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Route every prompt through TypeSafe Jev and send it to the cheapest model that can handle it. Cut LLM token costs and latency — wire it up in minutes. Token Cut helps developers cut AI costs by automatically routing every request to the lowest-cost model capable of handling it. Powered by Jev, it understands each task first, then selects the right model across providers like OpenRouter, fal.ai, Replicate, and Kie.ai. One API, smarter routing, less wasted spend — without sacrificing quality.
Your user says:
“Hi.”
Your app: GPT-5.6.
“Translate this sentence.”
Your app: GPT-5.6.
“Extract 3 fields from this JSON.”
Your app: GPT-5.6.
Your API bill at the end of the month: 💀
LOL.
That’s basically why I built Token Cut.
We’re using expensive models for way too many tasks that simply don’t need them.
The obvious answer is: “Just use cheaper models.”
But which one?
There are hundreds of models across OpenRouter, fal.ai, Replicate, Kie.ai and other providers. Prices change. New models launch. And a model that’s great for one task might be terrible for another.
I didn’t want to maintain 100 if/else rules just to decide which model gets a prompt.
So I built Token Cut.
How it works is pretty simple:
Request → Jev judges the task → Token Cut picks the lowest-cost capable model → Done.
Simple question? Cheap model.
Translation? Cheap model.
Coding and reasoning? Give it something stronger.
Actually difficult problem? Okay, bring out the frontier model. 🚀
The philosophy is simple:
Don’t use the smartest model you can afford.
Use the cheapest model that can do the job well.
And Token Cut isn’t trying to replace OpenRouter, fal.ai, Replicate or Kie.ai.
They give you access to models.
Token Cut helps decide which model you should actually use.
I also didn’t want to build another dashboard that tells you:
“Congrats! You spent $5,000 on LLMs last month.”
Thanks. Very helpful. 😂
By then, the money is already gone.
Token Cut tries to make the cost decision before the API call happens.
If a $0.0002 model can handle the request, why send it to something 20x more expensive?
Of course, cheaper isn’t always better.
The goal isn’t to save 90% and make your product 50% worse.
The goal is to stop wasting expensive intelligence on cheap problems.
Token Cut is still early, and I’m building it in public.
If you’re already spending real money on LLM APIs, I’d love to know:
How do you choose models today?
One model for everything?
A giant pile of routing rules?
Or have you already built your own router?
Would love your feedback 👇
tokencut.lol
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About Token Cut on Product Hunt
“Cut LLM Token Costs With Jev Model Routing”
Token Cut was submitted on Product Hunt and earned 2 upvotes and 1 comments, placing #153 on the daily leaderboard. Route every prompt through TypeSafe Jev and send it to the cheapest model that can handle it. Cut LLM token costs and latency — wire it up in minutes. Token Cut helps developers cut AI costs by automatically routing every request to the lowest-cost model capable of handling it. Powered by Jev, it understands each task first, then selects the right model across providers like OpenRouter, fal.ai, Replicate, and Kie.ai. One API, smarter routing, less wasted spend — without sacrificing quality.
Token Cut was featured in Productivity (663.5k followers), Artificial Intelligence (481.6k followers) and Tech (635k followers) on Product Hunt. Together, these topics include over 471.6k products, making this a competitive space to launch in.
Who hunted Token Cut?
Token Cut was hunted by Delvin Tang. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Token Cut stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Delvin Tang