Add think chunk (#21333)

Signed-off-by: Julien Denize <julien.denize@mistral.ai>

requirements/common.txt

@@ -33,7 +33,7 @@ pyzmq >= 25.0.0
 msgspec
 gguf >= 0.13.0
 importlib_metadata; python_version < '3.10'
-mistral_common[opencv] >= 1.8.0
+mistral_common[image,audio] >= 1.8.2
 opencv-python-headless >= 4.11.0    # required for video IO
 pyyaml
 six>=1.16.0; python_version > '3.11' # transitive dependency of pandas that needs to be the latest version for python 3.12

requirements/nightly_torch_test.txt

@@ -23,7 +23,7 @@ jiwer # required for audio tests
 timm # required for internvl test
 transformers_stream_generator # required for qwen-vl test
 matplotlib # required for qwen-vl test
-mistral_common[opencv] >= 1.8.0 # required for voxtral test
+mistral_common[image,audio] >= 1.8.2 # required for voxtral test
 num2words # required for smolvlm test
 opencv-python-headless >= 4.11.0 # required for video test
 datamodel_code_generator # required for minicpm3 test

requirements/test.in

@@ -28,7 +28,7 @@ torchvision==0.22.1
 transformers_stream_generator # required for qwen-vl test
 mamba_ssm # required for plamo2 test
 matplotlib # required for qwen-vl test
-mistral_common[opencv] >= 1.8.0 # required for voxtral test
+mistral_common[image,audio] >= 1.8.2 # required for voxtral test
 num2words # required for smolvlm test
 open_clip_torch==2.32.0 # Required for nemotron_vl test
 opencv-python-headless >= 4.11.0 # required for video test

requirements/test.txt

@@ -447,7 +447,7 @@ mbstrdecoder==1.1.3
     #   typepy
 mdurl==0.1.2
     # via markdown-it-py
-mistral-common==1.8.0
+mistral-common==1.8.2
     # via -r requirements/test.in
 mlflow==2.22.0
     # via terratorch
@@ -999,8 +999,11 @@ soundfile==0.12.1
     # via
     #   -r requirements/test.in
     #   librosa
+    #   mistral-common
 soxr==0.5.0.post1
-    # via librosa
+    # via
+    #   librosa
+    #   mistral-common
 sqlalchemy==2.0.41
     # via
     #   alembic

tests/entrypoints/test_chat_utils.py

@@ -6,6 +6,10 @@ from collections.abc import Mapping
 from typing import Literal, Optional
 
 import pytest
+from mistral_common.tokens.tokenizers.base import (SpecialTokenPolicy,
+                                                   SpecialTokens)
+from mistral_common.tokens.tokenizers.tekken import (SpecialTokenInfo,
+                                                     Tekkenizer)
 
 from vllm.assets.audio import AudioAsset
 from vllm.assets.image import ImageAsset
@@ -21,6 +25,7 @@ from vllm.multimodal import MultiModalDataDict
 from vllm.multimodal.utils import (encode_audio_base64, encode_image_base64,
                                    encode_video_base64)
 from vllm.transformers_utils.tokenizer_group import TokenizerGroup
+from vllm.transformers_utils.tokenizers.mistral import MistralTokenizer
 
 from ..models.registry import HF_EXAMPLE_MODELS
 from ..utils import VLLM_PATH
@@ -1374,3 +1379,165 @@ def test_resolve_content_format_examples(template_path, expected_format):
     )
 
