Introducing Claude Code Router: Smart Model Routing & Real-Time Cost Optimization
Claude Code Router redefines AI-powered development by intelligently orchestrating coding requests across a diverse ecosystem of large language models. Built as an open-source, production-ready Node.js CLI extension to Anthropic’s Claude Code, it transforms static model usage into a dynamic, context-aware routing engine. Rather than locking developers into a single LLM—regardless of task complexity, latency tolerance, or budget constraints—the router evaluates each coding request in real time and dispatches it to the optimal model based on purpose, performance profile, and cost efficiency. Supported providers include OpenRouter, DeepSeek, Ollama (for local inference), Google Gemini, and Volcengine—enabling seamless hybrid workflows that blend cloud scalability with on-device privacy and affordability.
Getting Started in Minutes
Installation is streamlined and developer-first. With Node.js and npm already installed, run npm install -g @anthropic-ai/claude-code to set up the base CLI, then add intelligent routing with npm install -g @musistudio/claude-code-router. This registers the ccr command globally. Launch your enhanced coding assistant with ccr code. For granular control, create or edit ~/.claude-code-router/config.json to define provider credentials, model availability, and role-based routing policies—including defaults for everyday tasks, dedicated “think” models for complex reasoning, lightweight “background” models for low-priority automation, and long-context specialists for repository-scale analysis. Once configured, all interactions remain intuitive: type as usual in Claude Code—and behind the scenes, the router makes intelligent, real-time decisions about where—and how—to execute each request.
Intelligent Capabilities, Engineered for Impact
Adaptive Model Routing: Automatically or manually route prompts using natural-language commands like /model openrouter,google/gemini-2.5-pro-preview, with support for fallback chains and load-aware balancing.
Role-Aware Model Assignment: Assign models not just by capability—but by *intent*: background workers for linting and scaffolding, high-reasoning models for architecture design, and long-context engines for full-stack debugging across massive codebases.
Real-Time Cost Intelligence: Monitor token usage, API pricing tiers, and latency metrics per provider—and dynamically prioritize lower-cost alternatives when quality thresholds are met (e.g., swapping Claude 3.5 Sonnet for DeepSeek-V3 on routine refactoring).
Extensible Ecosystem: Plug in multimodal tools (image understanding, web search), export structured logs for observability, and trigger CI/CD pipelines via GitHub Actions—using any configured model as your automated engineering co-pilot.
Zero-Lock-in Configuration: Full declarative control via JSON—no hidden abstractions. Define custom routing logic, rate-limiting rules, retry strategies, and even model-specific prompt wrappers—all within a single, version-controllable config file.
Where Smart Routing Delivers Real Value
Engineering teams scaling AI-assisted development without scaling cloud bills—routing simple PR summaries to economical models while reserving premium LLMs only for critical design decisions.
Open-source maintainers automating triage, documentation generation, and security scanning—leveraging local Ollama models for privacy-sensitive repos and cloud models for cross-platform compatibility checks.
DevOps engineers embedding AI into CI pipelines—dynamically selecting models based on job type (e.g., fast-response models for test feedback, high-context models for root-cause analysis of flaky builds).
Solo developers maximizing utility from limited API budgets—automatically downgrading to free-tier models during exploration mode, then upgrading on-demand for deep reasoning sessions.
AI research labs benchmarking model performance across coding tasks—routing identical prompts to multiple backends simultaneously and comparing outputs, latency, and cost per operation.