How do I reduce my Cursor API bill?

Cursor Pro's $20/mo covers 500 fast requests — past that you pay OpenAI / Anthropic per-call rates directly, which is why heavy users end up at $50-$200/mo. The fix is to point Cursor's Custom API at CodeRouter and set model to 'auto'. CodeRouter detects what phase of coding the request is (planning, implementation, debugging, test generation, docs) and routes to the cheapest capable model per phase — Opus only for planning, DeepSeek V4 Pro for implementation and test generation, Haiku for docstrings. Same Cursor IDE, same keyboard shortcuts, 70-90% lower monthly bill. Setup takes 2 minutes — just change base_url to https://www.coderouter.io/api/v1 and paste your cr_ API key.

What is the cheapest API for Claude Code / Aider / Copilot?

There isn't a single 'cheapest API' — the cheapest model depends on what the coding agent is doing. For planning and architecture, you still want Claude Opus 4.8 or Sonnet 4.6. For implementation, DeepSeek V4 Pro ($0.44/$0.87 per 1M) and Kimi K2.6 are 15-40x cheaper than Opus with near-equivalent code quality. For test generation, DeepSeek V4 Pro or GLM-5.2 is 15-50x cheaper. For docstrings and simple formatting, Haiku 4.5 ($1/$5) or Gemini 2.5 Flash ($0.30/M output) is 15-250x cheaper. CodeRouter is the gateway that picks per request automatically — aim a single base_url at https://www.coderouter.io/api/v1 from Claude Code, Aider, Copilot (via LiteLLM), Cursor, Windsurf, or any OpenAI-compatible agent.

Does DeepSeek V4 Pro work as well as Claude Sonnet for coding?

For implementation and test generation phases — yes, DeepSeek V4 Pro matches Claude Sonnet 4.6 on HumanEval, MBPP, and LiveCodeBench within 1-3 points. For multi-file refactoring and architecture planning, Sonnet still has an edge on long-context reasoning. DeepSeek V4 Pro costs $0.44 input / $0.87 output per 1M tokens after the July 2026 price cut, vs Sonnet's $3/$15 — roughly 7-17x cheaper. The right answer for most coding agents is not 'pick one forever' but 'use DeepSeek V4 Pro for the implement/test phases and Sonnet or Kimi K3 for the plan/refactor phases.' That's phase-aware routing in practice — CodeRouter decides per request in ~10ms.

What is phase-aware LLM routing?

Phase-aware LLM routing classifies each coding-agent request by what phase of software work it represents — planning, implementation, debugging, testing, refactoring, or documentation — and routes it to the cheapest model that can handle that specific phase. A 'write unit tests for this function' request goes to DeepSeek V4 Pro ($0.87/M output). A 'refactor this multi-file feature and plan the migration' request goes to Claude Opus 4.8 ($25/M output). This is different from picking one model for everything, and different from OpenRouter-style model-selection (which still requires you to choose manually). CodeRouter's classifier runs in ~10ms on the server, so the agent never notices the extra hop.

CodeRouter vs OpenRouter — which saves more money on coding?

OpenRouter is a model marketplace — it gives you access to 300+ models behind one API key, but you still pick which model to send each request to. Most Cursor / Aider / Claude Code users default to the premium model (Opus, GPT-5) for everything and end up paying full price. CodeRouter is a phase-aware router — set model to 'auto' and we pick the cheapest capable model per request based on the coding phase. CodeRouter also adds things OpenRouter doesn't: coding-specific capability scores per model (implementation, debug, test, refactor), per-end-user attribution for SaaS agent builders, and built-in quota + top-up billing. For pure coding workloads, typical CodeRouter savings are 70-90% vs picking one model on OpenRouter.

Will CodeRouter break my Cursor / Aider / Claude Code agent?

No. CodeRouter exposes a standard OpenAI-compatible chat completions endpoint (POST /api/v1/chat/completions) with the same request and response format your agent already uses — including streaming, tool use, and function calling. We implement the same JSON schema and stream format, so Cursor, Aider, Claude Code, Cline, Continue.dev, Windsurf, OpenClaw, and any LiteLLM-wrapped client work unmodified. If a routed model fails, the fallback chain tries up to 2 alternates automatically (on 429, 500-504, timeouts, missing keys). You can also pin an explicit model instead of 'auto' any time.

How do I set up CodeRouter with Cursor in 2 minutes?

1) Sign up free at https://www.coderouter.io/login and copy your API key (starts with cr_). 2) In Cursor, open Settings -> Models -> OpenAI API Key, and under 'Override OpenAI Base URL' paste https://www.coderouter.io/api/v1. Paste your cr_ key in the API Key field. 3) Add 'auto' to the Custom Models list and select it as your active model. That's it — phase-aware routing is live. Aider users set OPENAI_API_BASE and OPENAI_API_KEY env vars to the same values. Claude Code users set ANTHROPIC_BASE_URL to https://www.coderouter.io/api/v1 and ANTHROPIC_API_KEY to the cr_ key. Full guide at https://www.coderouter.io/setup.

The CodeRouter MCP Server

Your coding agent hits an LLM API error, pastes it into this MCP server, and gets back the verified cause, whether retrying helps, and the fix — without you opening a browser. Backed by the same curated database as the LLM API Error Reference: every entry is a real, verified error string. Free, no API key, stateless.

Install

Claude Code (one command):

claude mcp add --transport http coderouter https://www.coderouter.io/api/mcp

Any MCP client with JSON config (Cursor, Windsurf, custom agents):

{
  "mcpServers": {
    "coderouter": {
      "type": "http",
      "url": "https://www.coderouter.io/api/mcp"
    }
  }
}

Transport: MCP Streamable HTTP (JSON-RPC over POST). Endpoint: https://www.coderouter.io/api/mcp

Tools

lookup_llm_error

Pass an error message (exact string preferred — that is how the database is indexed), a status code, or a symptom description. Returns the best matches with meaning, retryability, fix, and a deep-dive link.

list_llm_errors

Lists the full database, optionally filtered by provider (openai, anthropic, deepseek, gemini, cursor…).

What's in the database

16 verified entries as of 2026-09, covering OpenAI (quota vs rate limits, context overflow), Anthropic (billing 400s, 529 Overloaded), the DeepSeek reasoning_content 400 family, Gemini / AI Studio endpoint and quota errors, and Cursor / Claude Code / proxy-layer configuration failures. The bar for inclusion: the exact error string, the provider/tool combination, and a reproducible cause — no invented errors.

[DeepSeek 400] The reasoning_content in the thinking mode must be passed back to the api
[cc-switch + DeepSeek 400] cc switch local proxy failed while handling codex endpoint /responses ... upstream_status:…
[DeepSeek] deepseek returned tool calls without replayable thinking content; continuing with degraded…
[LangChain + DeepSeek 400] LangChain agent loop fails with reasoning_content 400 on the second tool step
[Claude Code 401] HTTP 401 immediately after setting a custom base URL in Claude Code
[Anthropic 400] Your credit balance is too low to access the Anthropic API. Please go to Plans & Billing t…
[Anthropic 529] {"type":"overloaded_error","message":"Overloaded"}
[OpenAI 429] You exceeded your current quota, please check your plan and billing details.
…and more — see the full reference.

Why an MCP server for this

Most LLM API failures are translation-layer problems — proxies and history stores dropping fields the provider contract requires — and the fastest diagnosis is matching the exact string. Putting the reference behind MCP means the agent that hit the error can diagnose it in the same loop. If the error is not in the database, the server says so honestly instead of guessing. Found one we're missing? The reference page explains the contribution bar.