     assert resolved_format == expected_format
+
+
+def test_parse_chat_messages_include_thinking_chunk(mistral_model_config,
+                                                    mistral_tokenizer):
+    messages = [{
+        "role":
+        "system",
+        "content": [{
+            "type": "text",
+            "text": "You are a helpful assistant."
+        }, {
+            "type":
+            "thinking",
+            "closed":
+            True,
+            "thinking":
+            "Only return the answer when you are confident."
+        }]
+    }, {
+        "role": "user",
+        "content": "What is 2+2?"
+    }, {
+        "role":
+        "assistant",
+        "content": [{
+            "type": "text",
+            "text": "Let me think about it."
+        }, {
+            "type": "thinking",
+            "closed": True,
+            "thinking": "2+2 = 4"
+        }, {
+            "type": "text",
+            "text": "The answer is 4.",
+        }],
+    }]
+
+    conversation_with_thinking, _ = parse_chat_messages(
+        messages,
+        mistral_model_config,
+        mistral_tokenizer,
+        content_format="openai",
+    )
+
+    expected_conversation = [{
+        "role":
+        "system",
+        "content": [{
+            "type": "text",
+            "text": "You are a helpful assistant."
+        }, {
+            "type": "text",
+            "text": "Only return the answer when you are confident."
+        }],
+    }, {
+        "role":
+        "user",
+        "content": [{
+            "type": "text",
+            "text": "What is 2+2?"
+        }],
+    }, {
+        "role":
+        "assistant",
+        "content": [
+            {
+                "type": "text",
+                "text": "Let me think about it."
+            },
+            {
+                "type": "text",
+                "text": "2+2 = 4"
+            },
+            {
+                "type": "text",
+                "text": "The answer is 4."
+            },
+        ]
+    }]
+
+    assert conversation_with_thinking == expected_conversation
+
+
+def test_apply_mistral_chat_template_thinking_chunk():
+    # Moved import here to avoid yapf and isort conflicts
+    from vllm.entrypoints.chat_utils import apply_mistral_chat_template
+    messages = [{
+        "role":
+        "system",
+        "content": [{
+            "type": "text",
+            "text": "You are a helpful assistant."
+        }, {
+            "type":
+            "thinking",
+            "closed":
+            True,
+            "thinking":
+            "Only return the answer when you are confident."
+        }]
+    }, {
+        "role": "user",
+        "content": "What is 2+2?"
+    }, {
+        "role":
+        "assistant",
+        "content": [{
+            "type": "text",
+            "text": "Let me think about it."
+        }, {
+            "type": "thinking",
+            "closed": True,
+            "thinking": "2+2 = 4"
+        }, {
+            "type": "text",
+            "text": "The answer is 4.",
+        }],
+    }, {
+        "role": "user",
+        "content": "Thanks, what is 3+3?"
+    }]
+
+    # TODO(Julien): upon model release change to a tokenizer already configured.
+    # =================================================================
+    mistral_tokenizer = MistralTokenizer.from_pretrained(
+        "mistralai/Devstral-Small-2507")
+    assert isinstance(mistral_tokenizer.tokenizer, Tekkenizer)
+    # Add think special tokens to the tokenizer
+    mistral_tokenizer.tokenizer._all_special_tokens[35] = SpecialTokenInfo(
+        rank=35, is_control=True, token_str=SpecialTokens.begin_think.value)
+    mistral_tokenizer.tokenizer._all_special_tokens[36] = SpecialTokenInfo(
+        rank=36, is_control=True, token_str=SpecialTokens.end_think.value)
+    mistral_tokenizer.tokenizer._special_tokens_reverse_vocab = {
+        k: v
+        for k, v in
+        mistral_tokenizer.tokenizer._special_tokens_reverse_vocab.items()
+        if v not in {35, 36}
+    }
+    mistral_tokenizer.tokenizer._special_tokens_reverse_vocab[
+        SpecialTokens.begin_think.value] = 35
+    mistral_tokenizer.tokenizer._special_tokens_reverse_vocab[
+        SpecialTokens.end_think.value] = 36
+    mistral_tokenizer.instruct.BEGIN_THINK = 35
+    mistral_tokenizer.instruct.END_THINK = 36
+    # =================================================================
+
+    tokens_ids = apply_mistral_chat_template(mistral_tokenizer,
+                                             messages,
+                                             chat_template=None,
+                                             tools=None)
+
+    string_tokens = mistral_tokenizer.mistral.decode(
+        tokens_ids, special_token_policy=SpecialTokenPolicy.KEEP)
+
+    expected_tokens = (
+        r"<s>[SYSTEM_PROMPT]You are a helpful assistant.[THINK]Only return the"
+        r" answer when you are confident.[/THINK][/SYSTEM_PROMPT]"
+        r"[INST]What is 2+2?[/INST]"
+        r"Let me think about it.[THINK]2+2 = 4[/THINK]The answer is 4.</s>"
+        r"[INST]Thanks, what is 3+3?[/INST]")
+
+    assert string_tokens == expected_tokens

