How to Use Claude Code in 2026 (and Route It Cheaper)

Claude Code is Anthropic's command-line coding agent — it reads your repo, plans, edits files, and runs commands from the terminal. This guide covers installing and setting it up, the workflow that gets the most out of it, the cost reality of running a flagship agent on big tasks, and the part most guides skip: how to route it to cheaper models with claude-code-router or a gateway so a large job doesn't run up a flagship-priced bill.

What Claude Code is

Claude Code is Anthropic's command-line coding agent. You run it in a project directory and it reads the codebase, plans changes, edits files, and runs commands directly from the terminal — an agentic alternative to pasting snippets into a chat window. By default it's powered by Claude's flagship Opus models, which is why it's capable and also why it can get expensive on large tasks.

Install & set up

1. Install the CLI — e.g. npm install -g @anthropic-ai/claude-code (or the official installer per Anthropic's docs).
2. Run it in your project: claude
3. Authenticate with a Claude subscription, or an Anthropic API key: export ANTHROPIC_API_KEY=...
4. Approve as it goes — Claude Code asks before editing files or running commands, so you stay in control.

Using it well

Describe what you want in plain language — "add input validation to the signup form and write tests," "find and fix the failing test," "refactor this module to use the new API." Claude Code proposes a plan, edits the relevant files, and can run your tests or commands, iterating until the task is done. It's at its best on multi-file, repo-aware tasks where a chat window would lose context: end-to-end features, cross-module refactors, and debugging that spans several files. Give it a clear goal and let it read the project rather than pasting snippets.

The cost problem

Because Claude Code defaults to Opus, a large agentic task — reading lots of files, multiple iterations — can consume a lot of tokens at flagship rates ($5/$25 per million). For heavy users, a month of intensive use can run into the hundreds or low thousands of dollars. On a heavy coding-agent workload (modeled at 100M input + 20M output tokens/month), all-flagship usage is around $1,000/month. The fix isn't to stop using it; it's to route the easy parts to cheaper models and keep the flagship for the hard steps.

How claude-code-router works

claude-code-router is an open-source proxy that sits between Claude Code and the model providers. Claude Code talks to it as if it were Anthropic; the router then decides which model actually handles each request — a cheap model (DeepSeek, Qwen) for routine calls, a flagship for the hard ones — based on rules you configure. It works by pointing Claude Code's base URL at the router, so no change to how you use the CLI; only the backend changes.

Route it cheaper (worked config)

Two ways to cut Claude Code's cost without losing quality on the hard steps:

• claude-code-router — the open-source proxy above, with per-rule model selection.
• Point it at a gateway — set ANTHROPIC_BASE_URL to a gateway's endpoint so Claude Code's calls run through it, where you apply price routing and failover across models.

# Route Claude Code through a gateway (or claude-code-router) instead of direct
export ANTHROPIC_BASE_URL="https://www.datallmlab.com/v1"
export ANTHROPIC_API_KEY="$DATALLMLAB_API_KEY"
claude   # now its requests go through the gateway, where routing applies

The savings

Routing the easy majority of an agent's work to a cheap model and reserving a flagship for hard steps is the same cost-routing pattern that cuts general LLM spend by 60%+:

Tips to keep it cheap & effective

• Route by difficulty — cheap model for reads and small edits, flagship for the hard reasoning steps. Most calls are routine.
• Scope the task — a clear, bounded goal means fewer iterations and fewer tokens than a vague prompt that wanders.
• Keep the repo tidy for context — good structure and docs let a cheaper model succeed on more of the work.
• Watch the loop — agents can spiral on a stuck task; cap iterations or step in rather than burning tokens.

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