Pre-Setup Checklist
- Confirm the provider offers an OpenAI-compatible API, not only a web chat product.
- Check whether it supports the GPT, DeepSeek, Qwen, Gemini or other coding model you need.
- Verify base URL, model name, API key, streaming output and error format.
- Confirm whether input and output tokens are billed separately and whether failed requests are charged.
- Review privacy policy, log retention, refund rules and support channels.
Common Configuration Structure
Exact variable names may change with Codex CLI versions and runtime style. Treat this as a common structure and follow current tool and provider docs first.
OPENAI_API_KEY="your-relay-api-key"
OPENAI_BASE_URL="https://example.com/v1"
OPENAI_MODEL="gpt-5-codex" Small-Balance Test Flow
- Start with a public sample repository or non-sensitive code snippet.
- Test code explanation, cross-file edits, long context, streaming output and error recovery.
- Record latency, success rate, billing, output quality and possible model substitution.
- Normalize token cost across providers before deciding on long-term use.
Selection Advice
GPT/Codex workflows care most about OpenAI-compatible behavior, model quality, context length, response speed and predictable billing. Low price is only one dimension and should not replace privacy and stability.
FAQ
Does a Codex CLI relay need OpenAI-compatible API support?
Usually yes. Codex CLI and most GPT coding tools are easiest to connect through OpenAI-compatible APIs, so verify base URL, model names, auth and streaming behavior.
Can low-cost GPT/Codex relays be used directly for production projects?
Not directly. First test with a small balance, non-sensitive code and a fixed test suite for stability, billing, model quality and privacy disclosures.
How should I compare Codex API relay prices?
Normalize every provider to cost per 1M input tokens and 1M output tokens, then compare failed-request billing, cache rules, minimum recharge and refunds.