to the new system. Developers using multi-provider models should expect shifting traffic and can still pin providers or sort by price.
How we rebuilt provider routing for better, faster inference

Our default routing used to send most traffic to the cheapest provider for a model. The new routing algorithm constantly monitors the performance of every provider endpoint to optimize the balance of cost, speed, and reliability for each new request.
Today we’re announcing the biggest improvement to our provider routing algorithm since the founding of OpenRouter. Until now, our default router mostly rewarded providers for one thing: a low price. The new router rewards the inference that is actually best for you: cheap, but also fast, high-throughput, and reliable. Providers that deliver more value automatically win more traffic, so every provider has a reason to keep improving. That means the inference you get through OpenRouter keeps getting faster, more reliable, and more cost-effective, and you don’t have to change a line of code. It’s already working. The new algorithm has unlocked significant improvements in TTFT (16.9%), throughput (16.3%) and capacity errors (a remarkable 67% reduction!), with only minor increases in cost (6.0%). That’s before providers have had any time to optimize for the new algorithm. See our full experimental results for more details. It measures what you actually get. For every model, it tracks each endpoint’s realized price, latency, throughput, and error rate, and picks the best blend of cost and performance for each request. It reacts in real time. When…
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