Launch HN: Tokenless (YC S26) – Automatic model switching to save money

The cost of AI tokens is top-of-mind for many. Companies like Uber and Salesforce have been complaining about blowing their yearly AI spend faster than expected.

Frontier models are amazing for dev work, but are so expensive. Open-source models are cheap and rapidly improving, closing the gap with frontier models, but aren’t quite there yet.

Tokenless gets you the best of both worlds–routing harder turns to smarter models only when needed, which keeps costs low.

Before Tokenless, I was doing a PhD at Princeton. While using coding/other agents, I constantly agonized over model choice, to make sure my AI spend was going as far as possible on my academic Cursor account.

At the same time, I was doing LLM research, and a small technique I developed while in recovery from NeurIPS submission season seemed to hit SOTA pretty fast. I was surprised that such simple ideas could do routing well.

We’ve been able to develop a version of the router that matches the performance of Claude Fable 5 at half the cost. The blog post on our website explores the technical details on how we did this (https://usetokenless.com/blog/building-tokenless/).

Highlights: - Our approach queries multiple models at once and uses their progress to make decisions (this technique is novel AFAIK, let us know if you know anyone else doing this). - Switching models doesn’t destroy the cache if the routing algorithm is aware of when the cache is hot/cold.

To come: - Adding Kimi K3, all other GPT efforts and more to the router

Go ahead and sign up on usetokenless.com and try using Tokenless with your agent, you’ll get $20 of free credit. Here’s a demo on how to use it: https://youtu.be/sjZWriclcls

Tokenless provides frontier-level intelligence for cheaper, so we’d love some feedback on how it feels to use, any corner cases that the router routes incorrectly, and whether you find the routing problem interesting!

Launch HN: Tokenless (YC S26) – Automatic model switching to save money
usetokenless.com

Not exactly. It can also make sense if the cache is hot to switch models. For example, we might currently be on Deepseek and the next task is SO HARD that it doesn't make sense to not use frontier (1 turn to crack versus 100 turns for Deepseek to crack). Likewise, the router may judge that it's likely that the next few set of tool-calls will be ridiculously easy, and so switching to Deepseek will save over time.

Remember that Deepseek can be something like 30x cheaper even cold.

There is a more fine-grained view of caching here, because each model can have a different amount of the prefix already cached as well (since we were switching a bunch) so even if the cache is somewhat warm it may make sense to switch.

That being said, whatever models we select to race at any point will ALL have their cache updated, so if they're chosen on the next race they'll be a lot cheaper.

Sounds like DigitalOceans Model synthesis (https://www.digitalocean.com/blog/model-synthesis) or AilinOne (https://ailin.guide/) ?

τ³-Banking is the only one which you show better accuracy and cheaper. If i'm reading the blog results right, for deepswe and terminalbench, you are worse+cheaper than frontier, and better+more expensive than just small models. Which is exactly what i would expect even for a router that switches at random.

Speaking of random routing, this would be a great ablation study as well. What about also if you route each request to a tiny 7B model classifier? Why is your approach SOTA?