Some open models support a "thinking" mode that allows them to perform step-by-step reasoning before providing a final answer. This is useful for tasks requiring transparent logic, like mathematical proofs, intricate code debugging, or multi-step agent planning.
Model-specific guidance
The following sections provide model-specific guidance for thinking.
DeepSeek R1 0528
For DeepSeek R1 0528, reasoning is surrounded by <think></think>
tags in
the content
field. There is no reasoning_content
field.
Example request:
curl
-X
POST
\
-H
"Authorization: Bearer
$(
gcloud
auth
print-access-token )
"
\
-H
"Content-Type: application/json"
\
https://us-central1-aiplatform.googleapis.com/v1/projects/test-project/locations/us-central1/endpoints/openapi/chat/completions
-d
'{
"model": "deepseek-ai/deepseek-r1-0528-maas",
"messages": [{
"role": "user",
"content": "Who are you?"
}]
}'
Example response:
{
"choices"
:
[
{
"finish_reason"
:
"stop"
,
"index"
:
0
,
"logprobs"
:
null
,
"message"
:
{
"content"
:
"<think>\nHmm, the user just asked \u201cWho are you?\u201d -
a simple but fundamental question. \n\nFirst, let's consider the
context. This is likely their first interaction, so they're probably
testing the waters or genuinely curious about what I am. The phrasing is
neutral - no urgency or frustration detected. \n\nI should keep my
response warm and informative without being overwhelming. Since they
didn't specify language preference, I'll default to English but note
they might be multilingual. \n\nKey points to cover:\n- My identity
(DeepSeek-R1)\n- My capabilities \n- My purpose (being helpful)\n- Tone:
friendly with emojis to seem approachable\n- Ending with an open
question to continue conversation\n\nThe smiley face feels appropriate
here - establishes friendliness. Mentioning \u201cAI assistant\u201d
upfront avoids confusion. Listing examples of what I can do gives
concrete value. Ending with \u201cHow can I help?\u201d turns it into an
active conversation starter. \n\nNot adding too much technical detail
(like model specs) since that might overwhelm a first-time user. The
\u201calways learning\u201d phrase subtly manages expectations about my
limitations.\n</think>\nI'm DeepSeek-R1, your friendly AI assistant!
\ud83d\ude0a \nI'm here to help you with questions, ideas, problems, or
just a chat\u2014whether it's about homework, writing, coding, career
advice, or fun trivia. I can read files (like PDFs, Word, Excel, etc.),
browse the web for you (if enabled), and I'm always learning to be more
helpful!\n\nSo\u2026 how can I help you today? \ud83d\udcac\u2728"
,
"role"
:
"assistant"
}
}
],
"created"
:
1758229877
,
"id"
:
"dXXMaKrsKqaQm9IPsLeTkAQ"
,
"model"
:
"deepseek-ai/deepseek-r1-0528-maas"
,
"object"
:
"chat.completion"
,
"system_fingerprint"
:
""
,
"usage"
:
{
"completion_tokens"
:
329
,
"prompt_tokens"
:
9
,
"total_tokens"
:
338
}
}
DeepSeek v3.1
DeepSeek-V3.1 supports hybrid reasoning, which lets you turn
reasoning on or off. By default, reasoning is off. To enable reasoning,
include "chat_template_kwargs": { "thinking": true }
in your request.
When reasoning is enabled, thinking text appears at the beginning of the content
field, followed by </think>
and then the answer text
(for example, THINKING_TEXT</think>ANSWER_TEXT
).
Note that there is no initial <think>
tag.
Example request:
curl
-X
POST
\
-H
"Authorization: Bearer
$(
gcloud
auth
print-access-token )
"
\
-H
"Content-Type: application/json"
\
https://us-west2-aiplatform.googleapis.com/v1/projects/test-project/locations/us-west2/endpoints/openapi/chat/completions
-d
'{
"model": "deepseek-ai/deepseek-v3.1-maas",
"messages": [{
"role": "user",
"content": "What are the first 3 even prime numbers?"
}],
"chat_template_kwargs": {
"thinking": true
}
}'
Example response:
{
"choices"
:
[
{
"finish_reason"
:
"stop"
,
"index"
:
0
,
"logprobs"
:
null
,
"matched_stop"
:
1
,
"message"
:
{
"content"
:
"SNIPPED</think>The only even prime number is 2. By
definition, a prime number is a natural number greater than 1 that has
no positive divisors other than 1 and itself. Since all even numbers
other than 2 are divisible by 2 and themselves, they have at least three
divisors and are not prime. Therefore, there are no second or third even
prime numbers. The concept of \"first three even prime numbers\" is not
applicable beyond the first one."
,
"reasoning_content"
:
null
,
"role"
:
"assistant"
,
"tool_calls"
:
null
}
}
],
"created"
:
1758229458
,
"id"
:
"ynPMaIWPN-KeltsPh87s0Qg"
,
"model"
:
"deepseek-ai/deepseek-v3.1-maas"
,
"object"
:
"chat.completion"
,
"usage"
:
{
"completion_tokens"
:
1525
,
"prompt_tokens"
:
17
,
"prompt_tokens_details"
:
null
,
"total_tokens"
:
1542
}
}
Qwen3-Next Thinking
For Qwen3-Next Thinking models, reasoning tokens are in the reasoning_content
field, and normal response text is in the content
field.
Example request:
curl
-X
POST
\
-H
"Authorization: Bearer
$(
gcloud
auth
print-access-token )
"
\
-H
"Content-Type: application/json"
\
https://aiplatform.googleapis.com/v1/projects/test-project/locations/global/endpoints/openapi/chat/completions
-d
'{
"model": "qwen/qwen3-next-80b-a3b-thinking-maas",
"messages": [{
"role": "user",
"content": "Who are you?"
