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OpenAI Chat Completions Format: A Practical 2026 Guide

The OpenAI-compatible chat completions format gives applications a familiar way to send conversations to AI models and receive structured responses. In 2026, it remains useful for connecting existing SDK integrations to different providers. The essential distinction: compatible request syntax does not guarantee identical model behavior or support for every parameter.

This guide explains the request structure, response fields, streaming behavior, and checks that make an integration reliable.

Understand the endpoint and authentication

The conventional endpoint is POST /v1/chat/completions. Requests normally carry an Authorization: Bearer YOUR_API_KEY header and Content-Type: application/json. Keep the key on your server or in a secret manager; never embed it in browser JavaScript.

For 59API, the service host is https://api.59api.com. Configure your OpenAI SDK with your 59API key and the documented API base path. Check whether that base includes /v1: SDKs typically append /chat/completions, so a duplicated or missing version segment can produce a 404.

Build a valid messages array

A basic request contains model and messages. Use an exact model identifier supported by your provider, rather than assuming a marketing name is an accepted API value. A minimal request body looks like this; replace the model placeholder before sending:

{"model":"YOUR_SUPPORTED_MODEL_ID","messages":[{"role":"user","content":"Explain HTTP caching in three sentences."}]}

Each message has a role and content. Common roles have distinct purposes:

• system: Application instructions, such as tone or output requirements. Some models also support a developer role; check the target model's requirements.
• user: The person's question, document, or other input.
• assistant: A previous model response, included to preserve conversational context.
• tool: The result of an executed tool, linked to the corresponding tool call.

For ordinary text, content is a string. Multimodal requests may use an array of typed content parts, including text and image references. Support depends on both the relay and the selected model. Chat Completions requests are generally stateless: send the relevant conversation history with each request and trim older material when approaching the context limit.

Read responses without assuming text is present

A typical non-streaming response contains id, model, choices, and usage. For a straightforward text answer, read choices[0].message.content. Also inspect choices[0].finish_reason: stop usually indicates completion, length signals an output limit, and tool_calls indicates requested tool execution.

Content can be null when the assistant requests tools. Handle that case explicitly. When supplied, usage fields such as prompt_tokens and completion_tokens help track consumption, but your provider's billing records remain the source for actual charges.

Handle streaming and tools correctly

Set stream: true to receive Server-Sent Events. Text typically arrives through choices[0].delta.content, rather than message.content. Accumulate fragments, tolerate chunks without text, and recognize the terminal [DONE] marker. A network chunk can contain part of an event or several events, so use an SSE parser or your SDK's streaming iterator.

For tool calling, declare functions under tools with JSON Schema parameters. The model returns a proposed function name and serialized arguments; your application validates them, executes authorized operations, and sends back a tool message with the matching tool_call_id. Then request the assistant's next response. Streamed tool arguments also require assembly before parsing.

Validate compatibility and control costs

Test a basic text request, streaming, and any required tools before switching production traffic. Parameters such as max_completion_tokens, temperature, and response_format vary by model and provider. JSON output and strict schema enforcement are separate capabilities; validate structured results in your application.

Handle authentication failures, rate limits, and server errors separately. Use bounded retries with exponential backoff for transient failures. Limit output length, avoid repeatedly sending unnecessary history, and compare cost per completed task alongside latency and accuracy.

Where 59API fits

59API offers low-cost, pay-as-you-go access to native, official-quality Claude and GPT models without model downgrades. Its OpenAI SDK compatibility makes it practical for existing integrations, and it also supports Claude Code and Codex through their respective configurations. A referral rebate adds another potential saving.

Sign up for 59API and test a small representative workload to check response quality, feature support, and actual spend before scaling.

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