tests/reasoning/test_mistral_reasoning_parser.py

@@ -0,0 +1,341 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import pytest
+from mistral_common.tokens.tokenizers.base import SpecialTokens
+from mistral_common.tokens.tokenizers.tekken import (SpecialTokenInfo,
+                                                     Tekkenizer)
+
+from tests.reasoning.utils import run_reasoning_extraction_mistral
+from vllm.reasoning import ReasoningParser, ReasoningParserManager
+from vllm.transformers_utils.tokenizers.mistral import MistralTokenizer
+
+parser_name = "mistral"
+
+
+@pytest.fixture(scope="module")
+def mistral_tokenizer():
+    # TODO(Julien): upon model release change to a tokenizer already configured.
+    # =================================================================
+    mistral_tokenizer = MistralTokenizer.from_pretrained(
+        "mistralai/Devstral-Small-2507")
+    assert isinstance(mistral_tokenizer.tokenizer, Tekkenizer)
+    # Add think special tokens to the tokenizer
+    mistral_tokenizer.tokenizer._all_special_tokens[35] = SpecialTokenInfo(
+        rank=35, is_control=True, token_str=SpecialTokens.begin_think.value)
+    mistral_tokenizer.tokenizer._all_special_tokens[36] = SpecialTokenInfo(
+        rank=36, is_control=True, token_str=SpecialTokens.end_think.value)
+    mistral_tokenizer.tokenizer._special_tokens_reverse_vocab = {
+        k: v
+        for k, v in
+        mistral_tokenizer.tokenizer._special_tokens_reverse_vocab.items()
+        if v not in {35, 36}
+    }
+    mistral_tokenizer.tokenizer._special_tokens_reverse_vocab[
+        SpecialTokens.begin_think.value] = 35
+    mistral_tokenizer.tokenizer._special_tokens_reverse_vocab[
+        SpecialTokens.end_think.value] = 36
+    mistral_tokenizer.instruct.BEGIN_THINK = 35
+    mistral_tokenizer.instruct.END_THINK = 36
+    # =================================================================
+    return mistral_tokenizer
+
+
+SIMPLE_REASONING = {
+    "output": "This is a reasoning section[/THINK]This is the rest",
+    "reasoning_content": "This is a reasoning section",
+    "content": "This is the rest",
+    "is_reasoning_end": True,
+}
+COMPLETE_REASONING = {
+    "output": "This is a reasoning section[/THINK]",
+    "reasoning_content": "This is a reasoning section",
+    "content": None,
+    "is_reasoning_end": True,
+}
+NO_CONTENT = {
+    "output": "This is content",
+    "reasoning_content": "This is content",
+    "content": None,
+    "is_reasoning_end": False,
+}
+NO_REASONING_STREAMING = {
+    "output": "This is a reasoning section",
+    "reasoning_content": "This is a reasoning section",
+    "content": None,
+    "is_reasoning_end": False,
+}
+MULTIPLE_LINES = {
+    "output": "This\nThat[/THINK]This is the rest\nThat",
+    "reasoning_content": "This\nThat",
+    "content": "This is the rest\nThat",
+    "is_reasoning_end": True,
+}
+SHORTEST_REASONING_NO_STREAMING = {
+    "output": "[/THINK]This is the rest",
+    "reasoning_content": "",
+    "content": "This is the rest",
+    "is_reasoning_end": True,
+}
+SHORTEST_REASONING = {
+    "output": "[/THINK]This is the rest",
+    "reasoning_content": None,
+    "content": "This is the rest",
+    "is_reasoning_end": True,
+}
+REASONING_WITH_THINK = {
+    "output": "[THINK]This is a reasoning section[/THINK]This is the rest",
+    "reasoning_content": "This is a reasoning section",
+    "content": "This is the rest",
+    "is_reasoning_end": True,
+}
+COMPLETE_REASONING_WITH_THINK = {
+    "output": "[THINK]This is a reasoning section[/THINK]",
+    "reasoning_content": "This is a reasoning section",
+    "content": None,
