Updated 2026-07-08
DeepSeek thinking mode non-streaming sample: use the official two-turn example before you improvise client state
DeepSeek's docs now publish a dedicated non-streaming thinking-mode sample. That matters because many teams jump straight into streaming UIs, agent wrappers, or tool loops before they ever verify the simpler contract. This page targets the search intent behind queries like DeepSeek thinking mode non streaming, DeepSeek non streaming sample, and DeepSeek reasoning_content ignored.
1. What the official non-streaming sample actually does
The sample uses the OpenAI-compatible client, sends a normal chat-completions request, enables thinking, and captures both `reasoning_content` and final `content` from the returned assistant message.
It then performs a second user turn with the first assistant message appended to history. DeepSeek's own inline note says the earlier `reasoning_content` will be ignored by the API in this ordinary multi-turn case.
Sources checked
- DeepSeek official non-streaming thinking-mode sample - Primary source for the exact two-turn non-streaming request pattern.
2. The useful boundary is simpler than many wrappers imply
The first operational lesson is that not every DeepSeek follow-up turn needs special replay logic. In this sample there is no tool call between the two user turns, so the API can safely ignore the earlier `reasoning_content`.
That gives you a cleaner debugging ladder. Validate the ordinary non-streaming path first, then move to tool-call replay rules only if your workflow actually introduces tools.
| Step | What DeepSeek shows | Why it matters |
|---|---|---|
| Turn 1 | Assistant returns `reasoning_content` plus final `content` | Confirms thinking output and visible answer are separate fields. |
| Turn 2 setup | Previous assistant message is appended to history | Shows the ordinary multi-turn shape before tools complicate it. |
| Inline docs note | `reasoning_content` will be ignored | Prevents teams from inventing unnecessary replay rules. |
3. Keep the payload explicit while you test
The official sample keeps the request shape readable: explicit `model`, explicit `messages`, explicit `reasoning_effort`, and explicit `extra_body` thinking settings.
That is the right baseline for support work. If a framework hides those fields behind helpers, reproduce the same payload directly before you trust the abstraction.
response = client.chat.completions.create(
model="deepseek-v4-pro",
messages=messages,
reasoning_effort="high",
extra_body={"thinking": {"type": "enabled"}},
)Sources checked
- DeepSeek official Thinking Mode guide - Use the broader guide for the protocol rules behind the sample.
4. Use this sample before you debug harder flows
If your application already mixes streaming, tool calls, middleware transforms, or custom chat storage, you can waste hours debugging the wrong layer. Reproduce this non-streaming sample first. If it works, your core DeepSeek route is probably healthy.
Then branch outward: for streaming behavior continue with `/guides/deepseek-thinking-mode-streaming-sample`; for tool-call replay continue with `/guides/deepseek-thinking-mode-tool-calls` and `/guides/deepseek-tool-call-python-sample`.
5. This is protocol support content, not storefront inventory
This guide explains the official non-streaming sample only. It does not create a new Coding Plan, stock promise, or pricing-page claim.
If a reader needs DeepSeek access, the CTA can point at `/pricing`, but actual purchasable products remain limited by real in-stock inventory in this repo.
FAQ
What does DeepSeek's official non-streaming thinking-mode sample show?
It shows a two-turn OpenAI-compatible chat-completions example where the assistant returns both `reasoning_content` and final `content`, then a normal follow-up user turn is sent.
Does DeepSeek require `reasoning_content` replay in this sample?
No. The official sample notes that the earlier `reasoning_content` will be ignored in this ordinary non-tool follow-up turn.
Why start with the non-streaming sample first?
Because it isolates the basic protocol contract before streaming transport, tool calls, or framework abstractions add more failure points.
Does this page mean a new DeepSeek model is sold on `/pricing`?
No. It is documentation-driven support content only and does not change product inventory.
The safest DeepSeek debugging order is to verify the official non-streaming thinking-mode sample first, then add streaming or tool complexity only after the plain two-turn path is stable.
Related model comparisons
Continue from this guide into structured DeepSeek-first comparison pages with model tables, routing advice, and pricing context.