“DeepSeek think” generally refers to DeepSeek’s Thinking Mode: a capability in which a supported model can return reasoning content before its final answer. In DeepSeek’s direct API documentation, Thinking Mode is a capability, not a separate model named “DeepThink.” The documented API model IDs include deepseek-flash and deepseek-v4-pro; both support thinking and non-thinking modes. (api-docs.deepseek.com)
For production, start with deepseek-flash if it meets your task requirements and its lower listed rates matter. Test deepseek-v4-pro on the same workload if you want to assess whether its higher listed price is justified. Published prices and features alone do not establish which model will perform better for your application.
What does “DeepSeek think” mean?
The phrase can refer informally to DeepSeek’s thinking capability or an app experience. In the direct API documentation, the term is Thinking Mode. Use a documented API model ID and request controls; do not treat “DeepSeek Think” or “DeepThink R1” as a current API model name.
In Thinking Mode, the API returns reasoning content in a reasoning_content field, separately from the final content. Treat that field as generated response data, not as an independently verified account of how an answer was produced. (api-docs.deepseek.com)
When should you use Thinking Mode?
Consider testing it for tasks involving multiple steps or constraints, such as code problems or comparative analysis. For straightforward requests, non-thinking mode may be sufficient. These are workload-based starting points, not guarantees of better accuracy or lower latency.
Compare both modes on representative prompts. Track task success, factual or rule-following errors, response time, output length, and token use. Keep the setting that meets your quality target at an acceptable operational cost; do not assume thinking is better for every prompt.
How to use Thinking Mode with the direct DeepSeek API
You need a DeepSeek API key and Python with the OpenAI SDK installed. DeepSeek documents the OpenAI-compatible API base URL as https://api.deepseek.com. This non-streaming example uses the documented deepseek-flash model, explicitly enables thinking, and sets effort to high. (api-docs.deepseek.com)
pip install openai
export DEEPSEEK_API_KEY="your-api-key"
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["DEEPSEEK_API_KEY"],
base_url="https://api.deepseek.com",
)
response = client.chat.completions.create(
model="deepseek-flash",
messages=[
{"role": "user", "content": "Compare these two options against the requirements I gave you."}
],
stream=False,
reasoning_effort="high",
extra_body={"thinking": {"type": "enabled"}},
)
message = response.choices[0].message
print("Answer:", message.content)
print("Reasoning field:", message.reasoning_content)
The final answer is in message.content; the separate reasoning field is message.reasoning_content. Thinking Mode is enabled by default, and the default effort is high; the example sets both explicitly. To disable thinking, use extra_body={"thinking": {"type": "disabled"}}. (api-docs.deepseek.com)
Choosing a model for production
The table reflects DeepSeek’s direct API pricing page as of November 10, 2026. DeepSeek says prices may change, so check the page before deployment. Its pricing text displays prices with a $ symbol but does not spell out an ISO currency code in the retrieved pricing details. (api-docs.deepseek.com)
| Direct API model | Documented version | Listed price per 1 million tokens (off-peak / peak) | A reasonable starting point |
|---|---|---|---|
deepseek-flash |
DeepSeek-V4.1-Flash-1017 | Input, cache hit: $0.003 / $0.006; input, cache miss: $0.15 / $0.30; output: $0.60 / $1.20 | Start here if it meets your tested quality target and its lower listed rates matter. |
deepseek-v4-pro |
DeepSeek-V4-Pro-0813 | Input, cache hit: $0.022 / $0.044; input, cache miss: $0.66 / $1.32; output: $1.98 / $3.96 | Test it on your workload if the potential task-specific benefit could justify its higher listed price. |
DeepSeek lists prices per 1 million tokens. It says off-peak rates are half of peak rates. Peak hours are 01:00–04:00 and 06:00–10:00 UTC, Monday through Friday, excluding Chinese public holidays. All other hours are off-peak, including weekends and Chinese public holidays in full. (api-docs.deepseek.com)
As an arithmetic illustration, aggregate usage of 1 million uncached input tokens and 100,000 output tokens at deepseek-flash’s listed off-peak rates would cost about $0.21: $0.15 for input plus $0.06 for output. At peak rates, the same aggregate token counts would cost about $0.42. This is not a claim that those token counts fit in one request. Thinking can add generated reasoning tokens and increase output-token use; check actual usage for the API protocol you use. (api-docs.deepseek.com)
Practical rule: use deepseek-flash as an initial candidate, compare thinking and non-thinking settings on representative tasks, and trial deepseek-v4-pro if your evaluation suggests a worthwhile benefit. DeepSeek documents GET https://api.deepseek.com/models for current model metadata; check that endpoint and the pricing page before deployment. (api-docs.deepseek.com)
Sources
Models & Pricing | DeepSeek API Docs
Thinking Mode | DeepSeek API Docs
Thinking Mode API Example (Non-Streaming) | DeepSeek API Docs