+    "is_reasoning_end": True,
+}
+MULTIPLE_LINES_WITH_THINK = {
+    "output": "[THINK]This\nThat[/THINK]This is the rest\nThat",
+    "reasoning_content": "This\nThat",
+    "content": "This is the rest\nThat",
+    "is_reasoning_end": True,
+}
+SHORTEST_REASONING_NO_STREAMING_WITH_THINK = {
+    "output": "[/THINK]This is the rest",
+    "reasoning_content": "",
+    "content": "This is the rest",
+    "is_reasoning_end": True,
+}
+SHORTEST_REASONING_WITH_THINK = {
+    "output": "[/THINK]This is the rest",
+    "reasoning_content": None,
+    "content": "This is the rest",
+    "is_reasoning_end": True,
+}
+THINK_NO_END = {
+    "output": "[THINK]This is a reasoning section",
+    "reasoning_content": "This is a reasoning section",
+    "content": None,
+    "is_reasoning_end": False,
+}
+EMPTY = {
+    "output": "",
+    "reasoning_content": "",
+    "content": None,
+    "is_reasoning_end": False,
+}
+EMPTY_STREAMING = {
+    "output": "",
+    "reasoning_content": None,
+    "content": None,
+    "is_reasoning_end": False,
+}
+NEW_LINE = {
+    "output": "\n[THINK]This is a reasoning section[/THINK]\nThis is the rest",
+    "reasoning_content": "This is a reasoning section",
+    "content": "\nThis is the rest",
+    "is_reasoning_end": True,
+}
+# Streaming cannot handle new lines at the beginning of the output
+# because we need to support [THINK]...[/THINK] and [/THINK]...
+# We cannot know if the text before [THINK] is reasoning content
+# or not.
+NEW_LINE_STREAMING = {
+    "output": "\n[THINK]This is a reasoning section[/THINK]\nThis is the rest",
+    "reasoning_content": "\nThis is a reasoning section",
+    "content": "\nThis is the rest",
+    "is_reasoning_end": True,
+}
+
+TEST_CASES = [
+    pytest.param(
+        False,
+        SIMPLE_REASONING,
+        id="simple_reasoning",
+    ),
+    pytest.param(
+        True,
+        SIMPLE_REASONING,
+        id="simple_reasoning_streaming",
+    ),
+    pytest.param(
+        False,
+        COMPLETE_REASONING,
+        id="complete_reasoning",
+    ),
+    pytest.param(
+        True,
+        COMPLETE_REASONING,
+        id="complete_reasoning_streaming",
+    ),
+    pytest.param(
+        False,
+        NO_CONTENT,
+        id="no_content_token",
+    ),
+    pytest.param(
+        True,
+        NO_REASONING_STREAMING,
+        id="no_reasoning_token_streaming",
+    ),
+    pytest.param(
+        False,
+        MULTIPLE_LINES,
+        id="multiple_lines",
+    ),
+    pytest.param(
+        True,
+        MULTIPLE_LINES,
+        id="multiple_lines_streaming",
+    ),
+    pytest.param(
+        True,
+        SHORTEST_REASONING,
+        id="shortest",
+    ),
+    pytest.param(
+        False,
+        SHORTEST_REASONING_NO_STREAMING,
+        id="shortest_streaming",
+    ),
+    pytest.param(
+        False,
+        REASONING_WITH_THINK,
+        id="reasoning_with_think",
+    ),
+    pytest.param(
+        True,
+        REASONING_WITH_THINK,
+        id="reasoning_with_think_streaming",
+    ),
+    pytest.param(
+        False,
+        COMPLETE_REASONING_WITH_THINK,
+        id="complete_reasoning_with_think",
+    ),
+    pytest.param(
+        True,
+        COMPLETE_REASONING_WITH_THINK,
+        id="complete_reasoning_with_think_streaming",
+    ),
+    pytest.param(
+        False,
+        MULTIPLE_LINES_WITH_THINK,
+        id="multiple_lines_with_think",
+    ),
+    pytest.param(
+        True,
+        MULTIPLE_LINES_WITH_THINK,
+        id="multiple_lines_with_think_streaming",
+    ),
+    pytest.param(
+        False,
+        SHORTEST_REASONING_NO_STREAMING_WITH_THINK,
