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OpenAI Chat Completions Format Explained

Understanding the OpenAI-Compatible Chat Completions Format

If you are building an AI feature for the first time, the OpenAI-compatible chat completions format is one of the most useful API patterns to learn. It is the request and response structure used by many modern AI tools to send a conversation to a model and receive a generated reply. The best part is that once your app understands this format, you can often switch between GPT models, Claude models, and other compatible providers with very little code change.

59API is a good example of why this matters. It provides low-cost, pay-as-you-go access to Claude models such as Opus, Sonnet, Haiku, and Fable, as well as GPT models, while staying compatible with OpenAI SDKs, Claude Code, Codex, and common developer tooling. Its API base URL is https://api.59api.com, so in many projects you only need to change the base URL, API key, and model name.

What Is a Chat Completion?

A chat completion is a model-generated response based on a list of messages. Instead of sending one plain prompt, you send a conversation. Each message has a role and content. The model reads the full message list in order and predicts the next assistant message.

The core structure usually includes three things: a model name, a messages array, and optional generation settings. The model decides which AI system to use. The messages array contains the conversation. The settings control output style, length, randomness, and sometimes streaming.

The Three Main Message Roles

The messages array is the heart of the format. Beginners should understand these roles first:

• system: High-level instructions that define behavior, tone, rules, or constraints. For example, you might tell the model to answer as a concise technical support assistant.
• user: The request from the end user. This is the question, task, or instruction your application wants answered.
• assistant: Previous model responses. Including these helps preserve conversation history and context.

A simple request might contain one system message and one user message. A longer chat app will send several past user and assistant messages so the model can continue naturally. However, you should avoid sending unnecessary history because it increases token usage and cost.

Common Request Fields

Although exact model support varies, most OpenAI-compatible chat completion requests use familiar fields:

• model: The target model identifier, such as a GPT model or a Claude model exposed by the relay.
• messages: The ordered conversation list with role and content.
• temperature: Controls randomness. Lower values are better for factual, predictable output. Higher values are better for brainstorming.
• max_tokens: Limits how many tokens the model can generate in the response.
• stream: When true, tokens are returned incrementally, which is useful for chat interfaces and coding tools.

For example, a documentation assistant might use a low temperature such as 0.2, while a naming or marketing idea generator might use 0.8. A customer support bot should usually set a reasonable max token limit to prevent overly long replies and control spending.

How the Response Is Structured

The response usually contains an ID, model information, usage data, and a choices array. The generated message is typically found in the first choice. In many SDKs, you read it from a path similar to choices[0].message.content. The usage section is also important because it shows prompt tokens, completion tokens, and total tokens. This helps you monitor cost and optimize prompts.

When streaming is enabled, the response arrives in small chunks instead of one final object. Your app displays each chunk as it arrives. This makes the experience feel faster, especially for long answers, code generation, or agent workflows.

Using the Format with 59API

To use an OpenAI-compatible SDK with 59API, configure the client with your 59API key and set the base URL to https://api.59api.com. Then call the same chat completions method your SDK already supports. The main change is choosing the model you want to run, whether that is a Claude family model or a GPT model.

This compatibility is useful for teams that want to compare model quality and price without rewriting their application. For example, you might use a fast, inexpensive Claude Haiku-style model for classification and routing, a stronger Sonnet-style model for coding help, and a premium Opus-style model for complex reasoning. Because 59API uses native official-quality models with no downgrade, you can focus on latency, price, and output quality instead of worrying about a hidden model substitution.

Practical Tips for Better Results

• Put durable rules in the system message. Tone, output format, safety boundaries, and role instructions belong there.
• Keep user messages specific. Clear inputs produce better completions than vague requests.
• Limit conversation history. Send only the messages needed for context to reduce token cost.
• Use structured output instructions. If you need JSON, lists, or short answers, say so explicitly.
• Track usage. Review token counts and adjust prompts, model choice, and max token limits.

One common beginner mistake is treating the model like a database. Chat completions are powerful for reasoning, drafting, extraction, and transformation, but your app should still provide the facts the model needs. For reliable business workflows, retrieve relevant data from your own system first, then include it in the user message or context.

Why This Format Matters

The OpenAI-compatible chat completions format has become a practical standard because it is simple, flexible, and widely supported. Learning it helps you build chatbots, coding assistants, summarizers, support tools, content workflows, and internal AI agents with the same basic pattern.

For developers who care about cost, 59API makes this format even more valuable. It is among the cheapest AI API relays, supports pay-as-you-go usage, works with existing OpenAI-compatible tools, and includes a referral rebate. If you want affordable access to Claude and GPT models without changing your stack, consider signing up for 59API and testing your current chat completions code against https://api.59api.com.

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