dsv4.1: chat encoding and tool parsing (#39665)

Co-authored-by: BBuf <1182563586@qq.com>

docs/docs/references/environment_variables.mdx

@@ -755,6 +755,11 @@ SGLang supports various environment variables that can be used to configure its
       <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Default <code>reasoning_effort</code> for the DeepSeek V4 chat encoder when a request does not set it. The preview profile accepts <code>high</code> and <code>max</code>; the official profile accepts <code>low</code>, <code>high</code>, and <code>max</code>. The profile is detected from the bundled encoder. Override it with <code>--json-model-override-args '&#123;"dsv4_reasoning_effort_profile":"official"&#125;'</code>.</td>
       <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}><code>""</code></td>
     </tr>
+    <tr>
+      <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>SGLANG_DSV41_REASONING_EFFORT</code></td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>Default <code>reasoning_effort</code> for the DeepSeek-V4.1 chat encoder when a request does not set it: one of <code>low</code>, <code>high</code>, <code>xhigh</code>, <code>max</code>, or an integer budget in [1, 100]. Unset means the encoder default <code>high</code>.</td>
+      <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.02)"}}><code>""</code></td>
+    </tr>
     <tr>
       <td style={{padding: "9px 12px", fontWeight: 500, backgroundColor: "rgba(255,255,255,0.02)"}}><code>SGLANG_DSV4_USE_BF16_KV_QUANT_SOURCE</code></td>
       <td style={{padding: "9px 12px", backgroundColor: "rgba(255,255,255,0.05)"}}>For DeepSeek V4, quantize the SWA FP8 KV cache from BF16-rounded values instead of FP32 registers. This matches trainer-side QAT and the DSA prefill-CP path, at the cost of an extra BF16 KV materialization and separate cache-store kernels.</td>

python/sglang/srt/entrypoints/openai/chat_encoding.py

@@ -10,9 +10,9 @@ from __future__ import annotations
 import ast
 import logging
 from pathlib import Path
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Optional, Union
 
-from sglang.srt.entrypoints.openai import encoding_dsv4
+from sglang.srt.entrypoints.openai import encoding_dsv4, encoding_dsv41
 
 logger = logging.getLogger(__name__)
 
@@ -105,6 +105,13 @@ def resolve_dsv4_reasoning_effort_profile(
     )
 
 
+def is_deepseek_v41_arch(*, arch: str, model_type: str) -> bool:
+    """Check model_type before matching the DeepseekV4 architecture substring;
+    V4.1 configs can also use the V4 architecture name.
+    """
+    return model_type == "deepseek_v41" or "DeepseekV41" in arch
+
+
 def resolve_chat_encoding_spec(
     *,
     hf_config: Any,
@@ -116,6 +123,8 @@ def resolve_chat_encoding_spec(
     None means the default path (HF chat template); any non-None spec also owns
     reasoning-history rendering (:func:`spec_owns_reasoning_history`).
     """
+    if tool_call_parser == "deepseekv41":
+        return "dsv41"
     if tool_call_parser == "deepseekv4":
         return "dsv4"
     if tool_call_parser == "deepseekv32":
@@ -126,6 +135,8 @@ def resolve_chat_encoding_spec(
     architectures = hf_config.architectures
     arch = architectures[0] if architectures else ""
 
+    if is_deepseek_v41_arch(arch=arch, model_type=hf_config.model_type):
+        return "dsv41"
     if "DeepseekV4" in arch:
         return "dsv4"
     if "KimiK3" in arch:
@@ -143,6 +154,56 @@ def resolve_chat_encoding_spec(
     return None
 