+        id="shortest_with_think",
+    ),
+    pytest.param(
+        True,
+        SHORTEST_REASONING_WITH_THINK,
+        id="shortest_with_think_streaming",
+    ),
+    pytest.param(
+        False,
+        THINK_NO_END,
+        id="think_no_end",
+    ),
+    pytest.param(
+        True,
+        THINK_NO_END,
+        id="think_no_end_streaming",
+    ),
+    pytest.param(
+        False,
+        EMPTY,
+        id="empty",
+    ),
+    pytest.param(
+        True,
+        EMPTY_STREAMING,
+        id="empty_streaming",
+    ),
+    pytest.param(
+        False,
+        NEW_LINE,
+        id="new_line",
+    ),
+    pytest.param(
+        True,
+        NEW_LINE_STREAMING,
+        id="new_line_streaming",
+    ),
+]
+
+
+@pytest.mark.parametrize("streaming, param_dict", TEST_CASES)
+def test_mistral_reasoning(
+    streaming: bool,
+    param_dict: dict,
+    mistral_tokenizer: MistralTokenizer,
+):
+    output = param_dict["output"]
+
+    index_think = output.find("[THINK]")
+    len_think = len("[THINK]")
+    index_end_think = output.find("[/THINK]")
+    len_end_think = len("[/THINK]")
+
+    # encode everything to tokens ids
+    output_tokens = []
+    if index_think != -1:
+        output_before_think = output[:index_think]
+        output_tokens += mistral_tokenizer.tokenizer.encode(
+            output_before_think, False, False)
+        output_tokens += [mistral_tokenizer.instruct.BEGIN_THINK]
+
+        if index_end_think != -1:
+            output_middle = output[index_think + len_think:index_end_think]
+            output_after_think = output[index_end_think + len_end_think:]
+            output_tokens += mistral_tokenizer.tokenizer.encode(
+                output_middle, False, False)
+            output_tokens += [mistral_tokenizer.instruct.END_THINK]
+            output_tokens += mistral_tokenizer.tokenizer.encode(
+                output_after_think, False, False)
+        else:
+            output_middle = output[index_think + len_think:]
+            output_tokens += mistral_tokenizer.tokenizer.encode(
+                output_middle, False, False)
+    elif index_end_think != -1:
+        output_before_think = output[:index_end_think]
+        output_after_think = output[index_end_think + len_end_think:]
+        output_tokens += mistral_tokenizer.tokenizer.encode(
+            output_before_think, False, False)
+        output_tokens += [mistral_tokenizer.instruct.END_THINK]
+        output_tokens += mistral_tokenizer.tokenizer.encode(
+            output_after_think, False, False)
+    else:
+        output_tokens += mistral_tokenizer.tokenizer.encode(
+            output, False, False)
+
+    parser: ReasoningParser = ReasoningParserManager.get_reasoning_parser(
+        parser_name)(mistral_tokenizer)
+
+    reasoning, content = run_reasoning_extraction_mistral(parser,
+                                                          output_tokens,
+                                                          streaming=streaming)
+
+    assert reasoning == param_dict["reasoning_content"]
+    assert content == param_dict["content"]
+
+    # Test is_reasoning_end
+    is_reasoning_end = parser.is_reasoning_end(output_tokens)
+    assert is_reasoning_end == param_dict["is_reasoning_end"]
+
+    # Test extract_content
+    if param_dict["content"] is not None:
+        content = parser.extract_content_ids(output_tokens)
+        assert content == mistral_tokenizer.tokenizer.encode(
+            param_dict["content"], bos=False, eos=False)
+    else:
+        content = parser.extract_content_ids(output_tokens)
+        assert content == []

