# What is an LLM router? Authors: AutoRouter Published: 2026-10-23T02:20:02.885Z Reading time: 8 min Canonical: https://autorouter.com/blog/what-is-an-llm-router Publisher: AutoRouter, the Mac app for Codex and Claude Code (https://autorouter.com) > An LLM router is software that sits between an application and several large language models and decides which model handles each request, usually to lower cost or keep requests flowing without giving up quality. Routers range from gateways that pick a provider by price and uptime to rule sets, learned classifiers, cascades and plan-aware routers. AutoRouter, the Mac app for Codex and Claude Code, is a plan-aware router and is unrelated to LiteLLM's Auto Router or OpenRouter's Auto Router. An LLM router is software that sits between an application and several large language models and decides which model handles each request. Most routers aim to lower cost without losing answer quality, or to keep requests flowing when a provider fails. [AutoRouter, the Mac app for Codex and Claude Code](https://autorouter.com), is one kind of LLM router: it picks a model for each turn in those two apps, after the person's own rules, and uses the ChatGPT and Claude plans they already pay for first. Several products with similar names, including LiteLLM's Auto Router, OpenRouter's Auto Router and Cursor's Auto, are made by other companies and are unrelated to AutoRouter. AutoRouter wrote this post. Claims about other products come from their own docs, linked where used and read on October 22, 2026. ## What does an LLM router do? An LLM router takes a request addressed to one model name, chooses which model or provider answers it, and returns the answer to the app. You call [OpenRouter's Auto Router](https://openrouter.ai/docs/guides/routing/routers/auto-router) by sending `openrouter/auto` as the model, for example, and you deploy [Microsoft Foundry's model router](https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-router) like any single model (October 22, 2026). The choice can rest on the prompt's text, how hard the task looks, price, availability, the user's rules or the user's subscriptions. ## What kinds of LLM routers are there? There are five common kinds: gateways, rule-based routers, learned or classifier routers, cascades and plan-aware routers. Many products combine two or more. | Kind | How it picks | What it optimizes | | --------------------- | ------------------------------------------------------------------------------------- | --------------------------------------- | | Gateway | Sends a named model's request to one of several providers, by price, load or failures | Uptime and price per token | | Rule-based | Fixed rules, such as a keyword, a mode or a context length | Predictable choices you control | | Learned or classifier | A trained or heuristic classifier predicts which model will answer well enough | Cost at a target quality | | Cascade | Tries a cheaper model, scores the answer, and moves up when the score is too low | Cost, with extra calls on hard requests | | Plan-aware | Weighs which models your subscriptions already cover against per-token prices | Use of plans you already pay for | **Gateways** usually keep the model you asked for and change where it runs. [LiteLLM's Router](https://docs.litellm.ai/docs/routing) adds retries, fallbacks and least-busy, latency-based or cost-based strategies, and [OpenRouter](https://openrouter.ai/docs/guides/routing/provider-selection) favors providers without recent outages, then the lowest price (October 22, 2026). **Rule-based routers** follow rules someone wrote. Claude Code's [`opusplan` setting](https://code.claude.com/docs/en/model-config) uses Opus in plan mode and Sonnet for execution (October 22, 2026). Rules are easy to predict, and they cover only the cases someone wrote down. **Learned and classifier routers** predict which model will answer well enough. [RouteLLM](https://arxiv.org/abs/2406.18665) (June 2024) trained routers on human preference data, and its authors [reported](https://lmsys.org/blog/2024-07-01-routellm/) cost reductions of over 85% on MT Bench, 45% on MMLU and 35% on GSM8K against using only GPT-4, at 95% of GPT-4's performance. [Hybrid LLM](https://arxiv.org/abs/2404.14618) (ICLR 2024) reported up to 40% fewer calls to the large model with no drop in response quality. [Microsoft Foundry's model router](https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-router) and [Amazon Bedrock intelligent prompt routing](https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-routing.html) are products of this kind (October 22, 2026). **Cascades** try a cheaper model first. [FrugalGPT](https://arxiv.org/abs/2305.05176) (May 2023) scores each answer and calls the next model only when the score falls below a threshold; its authors reported matching GPT-4 with up to 98% lower cost on their tasks. A hard request can pass through several models before it is answered. **Plan-aware routers** know which subscriptions you already pay for, such as a ChatGPT or Claude plan, and which models each one covers, and weigh that coverage against per-token prices when they pick. AutoRouter is one. In Codex, OpenAI models run on the ChatGPT plan; in Claude Code, Claude models run on the Claude plan; other models are paid per use. ## What are the trade-offs of using an LLM router? Every LLM router accepts some risk to quality, latency or caching in exchange for lower cost or better uptime. - **Quality.