 
+def parse_dsv41_reasoning_effort(value: Any) -> Union[str, int, None]:
+    """Map an API ``reasoning_effort`` onto what the V4.1 encoder accepts.
+
+    An int budget only reaches here through ``chat_template_kwargs``; None
+    means unsupported, and the caller applies its default.
+    """
+    if isinstance(value, bool):
+        return None
+    if isinstance(value, int):
+        return value if 1 <= value <= 100 else None
+    if isinstance(value, float):
+        return max(1, round(value * 100)) if 0.0 <= value <= 0.99 else None
+    if value in encoding_dsv41.REASONING_EFFORT_MAPPINGS:
+        return value
+    return None
+
+
+_OPENAI_FUNCTION_FIELD_ORDER = ("name", "description", "parameters")
+
+
+def dsv41_tool_payload(tool: Any) -> Dict[str, Any]:
+    """The tool dict the V4.1 encoder renders verbatim into the prompt.
+
+    Only fields the client sent, in the OpenAI field order; pydantic would
+    otherwise add defaults (strict=false) and reorder keys by declaration.
+    """
+    payload = tool.model_dump(exclude_unset=True, exclude_none=True)
+    function = dict(payload.get("function") or {})
+    ordered = {
+        k: function.pop(k) for k in _OPENAI_FUNCTION_FIELD_ORDER if k in function
+    }
+    ordered.update(function)
+    payload["function"] = ordered
+    return payload
+
+
+def default_dsv41_reasoning_effort_from_env(raw: Optional[str]) -> Union[str, int]:
+    """Parse ``SGLANG_DSV41_REASONING_EFFORT``; raises so a bad value fails at boot."""
+    if raw is None or not raw.strip():
+        return encoding_dsv41.DEFAULT_REASONING_EFFORT
+    value: Any = int(raw) if raw.strip().isdigit() else raw.strip()
+    effort = parse_dsv41_reasoning_effort(value)
+    if effort is None:
+        raise ValueError(
+            f"Invalid SGLANG_DSV41_REASONING_EFFORT={raw!r}; expected one of "
+            f"{list(encoding_dsv41.REASONING_EFFORT_MAPPINGS)} or an integer in [1, 100]"
+        )
+    return effort
+
+
 def spec_owns_reasoning_history(spec: Optional[str]) -> bool:
     """Whether the encoder for ``spec`` renders assistant reasoning history itself.
 
@@ -163,7 +224,7 @@ def spec_renders_prompt_ids(spec: Optional[str]) -> bool:
     Token-first encoders leave the text prompt empty; the MM processor
     expands their single placeholder ids rather than re-tokenizing text.
     """
-    return spec in ("inkling", "kimi_k3")
+    return spec in ("inkling", "kimi_k3", "dsv41")
 
 
 def encode_simple_chat(
@@ -177,11 +238,9 @@ def encode_simple_chat(
 
     Minimal encode for offline tools: no tools, no multimodal content, no
     continue_final_message; the serving path keeps its full request-level
-    pipeline in ``serving_chat``. Like
-    ``serving_chat``, an empty system message is prepended when the
-    conversation does not start with one (for the dsv4/dsv32 encoders this
-    currently renders to zero tokens, but keeping the insertion explicit ties
-    this helper to the serving semantics rather than to that coincidence).
+    pipeline in ``serving_chat``. System-message handling matches
+    ``serving_chat``: dsv4/dsv32 get an empty one prepended, dsv41 does not
+    (it renders a system token even for empty content).
     """
     if spec == "inkling":
         from sglang.srt.parser.inkling_renderer import render_inkling_messages
@@ -193,8 +252,8 @@ def encode_simple_chat(
             add_generation_prompt=False,
         )
 
-    if spec in ("dsv4", "dsv32"):
-        if messages and messages[0]["role"] != "system":
+    if spec in ("dsv4", "dsv32", "dsv41"):
+        if spec != "dsv41" and messages and messages[0]["role"] != "system":
             messages = [{"role": "system", "content": ""}] + list(messages)
         if spec == "dsv4":
             from sglang.srt.entrypoints.openai import encoding_dsv4
@@ -202,6 +261,10 @@ def encode_simple_chat(
             real_input = encoding_dsv4.encode_messages(
                 messages, thinking_mode=thinking_mode
             )
+        elif spec == "dsv41":
+            real_input = encoding_dsv41.encode_messages(
+                messages, thinking_mode=thinking_mode
+            )
         else:
             from sglang.srt.entrypoints.openai import encoding_dsv32
 

