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OpenAI Chat Completions: Fast Developer Guide

What the chat completions format actually is

If you are busy and just want your app to work, think of the OpenAI-compatible chat completions format as a standard contract: you send a JSON request to a chat endpoint, and you get back a structured assistant reply. The key benefit is portability. The same request shape works with the OpenAI SDK, many third-party relays, and tools that already speak OpenAI’s API language.

For developers using 59API, that portability matters even more. You get cheap pay-as-you-go access to Claude models and GPT models through a single OpenAI-compatible surface, with the base URL at https://api.59api.com. That means fewer integration changes, less vendor lock-in, and no need to rewrite your app when you switch models.

The minimum request you need

A basic chat completions request usually contains three things: a model name, a messages array, and your sampling settings. In practice, your app sends a POST request to /v1/chat/completions.

• model: the model identifier you want to use.
• messages: the conversation history as a list of role/content objects.
• temperature or max_tokens: controls creativity and output length.

The messages array is the heart of the format. Each item usually looks like a role and a content string. The most common roles are system, user, and assistant.

• system: sets behavior, tone, or policy.
• user: the actual prompt from your app or end user.
• assistant: prior model responses, useful for multi-turn chats.

A simple request might ask the model to act as a concise code reviewer in the system message, then provide a user prompt with a stack trace, error output, or a short product question.

How to think about roles and context

Most integration bugs come from misunderstanding the messages array. The model does not magically know your app state. If you want continuity, you must send the relevant conversation history each time. For a support bot, that may mean the last few turns plus the current user message. For a coding assistant, that may mean the current file, error logs, and a short instruction in the system role.

Keep the system message stable and precise. Put changing user content in the user role. If your app inserts previous assistant output, make sure it is truly part of the conversation history and not copied in as instructions.

What the response looks like

The response is structured, not just plain text. The important part is usually inside choices[0].message.content. You will also often see metadata like the request id, model used, and token usage.

• choices: one or more candidate completions.
• message.content: the generated answer text.
• usage: prompt, completion, and total token counts.

That usage block is useful for tracking cost. If you are trying to keep AI spend under control, token visibility is essential. With 59API, that matters because the service is designed for cheap, pay-as-you-go usage, so you can test, iterate, and ship without committing to a large upfront plan.

A quick practical setup

To wire this up fast, point your OpenAI SDK or compatible client at the 59API base URL, then provide your API key. After that, keep your existing chat completions code path and only change the endpoint and credentials. The request structure stays familiar, which is especially handy if you are using Claude Code, Codex, or an existing OpenAI SDK integration.

• Set the base URL to https://api.59api.com.
• Use your 59API key in the standard authorization header.
• Send the same chat/completions payload your app already expects.
• Pick the model that fits the task, such as a faster model for autocomplete or a stronger one for deeper reasoning.

If you want streaming, add the stream flag and process incremental deltas as they arrive. Streaming is ideal for chat UIs, terminal tools, and long reasoning tasks because users see output sooner.

Common mistakes to avoid

Busy teams often trip over a few predictable issues. First, do not confuse chat completions with plain text completion APIs; the message structure matters. Second, do not drop conversation history if the task depends on context. Third, watch your max token settings so the model does not stop too early. Finally, always log failures from your client library, because a malformed role or missing field is usually easy to fix once you inspect the exact payload.

Why 59API is a practical choice

If your goal is to move fast and keep costs low, 59API is a strong fit. It gives you access to native, official-quality models without a downgrade layer, so you are not trading quality for price. It is also among the cheapest relays available, supports both Claude and GPT families, and adds a referral rebate that can reduce your effective spend even further.

For a quick-start workflow, that combination is hard to beat: familiar OpenAI-compatible requests, broad model access, low pay-as-you-go cost, and minimal code changes. If you are ready to test it in your own stack, sign up and point your client at https://api.59api.com.

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