** A misjudged request goes to a model that can't do it well. [Amazon](https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-routing.html) says its routing may not suit specialized use cases and depends on its training data, and [Microsoft's](https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-router) Balanced mode accepts any model within about 1% to 2% of the best one's quality for a prompt, then picks the cheapest (October 22, 2026). For coding, see [does model routing make your results worse?](/blog/does-model-routing-hurt-quality) - **Latency.** A classifier adds a step before each request, and a [cascade](https://arxiv.org/abs/2305.05176) can call several models in a row. - **Caching.** Each model keeps its own prompt cache. [Claude Code's docs](https://code.claude.com/docs/en/prompt-caching) say the request after a model switch gets no cache hits, and [Anthropic](https://platform.claude.com/docs/en/build-with-claude/prompt-caching) bills a cache read at a tenth of the base input price or less (October 22, 2026), so frequent switching raises input cost. - **Context window.** [Microsoft](https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-router) says its router's effective context window is that of its smallest model unless you limit the pool (October 22, 2026). - **Cost.** Savings depend on how much work a cheaper model can do, plus any router fee. [OpenRouter's Auto Router](https://openrouter.ai/docs/guides/routing/routers/auto-router) adds no fee of its own (October 22, 2026); AutoRouter bills paid models at the provider's price plus 10% and earns nothing on plans. ## Is AutoRouter the same as LiteLLM's or OpenRouter's Auto Router? No. AutoRouter is a Mac app by Fractal Works, Inc. for Codex and Claude Code, and the products below are built by other companies for other setups. | Name | What it is | How you use it | | --------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------ | | [AutoRouter](https://autorouter.com) | Mac app that adds 20+ models to Codex and Claude Code and picks one for each turn | Pick AutoRouter in the Codex model menu, or with `/model` in Claude Code | | [LiteLLM Auto Router](https://docs.litellm.ai/docs/tutorials/claude_code_autorouter) | LiteLLM proxy feature, in beta, that sorts each request into a tier mapped to a model | `auto_router/complexity_router` in your LiteLLM config | | [OpenRouter Auto Router](https://openrouter.ai/docs/guides/routing/routers/auto-router) | API model ID that classifies the prompt's task type and ranks models by what OpenRouter users spend on it | Send `openrouter/auto` as the model | | [Cursor Auto](https://cursor.com/docs/cursor-router) | Auto option in Cursor's model picker; on Teams and Enterprise plans, Cursor Router classifies each agent request | Choose Auto in Cursor | For routers that work with Codex and Claude Code, see [model routers for Claude Code and Codex, compared](/blog/model-routers-claude-code-codex). ## Where does AutoRouter fit among LLM routers? AutoRouter is a plan-aware router that applies the person's rules first and makes every model clear a quality bar for the task before price counts. For each turn: 1. If one of your rules applies, it decides the turn, for example "Use Claude Opus 5.5 for UI work." 2. Otherwise AutoRouter works out how hard the turn is and only considers models that clear the quality bar for it. 3. Hard work goes to the most capable model your plans cover, and to credits only when none is strong enough. 4. Everything else goes to the model that costs you least, often your plan or a low-cost open model. Unclear work is never treated as a quick task. In the landing page's illustrations, a change across six files goes to Claude Opus 5.5 on the Claude plan, and a commit message goes to a low-cost open model so the plan isn't touched. Every turn shows its model, what it cost and why, and choosing a specific model in the picker sends turns straight to it. AutoRouter sometimes misjudges how hard a task is, and you can rate the pick when it does. It works with Codex (the app and the CLI) and the Claude Code CLI on macOS 14 or later, and it is in beta. More in [how AutoRouter picks a model for each turn](/blog/how-autorouter-picks-a-model). ## Questions ### Is an LLM router the same as an LLM gateway? A gateway is the proxy that forwards requests to providers, and a router is the logic that chooses the model; many gateways include one. LiteLLM's