Alcuni modelli aperti supportano una modalità "pensiero" che consente loro di eseguire un ragionamento passo passo prima di fornire una risposta finale. Questo è utile per le attività che richiedono una logica trasparente, come dimostrazioni matematiche, debug di codice complesso o pianificazione di agenti in più passaggi.
Indicazioni specifiche per il modello
Le sezioni seguenti forniscono indicazioni specifiche per il pensiero basato su modelli.
DeepSeek R1 0528
Per DeepSeek R1 0528, il ragionamento è racchiuso tra i tag <think></think> nel campo content. Non è presente alcun campo reasoning_content.
Richiesta di esempio:
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?"
}]
}'
Esempio di risposta:
{
"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 supporta il ragionamento ibrido, che ti consente di attivare
o disattivare il ragionamento. Per impostazione predefinita, il ragionamento è disattivato. Per attivarlo,
includi "chat_template_kwargs": { "thinking": true } nella richiesta.
Quando il ragionamento è attivato, il testo del pensiero viene visualizzato all'inizio del campo
content, seguito da </think> e poi dal testo della risposta
(ad esempio, THINKING_TEXT</think>ANSWER_TEXT).
Tieni presente che non è presente alcun tag <think> iniziale.
Richiesta di esempio:
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
}
}'
Esempio di risposta:
{
"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
Per i modelli Qwen3 - Next Thinking, i token di ragionamento si trovano nel campo reasoning_content, mentre il testo della risposta normale si trova nel campo content.
Richiesta di esempio:
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?"
}]
}'
Esempio di risposta:
{
"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
Per i modelli GPT OSS, il testo del pensiero si trova nel campo reasoning_content,
mentre il testo della risposta normale si trova nel campo content. Questi modelli supportano anche il parametro reasoning_effort.
Richiesta di esempio:
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"
}'
Esempio di risposta:
{
"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
}
}
Passaggi successivi
- Scopri di più sulla chiamata di funzione.
- Scopri di più sull'output strutturato.
- Scopri di più sulle previsioni batch.