Pre-Setup Checklist
- Confirm the provider explicitly supports Claude Code, not only OpenAI-compatible APIs.
- Verify the API base URL, model names, auth headers and streaming behavior.
- Normalize pricing to cost per 1M input tokens and 1M output tokens.
- Check privacy policy, log retention, refund policy and support channels.
Common Environment Variables
Relay-specific variable names vary. Treat this as a structure example and follow current Claude Code and provider documentation first.
ANTHROPIC_AUTH_TOKEN="your-relay-api-key"
ANTHROPIC_BASE_URL="https://example.com/anthropic" Small-Balance Test Flow
- Use the minimum recharge or trial credit.
- Test normal Q&A, code reading, code edits and long-context behavior on a non-sensitive sample project.
- Record latency, error codes, billing and output quality.
- Use fixed prompts to check for possible model substitution.
- Only then consider adding the relay to your daily coding workflow.
Model Verification Approach
A single answer cannot prove model identity. Use a fixed test suite covering long-context recall, tool-use style, code reasoning, multilingual details and refusal boundaries, then compare repeated outputs.
FAQ
Is a Claude Code relay the same as an OpenAI-compatible relay?
Not exactly. Claude Code depends more on Anthropic-compatible endpoints, model names and streaming behavior. A provider that only supports OpenAI-compatible APIs may not work directly.
What is the biggest risk when using Claude Code through a relay?
The main risks are prompt and code logging, model substitution, unstable long-context behavior, balance loss and unclear upstream access. Sensitive repositories should prefer official APIs or compliant enterprise options.
How do I know whether a Claude Code relay is worth long-term use?
Review docs, pricing, privacy and support first. Then test multi-turn code reading, edits, long context and retries on a fixed project while recording billing and error rates.