A Familiar Chat Interface Still Needs Careful Translation

One familiar interface can serve different models

An OpenAI-compatible chat completions endpoint gives applications a familiar way to send a conversation to a language model and receive its next reply. Instead of learning a completely different request format for every provider, developers can often reuse the same client library and much of the same application logic.

The important word is compatible. It describes an interface, not a promise that every model behaves identically or supports every feature. Think of it as a shared envelope: the address and packaging follow familiar rules, while the contents and delivery capabilities can still differ.

A request describes the conversation so far

A basic request names a model and supplies a list of messages. Each message has a role and content. User messages contain questions or instructions from the person using the application. Assistant messages represent earlier model replies. System or developer messages can establish application-level instructions, depending on the model and interface.

For an ordinary chat completions request, the application supplies the conversation context it wants the model to use. Sending a new question does not automatically give the endpoint access to every previous exchange. Your application usually keeps the history and includes the relevant parts again.

That makes conversation management a practical design task. Sending everything can increase cost and eventually exceed the model's context limit. Sending too little can make the assistant lose important details. A useful starting point is to retain recent exchanges and preserve essential facts in a compact summary.

Responses contain more than the assistant's words

A typical response includes one or more candidate completions, the assistant's message, and a finish reason. The finish reason helps distinguish a normal stopping point from an output limit or a request to use a tool. Usage information may also report input and output token counts.

Tokens are units of model processing, not a fixed number of words. Text, conversation history, and supported nontext inputs can contribute to usage in different ways. Avoid estimating a production bill from word count alone.

Streaming changes delivery rather than the basic task. Instead of waiting for a complete answer, the application receives incremental events and assembles them into a response. This can make a chat interface feel faster, but the application must handle interruptions and incomplete output.

Shared request shapes do not guarantee shared capabilities

Basic text chat is the easiest feature to move between compatible services. Tool calling, structured output, image input, reasoning controls, and caching deserve separate checks. A parameter accepted by one model may be unsupported, translated differently, or restricted by another.

Tool calling also does not mean the model executes your business logic. Typically, the model proposes a tool call, your application validates and performs the operation, and the result returns to the conversation. Authorization and safety checks remain your application's responsibility.

Before switching models, test representative conversations, unsupported parameters, error responses, and streaming behavior. A successful connection proves that requests can travel; it does not prove that the application still meets its quality requirements.

A relay centralizes access without removing provider differences

BestRelay, which publishes this article, is a quality-first API relay that forwards each request to the official model named in it, without substitution. Its coverage includes Claude, GPT, Gemini, DeepSeek, Kimi, GLM, Qwen, Grok, and more. That provides a shared access point, not identical behavior across those families.

BestRelay works with Claude Code, Codex, Gemini CLI, and any OpenAI SDK, although individual tools may require their own connection settings. Billing is pay-as-you-go using provider-reported token counts, with cache reads and writes priced separately. Members also earn a referral commission when people they invite top up; that program is separate from model execution.

Create a BestRelay account when you are ready to try the interface.

Start with one small conversation, inspect the response and usage, then add streaming or tools as needed. The shared interface reduces integration work; careful testing keeps that convenience from becoming a false assumption.