OpenAI-Compatible Chat Completions: A 2026 Guide
What does OpenAI-compatible chat completions mean?
The OpenAI-compatible chat completions format is a common HTTP interface for sending conversations to language models. Your application submits a model identifier and an ordered list of messages; the service returns an assistant message, structured tool calls, or streamed response fragments.
Compatibility makes integrations reusable, but it does not make every model interchangeable. Context limits, supported message types, reasoning controls, and structured-output features can differ. In 2026, the safest approach is to treat the format as a shared transport contract and test capabilities separately.
1. Configure authentication and the endpoint
A typical request uses POST /v1/chat/completions, an Authorization: Bearer YOUR_API_KEY header, and Content-Type: application/json. Keep API keys on your server or in a secret manager, never in browser JavaScript or committed source files.
59API's API base is https://api.59api.com. When configuring an OpenAI SDK, verify whether its base URL setting needs the versioned prefix, commonly https://api.59api.com/v1. The SDK normally appends the resource path itself; do not put the complete chat completions endpoint into that setting or accidentally duplicate /v1.
2. Build the messages array correctly
A minimal request body looks like this, with MODEL_ID replaced by the provider's documented identifier:
{"model":"MODEL_ID","messages":[{"role":"system","content":"Answer clearly and concisely."},{"role":"user","content":"Explain API rate limits in two sentences."}],"stream":false}
- model: Selects the model. A catalog display name is not necessarily its API identifier.
- messages: Preserves conversation order. Include relevant previous turns because ordinary chat completions requests do not automatically retrieve earlier requests.
- role: Common values include system, user, assistant, and tool. Some implementations also support developer messages; confirm support before using them.
- content: Often a string for text. Multimodal requests may use an array of typed content parts, but supported image, audio, and other input formats vary.
Keep durable application instructions separate from user input. Trim irrelevant history before exceeding the context window, and never treat retrieved documents or tool results as trusted instructions.
3. Read the response, not just the text
For a standard non-streaming response, inspect choices[0].message. Its content usually contains the answer, but a tool-calling response may contain tool_calls instead of ordinary text. Check choices[0].finish_reason to distinguish a completed answer, an output limit, or a tool handoff.
Record the returned model identifier and usage fields when available. Token accounting can differ across providers, especially for reasoning and cached inputs, so use actual billing records rather than assuming every reported token has the same price.
4. Handle streaming and tools as separate workflows
With stream: true, compatible endpoints typically return server-sent events. Parse each data event, read changes from choices[0].delta, and assemble the final message. Do not expect every chunk to contain readable text: some carry role information, tool-call fragments, or completion metadata. Usage reporting during streaming is implementation-dependent.
For tools, define named functions and their JSON Schema parameters in the tools field. When the model requests a function, validate its arguments, authorize the action, execute it in your application, and append a tool message containing the matching tool_call_id. Send the updated conversation again to obtain the final answer. The model proposes actions; your application controls execution.
5. Test compatibility before optimizing cost
Start with a small text request, then test streaming, tools, and structured outputs independently. Do not blindly copy optional parameters: temperature, stop sequences, and output-token controls may have model-specific restrictions. Check support for max_completion_tokens versus legacy max_tokens rather than sending both.
Set timeouts and bounded retries with backoff for transient failures and rate limits. Log request identifiers and latency without exposing secrets. Avoid automatically repeating workflows that could execute a purchase or another irreversible action.
Why use 59API for this workflow?
59API offers low-cost, pay-as-you-go access to native, official-quality models without downgrades, including DeepSeek V4 Flash and GPT-6 Astra. Its OpenAI SDK compatibility helps reuse integrations while comparing price, latency, and task quality. It also supports Claude Code, Codex, and Gemini CLI workflows through their appropriate configurations.
With no need to commit to one model family and a referral rebate available, 59API is a practical option for cost-conscious development. Sign up when you are ready to test a small workload, then expand after validating quality and compatibility.