Chat Compatibility Still Needs Provider Awareness

An OpenAI-compatible chat completions endpoint lets an application use a familiar request and response structure while accessing models from different providers. That reduces integration work, but compatibility is not a promise of identical behavior. The practical task is to separate the shared interface from the capabilities, limits, and billing rules behind it.

Step one establishes the endpoint's actual contract

Start by confirming that the service supports chat completions, rather than assuming every OpenAI interface is interchangeable. Chat completions and the Responses API are distinct interfaces. Support for one does not automatically establish support for the other.

The shared contract usually includes a model identifier, a sequence of messages, and generation controls. Optional features can include streaming, tool definitions, and structured output. Treat those features as capabilities to verify for each model, not benefits guaranteed by the word compatible.

Step two connects your client to the relay

Your client needs the service's API base and an appropriate API key. With an OpenAI SDK, these are commonly client settings, allowing much of the surrounding application logic to remain unchanged. Keep credentials outside source code, and distinguish authentication failures from account balance, permission, and rate-limit errors.

BestRelay publishes this walkthrough as a quality-first API relay. It forwards each request to the official model named in that request, without substitution, across providers including Claude, GPT, Gemini, DeepSeek, Kimi, GLM, Qwen, and Grok. It works with OpenAI SDKs and development tools including Claude Code, Codex, and Gemini CLI, although each tool's connection settings and supported workflows still matter.

Step three makes the model choice explicit

Select the exact model identifier listed by the service. A display name such as Gemini 2.5 Flash should not be assumed to be the identifier accepted in a request. Confirm the accepted identifier, context limit, supported input types, and available controls before building around it.

This distinction matters when switching providers. A familiar parameter may be unsupported, interpreted differently, or restricted for a particular model. Begin with a minimal request and add optional settings only after confirming that they work.

Step four turns conversation history into input

The messages describe the conversation the model should process. User messages supply requests, assistant messages preserve earlier replies, and supported instruction roles establish behavioral guidance. Role support and instruction handling can vary, so verify them rather than assuming identical priority rules everywhere.

In the usual stateless chat completions workflow, your application sends the relevant history with each request. The endpoint does not automatically remember earlier calls. Retaining every turn increases input size, so trim or summarize older material carefully while preserving constraints and essential facts.

Step five separates generated text from execution

For a nonstreaming request, inspect the returned choices, message content, completion status, and usage information. Do not assume every successful response contains a finished paragraph. A response may instead contain a tool call or stop because an output limit was reached.

With streaming, process incremental events rather than treating each fragment as a complete message. Accumulate text and tool arguments, handle interruption, and inspect completion signals. If the model requests a tool, your application validates and executes the operation, then returns its result through the supported conversation format. The model's request is not authorization to perform an action.

Step six verifies costs and failure recovery

Test unsupported parameters, timeouts, rate limits, and interrupted streams before production. Use bounded retries with backoff for appropriate transient failures. Retrying after an uncertain outcome can create duplicate work or additional charges, so make consequential downstream operations safe to repeat.

BestRelay bills pay-as-you-go using token counts reported by the provider, with cache reads and writes priced separately. Inspect usage records rather than estimating cost from visible text alone. Members also earn a referral commission when people they invite top up; that is separate from request accounting. Create a BestRelay account to test a small integration.

The reliable outcome is not merely a successful first response. It is a client that preserves model selection, handles each response type, checks compatibility explicitly, and reconciles actual usage with expected cost.