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SGLang supports parsing reasoning content out from “normal” content for reasoning models such as DeepSeek R1.

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Supported Models & Parsers

ModelReasoning tagsParserNotes
DeepSeek‑R1 series<think> … </think>deepseek-r1Supports all variants (R1, R1-0528, R1-Distill)
DeepSeek‑V3 series<think> … </think>deepseek-v3Including DeepSeek‑V3.2. Supports thinking parameter
Standard Qwen3 models<think> … </think>qwen3Supports enable_thinking parameter
Qwen3-Thinking models<think> … </think>qwen3 or qwen3-thinkingAlways generates thinking content
Kimi K2 Thinking◁think▷ … ◁/think▷kimi_k2Uses special thinking delimiters. Also requires --tool-call-parser kimi_k2 for tool use.
GPT OSS<|channel|>analysis<|message|> … <|end|>gpt-ossN/A

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Model-Specific Behaviors

DeepSeek-R1 Family:
  • DeepSeek-R1: No <think> start tag, jumps directly to thinking content
  • DeepSeek-R1-0528: Generates both <think> start and </think> end tags
  • Both are handled by the same deepseek-r1 parser
DeepSeek-V3 Family:
  • DeepSeek-V3.1/V3.2: Hybrid model supporting both thinking and non-thinking modes, use the deepseek-v3 parser and thinking parameter (NOTE: not enable_thinking)
Qwen3 Family:
  • Standard Qwen3 (e.g., Qwen3-2507): Use qwen3 parser, supports enable_thinking in chat templates
  • Qwen3-Thinking (e.g., Qwen3-235B-A22B-Thinking-2507): Use qwen3 or qwen3-thinking parser, always thinks
Kimi K2:
  • Kimi K2 Thinking: Uses special ◁think▷ and ◁/think▷ tags. For agentic tool use, also specify --tool-call-parser kimi_k2.
GPT OSS:
  • GPT OSS: Uses special <|channel|>analysis<|message|> and <|end|> tags

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Usage

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Launching the Server

Specify the --reasoning-parser option. 
import requests
from openai import OpenAI
from sglang.test.doc_patch import launch_server_cmd
from sglang.utils import wait_for_server, print_highlight, terminate_process

server_process, port = launch_server_cmd(
    "python3 -m sglang.launch_server --model-path deepseek-ai/DeepSeek-R1-Distill-Qwen-7B --host 0.0.0.0 --reasoning-parser deepseek-r1 --log-level warning"
)

wait_for_server(f"http://localhost:{port}")
Note that --reasoning-parser defines the parser used to interpret responses.

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OpenAI Compatible API

Using the OpenAI compatible API, the contract follows the DeepSeek API design established with the release of DeepSeek-R1:
  • reasoning_content: The content of the CoT.
  • content: The content of the final answer.
# Initialize OpenAI-like client
client = OpenAI(api_key="None", base_url=f"http://0.0.0.0:{port}/v1")
model_name = client.models.list().data[0].id

messages = [
    {
        "role": "user",
        "content": "What is 1+3?",
    }
]

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Non-Streaming Request

response_non_stream = client.chat.completions.create(
    model=model_name,
    messages=messages,
    temperature=0.6,
    top_p=0.95,
    stream=False,  # Non-streaming
    extra_body={"separate_reasoning": True},
)
print_highlight("==== Reasoning ====")
print_highlight(response_non_stream.choices[0].message.reasoning_content)

print_highlight("==== Text ====")
print_highlight(response_non_stream.choices[0].message.content)

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Streaming Request

response_stream = client.chat.completions.create(
    model=model_name,
    messages=messages,
    temperature=0.6,
    top_p=0.95,
    stream=True,  # Non-streaming
    extra_body={"separate_reasoning": True},
)

reasoning_content = ""
content = ""
for chunk in response_stream:
    if chunk.choices[0].delta.content:
        content += chunk.choices[0].delta.content
    if chunk.choices[0].delta.reasoning_content:
        reasoning_content += chunk.choices[0].delta.reasoning_content

print_highlight("==== Reasoning ====")
print_highlight(reasoning_content)

print_highlight("==== Text ====")
print_highlight(content)
Optionally, you can buffer the reasoning content to the last reasoning chunk (or the first chunk after the reasoning content). 
response_stream = client.chat.completions.create(
    model=model_name,
    messages=messages,
    temperature=0.6,
    top_p=0.95,
    stream=True,  # Non-streaming
    extra_body={"separate_reasoning": True, "stream_reasoning": False},
)

reasoning_content = ""
content = ""
for chunk in response_stream:
    if chunk.choices[0].delta.content:
        content += chunk.choices[0].delta.content
    if chunk.choices[0].delta.reasoning_content:
        reasoning_content += chunk.choices[0].delta.reasoning_content

print_highlight("==== Reasoning ====")
print_highlight(reasoning_content)

print_highlight("==== Text ====")
print_highlight(content)
The reasoning separation is enable by default when specify . To disable it, set the separate_reasoning option to False in request. 
response_non_stream = client.chat.completions.create(
    model=model_name,
    messages=messages,
    temperature=0.6,
    top_p=0.95,
    stream=False,  # Non-streaming
    extra_body={"separate_reasoning": False},
)

print_highlight("==== Original Output ====")
print_highlight(response_non_stream.choices[0].message.content)

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SGLang Native API

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-7B")
input = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True, return_dict=False
)

gen_url = f"http://localhost:{port}/generate"
gen_data = {
    "text": input,
    "sampling_params": {
        "skip_special_tokens": False,
        "max_new_tokens": 1024,
        "temperature": 0.6,
        "top_p": 0.95,
    },
}
gen_response = requests.post(gen_url, json=gen_data).json()["text"]

print_highlight("==== Original Output ====")
print_highlight(gen_response)

parse_url = f"http://localhost:{port}/separate_reasoning"
separate_reasoning_data = {
    "text": gen_response,
    "reasoning_parser": "deepseek-r1",
}
separate_reasoning_response_json = requests.post(
    parse_url, json=separate_reasoning_data
).json()
print_highlight("==== Reasoning ====")
print_highlight(separate_reasoning_response_json["reasoning_text"])
print_highlight("==== Text ====")
print_highlight(separate_reasoning_response_json["text"])

terminate_process(server_process)

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Offline Engine API

import sglang as sgl
from sglang.srt.parser.reasoning_parser import ReasoningParser
from sglang.utils import print_highlight

llm = sgl.Engine(model_path="deepseek-ai/DeepSeek-R1-Distill-Qwen-7B")
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-R1-Distill-Qwen-7B")
input = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True, return_dict=False
)
sampling_params = {
    "max_new_tokens": 1024,
    "skip_special_tokens": False,
    "temperature": 0.6,
    "top_p": 0.95,
}
result = llm.generate(prompt=input, sampling_params=sampling_params)

generated_text = result["text"]  # Assume there is only one prompt

print_highlight("==== Original Output ====")
print_highlight(generated_text)

parser = ReasoningParser("deepseek-r1")
reasoning_text, text = parser.parse_non_stream(generated_text)
print_highlight("==== Reasoning ====")
print_highlight(reasoning_text)
print_highlight("==== Text ====")
print_highlight(text)

llm.shutdown()

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Supporting New Reasoning Model Schemas

For future reasoning models, you can implement the reasoning parser as a subclass of BaseReasoningFormatDetector in python/sglang/srt/reasoning_parser.py and specify the reasoning parser for new reasoning model schemas accordingly.