Updated 2026-07-01
DeepSeek Create Chat Completion API guide: get the message contract right before you blame the model
DeepSeek's official Create Chat Completion reference is one of the strongest support-query pages in the docs because it makes the request and response contract explicit. It defines `messages` as required, spells out the supported role shapes, documents streaming chunks and usage output, and lists finish reasons such as `tool_calls` and `insufficient_system_resource`. This page turns that reference into an operator-friendly guide without drifting away from the official contract.
1. `messages` is the required contract, not a convenience field
DeepSeek's official Create Chat Completion reference says the body requires a `messages` array with at least one entry. The page then defines the supported role structures for system, user, assistant, and tool messages.
That matters because many integration bugs start with a half-correct OpenAI-compatible payload. If the role mix or content shape is wrong, the right fix is to repair the message object rather than swapping models or wrappers.
If your app also needs multi-turn memory rules, continue with `/guides/deepseek-chat-completions-stateless-history` after this page.
Sources checked
- DeepSeek official Create Chat Completion reference - Primary source for request body, message roles, streaming samples, and finish reasons.
2. Know the four role types before adding tools
The official schema distinguishes system, user, assistant, and tool messages instead of treating every turn as plain text. That is especially important once tool calls enter the flow, because DeepSeek's API expects tool results to come back as a dedicated `tool` role message, not as improvised assistant text.
The clean implementation habit is to validate role shape at the edge of your SDK wrapper. Catching a malformed tool or assistant message before the request leaves your service is cheaper than debugging a vague downstream failure.
| Role | What it carries | Common misuse |
|---|---|---|
| `system` | Instructional context | Stuffing user content into the wrong role |
| `user` | End-user prompt or follow-up | Forgetting it must include real content |
| `assistant` | Model reply or tool-call stub | Replaying full response objects instead of message content |
| `tool` | Structured tool result | Returning tool output as plain assistant text |
3. Streaming is chunked SSE, and usage arrives at the end
DeepSeek's official streaming sample shows SSE-style `chat.completion.chunk` events and ends with a final chunk that includes `usage`, followed by `[DONE]`.
That gives teams a safer parse order. Do not assume the first chunk contains the whole answer, and do not assume token usage is available before the stream finishes. Treat the stream as incremental deltas plus a final accounting record.
If your main concern is prompt budgeting, pair this with `/guides/deepseek-v4-pricing-per-million-tokens` and `/guides/deepseek-error-codes-guide`.
data: {"object":"chat.completion.chunk", ...}
...
data: {"finish_reason":"stop","usage":{"prompt_tokens":17,"completion_tokens":9,"total_tokens":26}}
data: [DONE]4. `finish_reason` tells you why generation stopped
The official response schema lists `stop`, `length`, `content_filter`, `tool_calls`, and `insufficient_system_resource` as possible finish reasons.
That is useful because not every truncated or interrupted response means the same thing. `tool_calls` means the model intentionally handed control to a tool path, while `insufficient_system_resource` means the inference system interrupted the request for capacity reasons.
A production client should branch on finish reason instead of pretending every non-`stop` outcome is a fatal unknown.
5. Treat the API reference as the single source of truth for wrappers
DeepSeek's chat-completion route is OpenAI-compatible, but compatibility is not a license to ignore the published schema. The official reference is where the role contract, response object types, tool-call path, and finish-reason semantics are pinned.
That is why this page complements the higher-level guides instead of duplicating them. The support question here is not just 'how do I chat with DeepSeek?' It is 'what exact request and response structure does DeepSeek expect today?'
FAQ
What is required in a DeepSeek Create Chat Completion request?
DeepSeek's official reference requires a `messages` array with at least one message.
Which message roles does DeepSeek support in chat completions?
The official schema defines system, user, assistant, and tool message shapes.
When does `usage` appear in a streamed DeepSeek response?
DeepSeek's official sample shows usage information in the final stream chunk before `[DONE]`.
What does `finish_reason: tool_calls` mean?
It means the model stopped to call a tool, not that the answer failed randomly.
What does `insufficient_system_resource` mean in DeepSeek chat completions?
The official reference says the request was interrupted because the inference system lacked sufficient resources.
The practical DeepSeek chat-completion rule is simple: validate the `messages` contract first, respect the dedicated role types, parse streaming responses as incremental chunks, and branch on `finish_reason` instead of guessing.
Related model comparisons
Continue from this guide into structured DeepSeek-first comparison pages with model tables, routing advice, and pricing context.