python/sglang/srt/entrypoints/openai/encoding_dsv41.py

@@ -0,0 +1,705 @@
+# Adapted from the DeepSeek-V4.1 release reference implementation.
+"""Encode DeepSeek-V4.1 chat messages.
+
+Mid-conversation system messages trigger the assistant generation header.
+"""
+
+import copy
+import json
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+# ============================================================
+# Special Tokens
+# ============================================================
+
+bos_token: str = "<｜begin▁of▁sentence｜>"
+eos_token: str = "<｜end▁of▁sentence｜>"
+thinking_start_token: str = "<think>"
+thinking_end_token: str = "</think>"
+dsml_token: str = "｜DSML｜"
+
+USER_SP_TOKEN = "<｜User｜>"
+ASSISTANT_SP_TOKEN = "<｜Assistant｜>"
+SYSTEM_SP_TOKEN = "<｜System｜>"
+LATEST_REMINDER_SP_TOKEN = "<｜latest_reminder｜>"
+
+IMAGE_PLACEHOLDER = "<｜deepseek_image｜>"
+
+# Task special tokens for internal classification tasks
+DS_TASK_SP_TOKENS = {
+    "action": "<｜action｜>",
+    "query": "<｜query｜>",
+    "authority": "<｜authority｜>",
+    "domain": "<｜domain｜>",
+    "title": "<｜title｜>",
+    "read_url": "<｜read_url｜>",
+}
+VALID_TASKS = set(DS_TASK_SP_TOKENS.keys())
+
+# ============================================================
+# Templates
+# ============================================================
+
+system_msg_template: str = "{content}"
+user_msg_template: str = "{content}"
+latest_reminder_msg_template: str = "{content}"
+assistant_msg_template: str = "{reasoning}{content}{tool_calls}" + eos_token
+assistant_msg_wo_eos_template: str = "{reasoning}{content}{tool_calls}"
+thinking_template: str = "{reasoning_content}"
+
+response_format_template: str = "## Response Format:\n\nYou MUST strictly adhere to the following schema to reply:\n{schema}"
+
+tool_calls_block_name: str = " calls"
+tool_call_tag_name: str = " invoke"
+tool_parameter_tag_name: str = " parameter"
+
+tool_call_template: str = '<{dsml_token}{tool_call_tag_name} name="{name}">\n{arguments}\n</{dsml_token}{tool_call_tag_name}>'
+tool_calls_template = (
+    "<{dsml_token}{tc_block_name}>\n{tool_calls}\n</{dsml_token}{tc_block_name}>"
+)
+
+tool_output_template: str = "<tool_result>{content}</tool_result>"
+
+REASONING_EFFORT_TEMPLATE = (
+    "Reasoning Effort: {budget} "
+    "(range 1-100, the higher the value, the more thorough the reasoning)\n\n"
+)
+
+REASONING_EFFORT_MAPPINGS: Dict[str, int] = {
+    "low": 25,
+    "high": 50,
+    "xhigh": 75,
+    "max": 100,
+}
+DEFAULT_REASONING_EFFORT = "high"
+
+TOOLS_TEMPLATE = """## Tools
+
+You have access to a set of tools to help answer the user's question. You can invoke tools by writing a "<{dsml_token}{tc_block_name}>" block like the following:
+
+<{dsml_token}{tc_block_name}>
+<{dsml_token}{tool_call_tag_name} name="$TOOL_NAME">
+<{dsml_token}{tool_parameter_tag_name} name="$PARAMETER_NAME" string="true|false">$PARAMETER_VALUE</{dsml_token}{tool_parameter_tag_name}>
+...
+</{dsml_token}{tool_call_tag_name}>
+<{dsml_token}{tool_call_tag_name} name="$TOOL_NAME2">
+...
+</{dsml_token}{tool_call_tag_name}>
+</{dsml_token}{tc_block_name}>
+
+String parameters should be specified as is and set `string="true"`. For all other types (numbers, booleans, arrays, objects), pass the value in JSON format and set `string="false"`.
+
+If thinking_mode is enabled (triggered by {thinking_start_token}), you MUST output your complete reasoning inside {thinking_start_token}...{thinking_end_token} BEFORE any tool calls or final response.
+
+Otherwise, output directly after {thinking_end_token} with tool calls or final response.
+
+### Available Tool Schemas
+
+{tool_schemas}
+
+You MUST strictly follow the above defined tool name and parameter schemas to invoke tool calls.
+"""
+
+# ============================================================
+# Utility Functions
+# ============================================================
+
+
+def to_json(value: Any) -> str:
+    try:
+        return json.dumps(value, ensure_ascii=False)
+    except:
+        return json.dumps(value, ensure_ascii=True)
+
+
+def tools_from_openai_format(tools):
+    return [tool["function"] for tool in tools]
+
+
+def tool_calls_from_openai_format(tool_calls):
+    return [
+        {
+            "name": tool_call["function"]["name"],
+            "arguments": tool_call["function"]["arguments"],
+        }
+        for tool_call in tool_calls
+    ]
+
+
+def encode_arguments_to_dsml(tool_call: Dict[str, Any]) -> str:
+    p_dsml_template = (
+        '<{dsml_token}{tool_parameter_tag_name} name="{key}" string="{is_str}">'
+        "{value}</{dsml_token}{tool_parameter_tag_name}>"
+    )
+    P_dsml_strs = []
+
+    raw_arguments = tool_call["arguments"]
+    arguments = (
