Route your prompts to the best LLM
Hey HN, we've just finished building a dynamic router for LLMs, which takes each prompt and sends it to the most appropriate model and provider. We'd love to know what you think! Here is a quick(ish) screen-recroding explaining how it works: https://youtu.be/ZpY6SIkBosE Best results when training a custom router on your own prompt data: https://youtu.be/9JYqNbIEac0 The router balances user preferences for quality, speed and cost. The end result is higher quality and faster LLM responses at lower cost. The quality for each candidate LLM is predicted ahead of time…
Route your prompts to the best LLM launched on May 22, 2024 with 298 votes, #148 of 4,539 launches that month and more than 95% of that year's launches. The site was not reachable when checked in September 2026.
In plain words
This product is a dynamic router that automatically directs each user prompt to the most suitable large language model from available providers. It uses a neural scoring function to predict which model will deliver the best results for each prompt, while balancing trade-offs between quality, speed, and cost. The router can be customized by training on a user's own prompt data, and it processes multiple LLMs across batches to optimize response quality and performance at lower cost. It is designed for developers and organizations that want to maximize efficiency when using multiple LLM services.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hey HN, we've just finished building a dynamic router for LLMs, which takes each prompt and sends it to the most appropriate model and provider. We'd love to know what you think! Here is a quick(ish) screen-recroding explaining how it works: https://youtu.be/ZpY6SIkBosE Best results when training a…
Does a similar job
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Prompt Engine – Auto pick LLMs based on your prompts2024 · jigsawstack.com · ▲93Nowadays, a common AI tech stack has hundreds of different prompts running across different LLMs. Three key problems: - Choices, picking from 100s of LLMs the best LLM for that 1 prompt is gonna be challenging, you're probably not picking the most optimized LLM for a prompt you wrote. - Scaling/Upgrading, similar to choices but you want to keep consistency of your output even when models depreciate or configurations change. - Prompt management is scary, if something works, you'll never want to touch it but you should be able to without fear of everything breaking. So we launched Prompt…
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Arch-Router – 1.5B model for LLM routing by preferences, not benchmarks2025 · ▲66Hi HN — we're the team behind Arch (https://github.com/katanemo/archgw), an open-source proxy for LLMs written in Rust. Today we're releasing Arch-Router (https://huggingface.co/katanemo/Arch-Router-1.5B), a 1.5B router model for preference-based routing, now integrated into the proxy. As teams integrate multiple LLMs - each with different strengths, styles, or cost/latency profiles — routing the right prompt to the right model becomes a critical part of the application design. But it's still an open problem. Most routing systems fall into two…
OpenRouter Model FusionApr 2026 · openrouter.ai · ▲144Hey HN, We've been building a multi-model API for LLMs called OpenRouter since we launched the Window AI extension in April this year [0]. The events of the last week make it clear the LLM landscape is unpredictable, so LLM aggregators are picking up interest. Unlike others, we're making a router on top of a public, explorable dataset. We recently built a way to rank and visualize this data, including the token counts we see going to and from different models, both open-sourced (like Llama, Mistral, finetunes, and variants) and closed-sourced (OpenAI, Anthropic). The API supports - 50+…
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Source: Show HN post ↗ · launch text quoted in part