tests/reasoning/utils.py

@@ -6,6 +6,7 @@ from typing import Optional, Union
 from vllm.entrypoints.openai.protocol import (ChatCompletionRequest,
                                               DeltaMessage)
 from vllm.reasoning import ReasoningParser
+from vllm.transformers_utils.tokenizers.mistral import MistralTokenizer
 
 
 class StreamingReasoningReconstructor:
@@ -54,6 +55,32 @@ def run_reasoning_extraction(
         return reasoning, content
 
 
+def run_reasoning_extraction_mistral(
+    reasoning_parser: ReasoningParser,
+    model_output: list[int],
+    request: Union[ChatCompletionRequest, None] = None,
+    streaming: bool = False,
+) -> tuple[Optional[str], Optional[str]]:
+    assert isinstance(reasoning_parser.model_tokenizer,
+                      MistralTokenizer), type(reasoning_parser.model_tokenizer)
+    if streaming:
+        reconstructor = run_reasoning_extraction_streaming_mistral(
+            reasoning_parser,
+            model_output,
+            request,
+        )
+        return (
+            reconstructor.reasoning_content,
+            reconstructor.other_content or None,
+        )
+    else:
+        str_output = reasoning_parser.model_tokenizer.convert_ids_to_tokens(
+            model_output)
+        reasoning, content = run_reasoning_extraction_nonstreaming(
+            reasoning_parser, str_output, request)
+        return reasoning, content
+
+
 def run_reasoning_extraction_nonstreaming(
     reasoning_parser: ReasoningParser,
     model_output: list[str],
@@ -94,3 +121,35 @@ def run_reasoning_extraction_streaming(
         previous_text = current_text
         previous_tokens = current_tokens
     return reconstructor
+
+
+def run_reasoning_extraction_streaming_mistral(
+    reasoning_parser: ReasoningParser,
+    model_deltas: list[int],
+    request: Union[ChatCompletionRequest, None] = None,
+) -> StreamingReasoningReconstructor:
+    assert isinstance(reasoning_parser.model_tokenizer,
+                      MistralTokenizer), type(reasoning_parser.model_tokenizer)
+    request = request or ChatCompletionRequest(messages=[], model="test-model")
+    reconstructor = StreamingReasoningReconstructor()
+    previous_text = ""
+    previous_tokens: list[int] = []
+    for model_delta in model_deltas:
+        token_delta = [model_delta]
+        delta = reasoning_parser.model_tokenizer.convert_ids_to_tokens(
+            [model_delta])[0]
+        current_text = previous_text + delta
+        current_tokens = previous_tokens + token_delta
+        delta_message = reasoning_parser.extract_reasoning_content_streaming(
+            previous_text,
+            current_text,
+            delta,
+            previous_tokens,
+            current_tokens,
+            token_delta,
+        )
+        if delta_message is not None:
+            reconstructor.append_delta(delta_message)
+        previous_text = current_text
+        previous_tokens = current_tokens
+    return reconstructor

vllm/entrypoints/chat_utils.py

@@ -151,6 +151,27 @@ class CustomChatCompletionContentSimpleVideoParam(TypedDict, total=False):
     video_url: Required[str]
 