+        json.loads(raw_arguments) if isinstance(raw_arguments, str) else raw_arguments
+    )
+    if not isinstance(arguments, dict):
+        raise ValueError(
+            "Assistant tool call function.arguments must be a JSON object."
+        )
+
+    for k, v in arguments.items():
+        P_dsml_strs.append(
+            p_dsml_template.format(
+                dsml_token=dsml_token,
+                tool_parameter_tag_name=tool_parameter_tag_name,
+                key=k,
+                is_str="true" if isinstance(v, str) else "false",
+                value=v if isinstance(v, str) else to_json(v),
+            )
+        )
+
+    return "\n".join(P_dsml_strs)
+
+
+def render_tools(tools: List[Dict[str, Union[str, Dict[str, Any]]]]) -> str:
+    tools_json = [to_json(t) for t in tools]
+
+    return TOOLS_TEMPLATE.format(
+        tool_schemas="\n".join(tools_json),
+        dsml_token=dsml_token,
+        tc_block_name=tool_calls_block_name,
+        tool_call_tag_name=tool_call_tag_name,
+        tool_parameter_tag_name=tool_parameter_tag_name,
+        thinking_start_token=thinking_start_token,
+        thinking_end_token=thinking_end_token,
+    )
+
+
+def render_reasoning_effort(
+    index: int,
+    thinking_mode: str,
+    effort: Union[str, int, None],
+) -> str:
+    """Render the numeric reasoning effort prefix (thinking mode, index 0 only)."""
+    if effort is None:
+        effort = DEFAULT_REASONING_EFFORT
+    if not (
+        (type(effort) is int and 1 <= effort <= 100)
+        or effort in REASONING_EFFORT_MAPPINGS
+    ):
+        raise ValueError(
+            f"Invalid reasoning effort for deepseek_v41: {effort!r}, should be "
+            f"int within [1,100] or {list(REASONING_EFFORT_MAPPINGS)}"
+        )
+    if type(effort) is str:
+        effort = REASONING_EFFORT_MAPPINGS[effort]
+    if index == 0 and thinking_mode == "thinking":
+        return REASONING_EFFORT_TEMPLATE.format(budget=effort)
+    return ""
+
+
+def find_last_user_index(messages: List[Dict[str, Any]]) -> int:
+    """Mid-conversation system messages also count as user messages here;
+    they trigger the assistant generation header.
+    """
+    last_user_index = -1
+    for idx in range(len(messages) - 1, -1, -1):
+        role = messages[idx].get("role")
+        if role in ["user", "developer"] or (role == "system" and idx > 0):
+            last_user_index = idx
+            break
+    return last_user_index
+
+
+def attach_task_to_last_user_message(messages: List[Dict[str, Any]], task: str) -> None:
+    """Set `task` on the most recent user/developer message; raise if none exists."""
+    idx = find_last_user_index(messages)
+    if idx == -1:
+        raise ValueError(
+            "`task` requires at least one message with role='user' or 'developer'."
+        )
+    messages[idx]["task"] = task
+
+
+# ============================================================
+# Message Rendering
+# ============================================================
+
+
+def render_message(
+    index: int,
+    messages: List[Dict[str, Any]],
+    thinking_mode: str,
+    drop_thinking: bool = True,
+    reasoning_effort: Union[str, int, None] = None,
+) -> str:
+    assert 0 <= index < len(messages)
+    assert thinking_mode in [
+        "chat",
+        "thinking",
+    ], f"Invalid thinking_mode `{thinking_mode}`"
+
+    msg = messages[index]
+    last_user_idx = find_last_user_index(messages)
+
+    role = msg.get("role")
+    content = msg.get("content")
+    tools = msg.get("tools")
+    response_format = msg.get("response_format")
+    tool_calls = msg.get("tool_calls")
+    reasoning_content = msg.get("reasoning_content")
+    wo_eos = msg.get("wo_eos", False)
+
+    if tools:
+        tools = tools_from_openai_format(tools)
+    if tool_calls:
+        tool_calls = tool_calls_from_openai_format(tool_calls)
+
+    reasoning_effort_prompt = render_reasoning_effort(
+        index, thinking_mode, reasoning_effort
+    )
+    # Index 0 also emits the system token for the effort prompt, even before a user.
+    prompt = (
+        SYSTEM_SP_TOKEN
+        if index == 0 and (reasoning_effort_prompt or role == "system")
+        else ""
+    )
+    prompt += reasoning_effort_prompt
+
+    if role == "system":
+        if index > 0:
+            prompt += SYSTEM_SP_TOKEN
+        prompt += system_msg_template.format(content=content or "")
+        if tools:
+            prompt += "\n\n" + render_tools(tools)
+        if response_format:
+            prompt += "\n\n" + response_format_template.format(
+                schema=to_json(response_format)
+            )
+
+    elif role == "developer":
+        assert content, f"Invalid message for role `{role}`: {msg}"
+