proxy offers both [load balancing](https://docs.litellm.ai/docs/routing) and [Auto Routing](https://docs.litellm.ai/docs/proxy/auto_routing), while [Cursor's Auto](https://cursor.com/docs/cursor-router) is a router inside an app. ### Does an LLM router lower answer quality? It can, whenever it sends a request to a model that can't handle it. RouteLLM's savings were [measured](https://lmsys.org/blog/2024-07-01-routellm/) at 95% of GPT-4's benchmark performance (July 1, 2024). Unless one of your rules names a model, AutoRouter makes every model clear a quality bar for the task before price counts, and it sends unclear work to a model that can do it well. ### Does AutoRouter publish benchmarks? No, AutoRouter doesn't publish benchmarks yet. Ratings from people and tests of each new model as it becomes available retune its routing, and the AutoRouter team reviews real routing decisions. ### What does AutoRouter cost? AutoRouter Free is $0 a month and routes across your ChatGPT and Claude plans. Pro is $20 a month with $20 of included usage each month and adds every other model; paid models are billed at the provider's price plus 10%. ## Sources - [AutoRouter](https://autorouter.com) - [Ong et al., RouteLLM: Learning to Route LLMs with Preference Data](https://arxiv.org/abs/2406.18665), arXiv, June 26, 2024, revised February 23, 2025 - [LMSYS: RouteLLM](https://lmsys.org/blog/2024-07-01-routellm/), July 1, 2024 - [Ding et al., Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing](https://arxiv.org/abs/2404.14618), ICLR 2024, arXiv, April 22, 2024 - [Chen, Zaharia and Zou, FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance](https://arxiv.org/abs/2305.05176), arXiv, May 9, 2023 - [LiteLLM: Router, load balancing and fallbacks](https://docs.litellm.ai/docs/routing), read October 22, 2026 - [LiteLLM: Auto Routing](https://docs.litellm.ai/docs/proxy/auto_routing), read October 22, 2026 - [LiteLLM: Auto Router with Claude Code and Claude Desktop](https://docs.litellm.ai/docs/tutorials/claude_code_autorouter), read October 22, 2026 - [OpenRouter: Auto Router](https://openrouter.ai/docs/guides/routing/routers/auto-router), read October 22, 2026 - [OpenRouter: Provider selection](https://openrouter.ai/docs/guides/routing/provider-selection), read October 22, 2026 - [Cursor: Cursor Router](https://cursor.com/docs/cursor-router), read October 22, 2026 - [Microsoft Learn: Model router for Microsoft Foundry concepts](https://learn.microsoft.com/en-us/azure/foundry/openai/concepts/model-router), updated October 19, 2026, read October 22, 2026 - [AWS: Understanding intelligent prompt routing in Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-routing.html), read October 22, 2026 - [Claude Code: Model configuration](https://code.claude.com/docs/en/model-config), read October 22, 2026 - [Claude Code: How Claude Code uses prompt caching](https://code.claude.com/docs/en/prompt-caching), read October 22, 2026 - [Anthropic: Prompt caching](https://platform.claude.com/docs/en/build-with-claude/prompt-caching), read October 22, 2026 ## Read next - [How to write model routing rules in plain English](https://autorouter.com/blog/plain-english-routing-rules.md): AutoRouter, the Mac app for Codex and Claude Code, lets you write routing rules in plain English, such as "Use Claude Opus 5.5 for UI work." When one of your rules applies, it decides the turn, and your rules come before AutoRouter's recommended ones. Choosing a specific model in the picker sends turns straight to that model instead. - [Which model to use for which coding task](https://autorouter.com/blog/which-model-for-which-task.md): Use a frontier model such as Claude Opus 5.5 or GPT-6 Astra for multi-file refactors, hard bugs and UI built from a design. Use a strong model such as Claude Sonnet 5.5 or GPT-6.1 Sol for tests and code review, and a fast model such as Claude Haiku 5.5 or GPT-6 Luna for renames, commit messages and one-line shell commands. AutoRouter, the Mac app for Codex and Claude Code, makes that choice on every turn. - [How to send quick coding tasks to a cheaper model](https://autorouter.com/blog/quick-tasks-on-cheaper-models.md): Commit messages, renames and one-line shell commands don't need a frontier model. At list prices on October 22, 2026, Claude Haiku 5.5 costs a fortieth of Claude Opus 5.5 per uncached token on prompts up to 100,000 tokens, and GPT-6 Luna costs a hundredth of GPT-6 Astra. You can run those turns on a small model yourself in Claude Code or Codex, or let AutoRouter, the Mac app for Codex and Claude Code, send each quick task to the cheapest capable model, which is often your ChatGPT or Claude plan or a low-cost open model. Tags: [Model routing](https://autorouter.com/blog/tags/model-routing.md), [AutoRouter](https://autorouter.com/blog/tags/autorouter.md), [Guides](https://autorouter.com/blog/tags/guides.md) Drafted with AI from the sources linked above, then checked and edited by the AutoRouter team.