 
+class CustomThinkCompletionContentParam(TypedDict, total=False):
+    """A Think Completion Content Param that accepts a plain text and a boolean.
+
+    Example:
+    {
+        "thinking": "I am thinking about the answer",
+        "closed": True,
+        "type": "thinking"
+    }
+    """
+
+    thinking: Required[str]
+    """The thinking content."""
+
+    closed: bool
+    """Whether the thinking is closed."""
+
+    type: Required[Literal["thinking"]]
+    """The thinking type."""
+
+
 ChatCompletionContentPartParam: TypeAlias = Union[
     OpenAIChatCompletionContentPartParam, ChatCompletionContentPartAudioParam,
     ChatCompletionContentPartInputAudioParam,
@@ -159,7 +180,8 @@ ChatCompletionContentPartParam: TypeAlias = Union[
     CustomChatCompletionContentSimpleImageParam,
     ChatCompletionContentPartImageEmbedsParam,
     CustomChatCompletionContentSimpleAudioParam,
-    CustomChatCompletionContentSimpleVideoParam, str]
+    CustomChatCompletionContentSimpleVideoParam, str,
+    CustomThinkCompletionContentParam]
 
 
 class CustomChatCompletionMessageParam(TypedDict, total=False):
@@ -938,6 +960,7 @@ _ImageEmbedsParser = partial(cast, ChatCompletionContentPartImageEmbedsParam)
 _InputAudioParser = partial(cast, ChatCompletionContentPartInputAudioParam)
 _RefusalParser = partial(cast, ChatCompletionContentPartRefusalParam)
 _PILImageParser = partial(cast, CustomChatCompletionContentPILImageParam)
+_ThinkParser = partial(cast, CustomThinkCompletionContentParam)
 # Need to validate url objects
 _ImageParser = TypeAdapter(ChatCompletionContentPartImageParam).validate_python
 _AudioParser = TypeAdapter(ChatCompletionContentPartAudioParam).validate_python
@@ -954,6 +977,8 @@ MM_PARSER_MAP: dict[
 ] = {
     "text":
     lambda part: _TextParser(part).get("text", None),
+    "thinking":
+    lambda part: _ThinkParser(part).get("thinking", None),
     "input_text":
     lambda part: _TextParser(part).get("text", None),
     "input_image":
@@ -1100,7 +1125,7 @@ def _parse_chat_message_content_part(
             "with empty / unparsable content.", part, part_type)
         return None
 
-    if part_type in ("text", "input_text", "refusal"):
+    if part_type in ("text", "input_text", "refusal", "thinking"):
         str_content = cast(str, content)
         if wrap_dicts:
             return {'type': 'text', 'text': str_content}

vllm/reasoning/__init__.py

@@ -6,6 +6,7 @@ from .deepseek_r1_reasoning_parser import DeepSeekR1ReasoningParser
 from .glm4_moe_reasoning_parser import Glm4MoeModelReasoningParser
 from .granite_reasoning_parser import GraniteReasoningParser
 from .hunyuan_a13b_reasoning_parser import HunyuanA13BReasoningParser
+from .mistral_reasoning_parser import MistralReasoningParser
 from .qwen3_reasoning_parser import Qwen3ReasoningParser
 
 __all__ = [
@@ -16,4 +17,5 @@ __all__ = [
     "HunyuanA13BReasoningParser",
     "Qwen3ReasoningParser",
     "Glm4MoeModelReasoningParser",
+    "MistralReasoningParser",
 ]