+        content_developer = USER_SP_TOKEN
+        content_developer += content
+
+        if tools:
+            content_developer += "\n\n" + render_tools(tools)
+        if response_format:
+            content_developer += "\n\n" + response_format_template.format(
+                schema=to_json(response_format)
+            )
+
+        prompt += user_msg_template.format(content=content_developer)
+
+    elif role == "user":
+        prompt += USER_SP_TOKEN
+
+        # Handle content blocks (tool results mixed with text)
+        content_blocks = msg.get("content_blocks")
+        if content_blocks:
+            parts = []
+            for block in content_blocks:
+                block_type = block.get("type")
+                if block_type == "text":
+                    parts.append(block.get("text", ""))
+                elif block_type == "tool_result":
+                    tool_content = block.get("content", "")
+                    if isinstance(tool_content, list):
+                        text_parts = []
+                        for b in tool_content:
+                            if b.get("type") == "text":
+                                text_parts.append(b.get("text", ""))
+                            else:
+                                text_parts.append(f"[Unsupported {b.get('type')}]")
+                        tool_content = "\n\n".join(text_parts)
+                    parts.append(tool_output_template.format(content=tool_content))
+                else:
+                    parts.append(f"[Unsupported {block_type}]")
+            prompt += "\n\n".join(parts)
+        else:
+            prompt += content or ""
+
+    elif role == "latest_reminder":
+        prompt += LATEST_REMINDER_SP_TOKEN + latest_reminder_msg_template.format(
+            content=content
+        )
+
+    elif role == "tool":
+        raise NotImplementedError(
+            "deepseek_v41 merges tool messages into user; please preprocess with merge_tool_messages()"
+        )
+
+    elif role == "assistant":
+        thinking_part = ""
+        tc_content = ""
+
+        if tool_calls:
+            tc_list = [
+                tool_call_template.format(
+                    dsml_token=dsml_token,
+                    tool_call_tag_name=tool_call_tag_name,
+                    name=tc.get("name"),
+                    arguments=encode_arguments_to_dsml(tc),
+                )
+                for tc in tool_calls
+            ]
+            tc_content += "\n\n" + tool_calls_template.format(
+                dsml_token=dsml_token,
+                tool_calls="\n".join(tc_list),
+                tc_block_name=tool_calls_block_name,
+            )
+
+        summary_content = content or ""
+        rc = reasoning_content or ""
+
+        # Check if previous message has a task - if so, this is a task output (no thinking)
+        prev_has_task = index - 1 >= 0 and messages[index - 1].get("task") is not None
+
+        if thinking_mode == "thinking" and not prev_has_task:
+            if not drop_thinking or index > last_user_idx:
+                thinking_part = (
+                    thinking_template.format(reasoning_content=rc) + thinking_end_token
+                )
+            else:
+                thinking_part = ""
+
+        if wo_eos:
+            prompt += assistant_msg_wo_eos_template.format(
+                reasoning=thinking_part,
+                content=summary_content,
+                tool_calls=tc_content,
+            )
+        else:
+            prompt += assistant_msg_template.format(
+                reasoning=thinking_part,
+                content=summary_content,
+                tool_calls=tc_content,
+            )
+    else:
+        raise NotImplementedError(f"Unknown role: {role}")
+
+    # Append transition tokens based on what follows
+    if index + 1 < len(messages) and messages[index + 1].get("role") not in [
+        "assistant",
+        "latest_reminder",
+    ]:
+        return prompt
+
+    task = messages[index].get("task")
+    if task is not None:
+        # Task special token for internal classification tasks
+        assert task in VALID_TASKS, (
+            f"Invalid task: '{task}'. Valid tasks are: {list(VALID_TASKS)}"
+        )
+        task_sp_token = DS_TASK_SP_TOKENS[task]
+
+        if task != "action":
+            # Non-action tasks: append task sp token directly after the message
+            prompt += task_sp_token
+        else:
+            # Action task: append Assistant + thinking token + action sp token
+            prompt += ASSISTANT_SP_TOKEN
+            prompt += (
+                thinking_end_token
+                if thinking_mode != "thinking"
+                else thinking_start_token
+            )
