TL;DR: No single LLM is optimal for every query and budget. This work unifies the scattered field of LLM routing into "five building blocks" and ships an open-source infrastructure bundling 16+ routers with a dedicated benchmark. Title: LLMRouter: Unified Infrastructure for Developing, Evaluating, and Deploying LLM Routers URL: Key points 🧩 Decomposes any router into 5 components: Context Encoder / Model Encoder / Scoring / Decision Rule / Learning Signal 📊 xRouteBench spans 5 tracks (Generic, Memory, Vision, TimeSeries, Personalized), 4,767 queries total 🛠️ A dense query-model matrix, built by dispatching every query to all 18 candidate models, serves as both training supervision and test bed 🚀 Learned routers beat the strongest fixed-model baseline (always pick the largest) by 14.6% relative ⚖️ No router dominates: RouterDC leads on accuracy but drops to 10th under tight cost constraints 🔁 Multi-turn routing adds cost without consistent gains (Router-R1 at just 22.3%) 👤 User conditioning helps, but the best design flips between simulated personas and real feedback A solid step that proves "the best router depends on task and budget" and lays a foundation for fair comparison. #LLM# #ModelRouting#

Model routing is having a moment thanks to the $7B Stripe acquisition of OpenRouter. But it's also increasingly important in enterprises. We talk to @glean CEO @jainarvind about why model routing helps control AI costs for organizations.

$ASAN Asana CEO: Model routing is becoming a margin lever “We've gotten rather good at… figuring out which types of tasks should go to which types of model… to both solve for quality and cost optimization.”

Building my model routing bot in Grok Bot, and looks like I'll need to make some changes after last night's announcement. So glad Cursor supports so many models, time to replace Luna and Terra, and there are sooooo many options to choose from!

The right model depends on the task. NVIDIA NeMo Switchyard helps developers route each agent workflow step across a chosen model pool based on their own quality, latency and cost criteria. Kari Briski joins @MTSlive to explain why agent workflows need model routing.

Introducing: Auto Private Model Routing on June Choose Low, Balanced, or High. June picks the right model for every request. Defaults to zero data retention and private models. Powered by @AskVenice. Free to try. Open source. MIT. Try June:

I think a lot about model routing, heck I even write a Substack just it, so yeah, I'm kinda obsessed. At a high level, I don't think most of the model routers out there make much sense for anyone but enterprise co's already paying api pricing to use services like Anthropic and OpenAI. And I think this is honestly, the target market for most of the companies releasing model routers today. So it makes sense, and yes, will definitely save enterprises money. If you're spending $500,000/year on a Claude Sub. And everyone is just using Opus High for everything, yup, putting some thought into model selection, even at API+ pricing, which is what most routers charge, is still a savings. But if you're using a subscription, at $100 or $200/mo, switching to a model router will dramatically increase what you pay, because you're not paying API pricing, you're paying a massively discounted price. This doesn't mean the model routing co's are doing anything wrong, they're just not solving a problem for you, they're solving a problem that big enterprise co's have. I've seen so many posts, and talked to so many people at big co's that say, it's all too overwhelming for them and their teams, so they tend to just see everyone using one model, usually the latest frontier model, and at High effort for everytihng. What will be interesting to see is over time will these companies just decide to outsource this decision logic to third party model routers, or will they use benchmark data, both public, and internal evals, to just give their teams some model routing decisions, i.e. use these three models, at these effort levels, etc. I think the biggest, unexplored territory right now is effort level. This is why I started @VulcanBench because I saw just about every benchmark just running evals with models at Max effort, and I knew for myself and my team, we used Medium effort more than any other effort level. But a model that does great at Max, might not be great at Medium, and another model might be better. And it could be completely different for Python code than Rust code, and medium-sized codebases vs. large. These days, I wake up every morning so excited to be doing research in this area. There is so much to discover. And that's my Monday brain dump, big week ahead, TGIM 🖖