Introducing Baseten Router


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Introducing Baseten Router: a Pareto-optimal model router for coding tasks

Baseten Router picks the right model per coding task, within 0.8 pts of Opus 5.5’s pass rate at 72% lower cost. Now in public preview on Baseten Grid.

Authors

!Guillaume TroadecGuillaume Troadec

!blauMatthew Blau

!AidanAidan Phillips

!Josh GreenJosh Green

!Ervin WangErvin Wang

!Alex TanAlex Tan

Date

November 4, 2026

!Introducing Baseten Router: a Pareto-optimal model router for coding tasks

TLDR


Baseten Router sends each coding task to the right model, open or closed, so you pay frontier prices only when the task needs it. In benchmarks, it came within 0.8 percentage points of Opus 5.5 at 72% lower cost. It’s the only router we tested that beat random routing by a statistically significant margin. Baseten Router is part of Baseten Grid, now in public preview.

Today, we’re sharing our initial results from training an intelligent model router for Baseten Grid.

Not every coding task needs the most capable model. As AI coding spend continues to grow rapidly [\[1\]](https://menlovc.com/perspective/2025-the-state-of-generative-ai-in-the-enterprise/) and cheaper open-weight models close the capability gap [\[2\]](https://epoch.ai/data-insights/open-closed-eci-gap), choosing the right model for each task becomes increasingly valuable. Baseten Router chooses between models based on their expected performance and cost for each coding task. The goal is frontier performance while paying frontier prices only when the task calls for it.

Baseten Router cuts costs by 72% compared to using Opus 5.5 for every task while remaining within 0.8 percentage points of its pass rate, measured on a test set spanning DeepSWE, SWE Atlas QnA, and Terminal-Bench. Adjusting the routing threshold lets users choose various trade-offs along the cost-performance Pareto frontier.

Among the commercial routers available - including those from Fireworks [\[3\]](https://fireworks.ai/blog/introducing-firerouter-with-opus), Together AI [\[4\]](https://www.together.ai/blog/together-link-frontier-quality-open-models-in-the-harness-you-already-use), and Applied Compute [\[5\]](https://www.appliedcompute.com/platform/shift-1?_gl=1*1h4hfr7*_gcl_aw*R0NMLjE3ODgyNzk1ODguQ2owS0NRanc3OW5VQmhDZ0FSSXNBRFNIa2EzM0ZfaUlZdHQtUUZFd1NFUkhsNkFJckJYWV82bjlTMmZoZjlxbHl1N3dvTEQxV095QlI3Y2FBZ1l6RUFMd193Y0I.*_gcl_au*MjA5MjgzNzgzNi4xNzkxNDc0NTUy) \- Baseten Router is the only router we evaluated that beats random routing on these benchmarks at the same cost by a statistically significant margin.

!Every Baseten point sits above the band where 90% of random routers fall and ahead of the other commercial routers (OpenRouter, FireRouter, Together, Applied Compute), so you get close to Opus-level accuracy at about a quarter of the cost.Every Baseten point sits above the band where 90% of random routers fall and ahead of the other commercial routers (OpenRouter, FireRouter, Together, Applied Compute), so you get close to Opus-level accuracy at about a quarter of the cost.

We’re working closely with design partners to specialize Baseten Router for their production coding workloads and prove out performance in the real world. Early feedback is promising, and we will share those results soon.

> Almost everyone at Opendoor uses AI, and most people don’t want an opinion on which model to pick. Auto routing sends them to the right model automatically, so they stay fully empowered and we stop paying frontier prices for work that doesn’t need it. > > \- Chintan Parikh, Head of Engineering, Opendoor

This post walks through how we built competitive performance into Baseten Router and how we continue to refine our approach.

How we built Baseten Router

Formulating the problem

From the input prompt, Baseten Router decides whether the coding task should be completed by a frontier closed-weight model or a cheaper open-weight model. In these experiments, we route between Opus 5.5 and GLM 5.3 Flash. We chose GLM 5.3 Flash because it is a strong coding model that is approximately 80% cheaper than Opus 5.5 across relevant workloads. Our approach can be extended to any model combination in any harness supported by Baseten Grid.

Our aim is to build a model router that decides between two models and gives a better trade-off between success rate and cost than either model alone. We formulated routing as choosing the model with the highest score, where the score is the predicted success rate minus a penalty proportional to the predicted cost:

πλ(x)=arg⁡max⁡m∈{Opus,GLM}p^m(x)−λc^m(x)\\pi\_\\lambda(x)=\\arg\\max\_{m\\in\\{\\text{Opus,GLM}\\}}\ \\hat p\_m(x)-\\lambda\\,\\hat c\_m(x)πλ​(x)=argm∈{Opus,GLM}max​p^​m​(x)−λc^m​(x)

For a given coding task xxx, p^m\\hat p\_mp^​m​ and c^m\\hat c\_mc^m​ are learned predictions of model m’s success rate and cost in USD on the task, respectively, and λ\\lambdaλ is a configurable weight that controls the trade-off. Intuitively, this favors Opus when its higher predicted success justifies its additional cost.

!Illustrative example of Baseten Router, which currently routes between Opus 5.5 and GLM 5.3 Flash.Illustrative example of Baseten...