vllm/reasoning/mistral_reasoning_parser.py

@@ -0,0 +1,47 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from vllm.logger import init_logger
+from vllm.reasoning import ReasoningParser, ReasoningParserManager
+from vllm.reasoning.deepseek_r1_reasoning_parser import (
+    DeepSeekR1ReasoningParser)
+from vllm.transformers_utils.tokenizers.mistral import MistralTokenizer
+
+logger = init_logger(__name__)
+
+
+@ReasoningParserManager.register_module("mistral")
+class MistralReasoningParser(DeepSeekR1ReasoningParser):
+    """
+    Reasoning parser for Mistral models.
+
+    The Mistral models uses [THINK]...[/THINK] tokens to denote reasoning
+    text. This parser extracts the reasoning content from the model output.
+    """
+
+    def __init__(self, tokenizer: MistralTokenizer):
+        if not isinstance(tokenizer, MistralTokenizer):
+            raise ValueError(
+                "The tokenizer must be an instance of MistralTokenizer.")
+
+        ReasoningParser.__init__(self, tokenizer)
+
+        if not self.model_tokenizer:
+            raise ValueError(
+                "The model tokenizer must be passed to the ReasoningParser "
+                "constructor during construction.")
+
+        from mistral_common.tokens.tokenizers.base import SpecialTokens
+
+        self.start_token = SpecialTokens.begin_think
+        self.end_token = SpecialTokens.end_think
+
+        self.start_token_id = tokenizer.tokenizer.get_control_token(
+            self.start_token)
+        self.end_token_id = tokenizer.tokenizer.get_control_token(
+            self.end_token)
+
+        if self.start_token_id is None or self.end_token_id is None:
+            raise RuntimeError(
+                "Mistral reasoning parser could not locate think start/end "
+                "tokens in the tokenizer!")

vllm/transformers_utils/tokenizers/mistral.py

@@ -145,6 +145,21 @@ def find_tokenizer_file(files: list[str]):
     return matched_files[0]
 
 
+def _aggregate_content(content: list) -> list[dict[str, Any]]:
+    aggregated_content: list[dict[str, Any]] = []
+    for chunk in content:
+        if chunk.get("type"
+                     ) == "text" and aggregated_content and aggregated_content[
+                         -1].get("type") == "text":
+            aggregated_content[-1]["text"] += "\n\n" + chunk.get("text")
+        else:
+            aggregated_content.append(chunk)
+    if len(aggregated_content) == 1 and aggregated_content[0].get(
+            "type") == "text":
+        content = aggregated_content[0]["text"]
+    return content
+
+
 def make_mistral_chat_completion_request(
         messages: list["ChatCompletionMessageParam"],
         tools: Optional[list[dict[str,
@@ -162,10 +177,10 @@ def make_mistral_chat_completion_request(
 
         # Convert list text content to string
         if message.get("role") in ("assistant", "tool"):
-            content = message.get("content")
+            content: Any = message.get("content")
             if isinstance(content, list):
-                content = "\n".join(chunk.get("text") for chunk in content)
+                content = _aggregate_content(content)
-                message["content"] = content
+            message["content"] = content
 
     # The Mistral client, in comparison to the OpenAI client, requires the
     # "parameters" dict to be present, even if it's empty.
@@ -465,6 +480,8 @@ class MistralTokenizer(TokenizerBase):
         skip_special_tokens: bool = True,
     ) -> list[str]:
         from mistral_common.tokens.tokenizers.base import SpecialTokens
+        from mistral_common.tokens.tokenizers.instruct import (
+            InstructTokenizerV13)
 
         # TODO(Patrick) - potentially allow special tokens to not be skipped
         assert (
@@ -474,10 +491,18 @@ class MistralTokenizer(TokenizerBase):
         assert self.is_tekken or self.is_spm, type(self.tokenizer)
 
         if self.is_tekken:
-            # skip special tokens except tool call
+            # skip special tokens except tool call and think tokens
-            ids = [
+            non_skip_special_tokens = {
-                i for i in ids if i > self.tokenizer.num_special_tokens or i ==
                 self.tokenizer.get_control_token(SpecialTokens.tool_calls)
+            }
+            if isinstance(self.instruct, InstructTokenizerV13):
+                if self.instruct.BEGIN_THINK:
+                    non_skip_special_tokens.add(self.instruct.BEGIN_THINK)
+                if self.instruct.END_THINK:
+                    non_skip_special_tokens.add(self.instruct.END_THINK)
+            ids = [
+                i for i in ids if i > self.tokenizer.num_special_tokens
+                or i in non_skip_special_tokens
             ]
 
         tokens = [self.tokenizer.id_to_piece(id) for id in ids]