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DeepSeek-V3.2 is a large language model designed to harmonize high computational efficiency with strong reasoning and agentic tool-use performance. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism that reduces training and inference cost while preserving quality in long-context scenarios. A scalable reinforcement learning post-training framework further improves reasoning, with reported performance in the GPT-5 class, and the model has demonstrated gold-medal results on the 2025 IMO and IOI. V3.2 also uses a large-scale agentic task synthesis pipeline to better integrate reasoning into tool-use settings, boosting compliance and generalization in interactive environments.

You can POST to our OpenAI Chat Completions compatible endpoint.
Given a list of messages from a conversation, the model will return a response.
curl "https://api.deepinfra.com/v1/openai/chat/completions" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $DEEPINFRA_TOKEN" \
-d '{
"model": "deepseek-ai/DeepSeek-V3.2",
"messages": [
{
"role": "user",
"content": "Hello!"
}
]
}'
To which you'd get something like:
{
"id": "chatcmpl-guMTxWgpFf",
"object": "chat.completion",
"created": 1694623155,
"model": "deepseek-ai/DeepSeek-V3.2",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": " Hello! It's nice to meet you. Is there something I can help you with or would you like to chat for a bit?"
},
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 15,
"completion_tokens": 16,
"total_tokens": 31,
"estimated_cost": 0.0000268
}
}
To create a longer chat-like conversation you just have to add each response message and each of the user messages to every request. This way the model will have the context and will be able to provide better answers. You can tweak it even further by providing a system message.
curl "https://api.deepinfra.com/v1/openai/chat/completions" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $DEEPINFRA_TOKEN" \
-d '{
"model": "deepseek-ai/DeepSeek-V3.2",
"messages": [
{
"role": "system",
"content": "Respond like a michelin starred chef."
},
{
"role": "user",
"content": "Can you name at least two different techniques to cook lamb?"
},
{
"role": "assistant",
"content": "Bonjour! Let me tell you, my friend, cooking lamb is an art form, and I'"'"'m more than happy to share with you not two, but three of my favorite techniques to coax out the rich, unctuous flavors and tender textures of this majestic protein. First, we have the classic \"Sous Vide\" method. Next, we have the ancient art of \"Sous le Sable\". And finally, we have the more modern technique of \"Hot Smoking.\""
},
{
"role": "user",
"content": "Tell me more about the second method."
}
]
}'
The conversation above might return something like the following
{
"id": "chatcmpl-b23a3fb60cde42ce8f24bb980b4dee87",
"object": "chat.completion",
"created": 1715688169,
"model": "deepseek-ai/DeepSeek-V3.2",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "Sous le Sable, my friend! It's an ancient technique that's been used for centuries in the Middle East and North Africa. The name itself..."
},
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 149,
"total_tokens": 487,
"completion_tokens": 338,
"estimated_cost": 0.00035493
}
}
The longer the conversation gets, the more time it takes the model to generate the response. The number of messages that you can have in a conversation is limited by the context size of a model. Larger models also usually take more time to respond.
You can turn any of the requests above into a streaming request by passing "stream": true:
curl "https://api.deepinfra.com/v1/openai/chat/completions" \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $DEEPINFRA_TOKEN" \
-d '{
"model": "deepseek-ai/DeepSeek-V3.2",
"stream": true,
"messages": [
{
"role": "user",
"content": "Hello!"
}
]
}'
to which you'd get a sequence of SSE events, finishing with [DONE].
data: {"id": "Rc5hsIPHOSfMP3rNSFUw9tfR", "object": "chat.completion.chunk", "created": 1694623354, "model": "deepseek-ai/DeepSeek-V3.2", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " "}, "finish_reason": null}]}
data: {"id": "Rc5hsIPHOSfMP3rNSFUw9tfR", "object": "chat.completion.chunk", "created": 1694623354, "model": "deepseek-ai/DeepSeek-V3.2", "choices": [{"index": 0, "delta": {"role": "assistant", "content": " Hi"}, "finish_reason": null}]}
data: {"id": "Rc5hsIPHOSfMP3rNSFUw9tfR", "object": "chat.completion.chunk", "created": 1694623354, "model": "deepseek-ai/DeepSeek-V3.2", "choices": [{"index": 0, "delta": {"role": "assistant", "content": "!"}, "finish_reason": null}]}
data: {"id": "Rc5hsIPHOSfMP3rNSFUw9tfR", "object": "chat.completion.chunk", "created": 1694623354, "model": "deepseek-ai/DeepSeek-V3.2", "choices": [{"index": 0, "delta": {"role": "assistant", "content": ""}, "finish_reason": null}]}
data: {"id": "Rc5hsIPHOSfMP3rNSFUw9tfR", "object": "chat.completion.chunk", "created": 1694623354, "model": "deepseek-ai/DeepSeek-V3.2", "choices": [{"index": 0, "delta": {"role": "assistant", "content": "</s>"}, "finish_reason": null}]}
data: {"id": "Rc5hsIPHOSfMP3rNSFUw9tfR", "object": "chat.completion.chunk", "created": 1694623354, "model": "deepseek-ai/DeepSeek-V3.2", "choices": [{"index": 0, "delta": {}, "finish_reason": "stop"}]}
data: [DONE]
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