}]
}'
Example response:
{
"choices"
:
[
{
"finish_reason"
:
"stop"
,
"index"
:
0
,
"logprobs"
:
null
,
"matched_stop"
:
151645
,
"message"
:
{
"content"
:
"Hello! I'm Qwen, a large language model developed by Alibaba
Cloud. I can help answer questions, write stories, emails, scripts,
perform logical reasoning, code, and more. How can I assist you today?
\ud83d\ude0a"
,
"reasoning_content"
:
"Okay, the user asked, Who are you?
I need to respond appropriately. First, I should state my name clearly:
I am Qwen, a large language model developed by Alibaba Cloud. Then,
maybe mention my capabilities, like answering questions, writing
stories, emails, scripts, logical reasoning, programming, etc. Also,
highlight that I can express views and play games. But keep it concise
since the user might just want a brief intro.\n\nWait, the user might
not know much about me, so I should explain what I can do in simple
terms. Maybe add something about being trained on a large amount of
data, so I can handle various topics. But don't get too technical. Also,
maybe mention that I'm here to help. Should I ask if they have any
specific questions? That might be good to invite further
interaction.\n\nLet me check the structure. Start with a greeting,
introduce myself, list key abilities, and offer assistance. Keep it
friendly and approachable. Avoid jargon. Maybe say something like \"I'm
Qwen, a large language model created by Alibaba Cloud. I can help with
answering questions, writing, coding, and more. How can I assist you
today?\" That seems good.\n\nWait, the original response in Chinese
might be different, but the user asked in English, so the response
should be in English. Let me confirm. The user's query is in English, so
I should respond in English. Yes.\n\nAlso, check for any specific
details. Maybe mention that I have 128K context length, but maybe that's
too technical. Maybe just say \"I have a large context window\" or not
necessary. For a basic introduction, maybe stick to the main points
without too many specs. The user might not need the technical details
right away.\n\nSo, the response should be: Hello! I'm Qwen, a large
language model developed by Alibaba Cloud. I can help answer questions,
write stories, emails, scripts, perform logical reasoning, code, and
more. How can I assist you today? That's concise and covers the main
points.\n\nWait, in the initial problem statement, the user's question
is \"Who are you?\" So the standard response would be to introduce
myself. Yes. Also, maybe add that I'm an AI language model, but the name
Qwen is important. So the key elements are name, developer,
capabilities, and offer help.\n\nYes. Let me make sure there's no
mistake. Alibaba Cloud is the correct developer. Yes. So the response
should be accurate. Alright, that seems good.\n"
,
"role"
:
"assistant"
,
"tool_calls"
:
null
}
}
],
"created"
:
1758229652
,
"id"
:
"knTMaJC0EJfM5OMP7I3xkAk"
,
"metadata"
:
{
"weight_version"
:
"default"
},
"model"
:
"qwen/qwen3-next-80b-a3b-thinking-maas"
,
"object"
:
"chat.completion"
,
"usage"
:
{
"completion_tokens"
:
573
,
"prompt_tokens"
:
14
,
"prompt_tokens_details"
:
null
,
"reasoning_tokens"
:
0
,
"total_tokens"
:
587
}
}
GPT OSS
For GPT OSS models, thinking text is in the reasoning_content
field,
and normal response text is in the content
field. These models also
support the reasoning_effort
parameter.
Example request:
curl
-X
POST
\
-H
"Authorization: Bearer
$(
gcloud
auth
print-access-token )
"
\
-H
"Content-Type: application/json"
\
https://us-central1-aiplatform.googleapis.com/v1/projects/test-project/locations/us-central1/endpoints/openapi/chat/completions
-d
'{
"model": "openai/gpt-oss-120b-maas",
"messages": [{
"role": "user",
"content": "Who are you?"
}],
"reasoning_effort": "high"
}'
Example response:
{
"choices"
:
[
{
"finish_reason"
:
"stop"
,
"index"
:
0
,
"logprobs"
:
null
,
"matched_stop"
:
200002
,
"message"
:
{
"content"
:
"I\u2019m ChatGPT, a large language model created by OpenAI
(based on the GPT\u20114 architecture). I\u2019m here to help answer
your questions, brainstorm ideas, explain concepts, and assist with a
wide variety of topics. Let me know what you\u2019d like to talk
about!"
,
"reasoning_content"
:
"The user asks: \"Who are you?\" It's a simple
question. They want to know who the assistant is. According to policy,
we should respond with a brief, friendly description: \"I am ChatGPT, a
large language model trained by OpenAI.\" Possibly mention the version
(GPT-4). Also mention we are here to assist. There's no request for
disallowed content. So a straightforward answer. Possibly ask if they
need help. Provide a short intro.\n\nThus answer: \"I am ChatGPT, a
large language model trained by OpenAI. I'm here to help answer your
questions...\"\n\nWe should avoid disallowed content. It's fine.\n\nThus
final answer."
,
"role"
:
"assistant"
,
"tool_calls"
:
null
}
}
],
"created"
:
1758229799
,
"id"
:
"JnXMaJq6HLGzquEP4OD18Qg"
,
"metadata"
:
{
"weight_version"
:
"default"
},
"model"
:
"openai/gpt-oss-120b-maas"
,
"object"
:
"chat.completion"
,
"usage"
:
{
"completion_tokens"
:
201
,
"prompt_tokens"
:
71
,
"prompt_tokens_details"
:
null
,
"reasoning_tokens"
:
0
,
"total_tokens"
:
272
}
}
What's next
- Learn about Function calling .
- Learn about Structured output .
- Learn about Batch predictions .
