TokenCut.lol
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.
A startup from The United Kingdom, founded by James Carter.
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.
A startup from The United Kingdom, founded by James Carter.
This page is designed to help you find out whether TokenCut.lol is good and if it is the right choice for you.
TokenCut is an AI model routing and cost optimization platform designed to help developers reduce LLM and generative AI API costs.
Powered by TypeSafe Jev, TokenCut analyzes each incoming task, determines its complexity and requirements, and routes it to the lowest-priced eligible model within your configuration. Simple requests can be handled by cheaper models, while reasoning-heavy or complex tasks can automatically be routed to more capable models when needed.
Instead of manually integrating and managing multiple AI providers, TokenCut provides one unified API for text, image, video, and audio models. Developers can define their preferred models and routing settings while TokenCut handles model selection and cost optimization automatically.
Whether you're building an AI SaaS product, chatbot, agent, content generator, or high-volume AI application, TokenCut helps reduce unnecessary model spending without requiring you to rewrite your application logic.
One API. Smarter model routing. Lower AI costs.
Listed in
Jev Model Routing
TypeSafe Jev analyzes each task and routes it to the most suitable model based on complexity and requirements.
Automatic Cost Optimization
Automatically selects the lowest-priced eligible model within your settings to reduce AI API costs without manual routing.
Unified Multimodal API
Access text, image, video, and audio models through one API with a consistent integration experience.
Token Cut is built specifically to reduce AI model costs without forcing developers to manually choose a model for every request.
With Jev-powered task classification and automatic model routing, simple requests can go to cheaper models while more complex tasks are routed to more capable ones. Developers keep control over which models are eligible, while Token Cut handles the routing automatically.
It also provides a single API for text, image, video, and audio, making it easier to manage multiple models and providers from one integration.
Token Cut uses TypeSafe Jev to understand the complexity and requirements of each task before choosing a model. Instead of sending every request to an expensive frontier model, it selects the lowest-priced eligible model within your settings. This makes model routing task-aware and cost-focused, while providing one unified API for text, image, video, and audio models.
Token Cut is primarily built for AI developers, indie hackers, SaaS teams, startups, and engineering teams that use multiple AI models or process a high volume of AI API requests.
It is especially useful for products where model costs grow with usage, such as AI agents, chatbots, coding tools, content generation platforms, AI SaaS products, and multimodal applications.
Token Cut started with a simple observation: not every AI request needs the most powerful and expensive model.
A simple classification, extraction, or factual question might be sent to the same frontier model as a difficult reasoning task, even though a much cheaper model could handle it perfectly well.
We built Token Cut around TypeSafe Jev to classify tasks before execution and automatically route them to the lowest-priced model capable of handling them. The goal is simple: use expensive models when they are actually needed, and stop overpaying when they are not.
Token Cut is built around TypeSafe Jev for task classification and intelligent model routing, combined with a unified API layer for accessing text, image, video, and audio AI models.
The routing system evaluates incoming requests, determines the appropriate task complexity, and selects the lowest-priced eligible model according to the developer's configuration.
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