+            prompt += task_sp_token
+
+    elif messages[index].get("role") in ["user", "developer"] or (
+        messages[index].get("role") == "system" and index > 0
+    ):
+        # Normal generation: append Assistant + thinking token
+        prompt += ASSISTANT_SP_TOKEN
+        if not drop_thinking and thinking_mode == "thinking":
+            prompt += thinking_start_token
+        elif drop_thinking and thinking_mode == "thinking" and index >= last_user_idx:
+            prompt += thinking_start_token
+        else:
+            prompt += thinking_end_token
+
+    return prompt
+
+
+# ============================================================
+# Preprocessing
+# ============================================================
+
+
+def merge_tool_messages(messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    """Tool results are encoded within user messages;
+    DeepSeek-V4.1 has no standalone tool role.
+    """
+    merged: List[Dict[str, Any]] = []
+
+    for msg in messages:
+        msg = copy.deepcopy(msg)
+        role = msg.get("role")
+
+        if role == "tool":
+            # Convert tool message to a user message with tool_result block
+            tool_block = {
+                "type": "tool_result",
+                "tool_use_id": msg.get("tool_call_id", ""),
+                "content": msg.get("content", ""),
+            }
+            # Merge into previous message if it's already a user (merged tool)
+            if (
+                merged
+                and merged[-1].get("role") == "user"
+                and "content_blocks" in merged[-1]
+            ):
+                merged[-1]["content_blocks"].append(tool_block)
+            else:
+                merged.append(
+                    {
+                        "role": "user",
+                        "content_blocks": [tool_block],
+                    }
+                )
+        elif role == "user":
+            content_blocks = msg.get("content_blocks")
+            if content_blocks is None:
+                content_blocks = [{"type": "text", "text": msg.get("content", "")}]
+            if (
+                merged
+                and merged[-1].get("role") == "user"
+                and "content_blocks" in merged[-1]
+                and merged[-1].get("task") is None
+            ):
+                merged[-1]["content_blocks"].extend(content_blocks)
+            else:
+                # Keeps structured content and every message-level field.
+                new_msg = msg
+                new_msg["content_blocks"] = content_blocks
+                merged.append(new_msg)
+        else:
+            merged.append(msg)
+
+    return merged
+
+
+def sort_tool_results_by_call_order(
+    messages: List[Dict[str, Any]],
+) -> List[Dict[str, Any]]:
+    last_tool_call_order: Dict[str, int] = {}
+
+    for msg in messages:
+        role = msg.get("role")
+        if role == "assistant" and msg.get("tool_calls"):
+            last_tool_call_order = {}
+            for idx, tc in enumerate(msg["tool_calls"]):
+                tc_id = tc.get("id") or tc.get("function", {}).get("id", "")
+                if tc_id:
+                    last_tool_call_order[tc_id] = idx
+
+        elif role == "user" and msg.get("content_blocks"):
+            tool_blocks = [
+                b for b in msg["content_blocks"] if b.get("type") == "tool_result"
+            ]
+            if len(tool_blocks) > 1 and last_tool_call_order:
+                sorted_blocks = sorted(
+                    tool_blocks,
+                    key=lambda b: last_tool_call_order.get(b.get("tool_use_id", ""), 0),
+                )
+                sorted_idx = 0
+                new_blocks = []
+                for block in msg["content_blocks"]:
+                    if block.get("type") == "tool_result":
+                        new_blocks.append(sorted_blocks[sorted_idx])
+                        sorted_idx += 1
+                    else:
+                        new_blocks.append(block)
+                msg["content_blocks"] = new_blocks
+
+    return messages
+
+
+# ============================================================
+# Vision Message Preprocessing
+# ============================================================
+
+
+def _is_image_block(block: Dict[str, Any]) -> bool:
+    return isinstance(block, dict) and block.get("type") == "image_url"
+
+
+def _extract_image(block: Dict[str, Any]) -> Dict[str, Any]:
+    image_url = block.get("image_url")
+    url = image_url if isinstance(image_url, str) else (image_url or {}).get("url", "")
+    if not url:
+        raise ValueError("Image block does not contain a valid source")
+    return {"url": url}
+
+
+def _process_image_blocks(
+    blocks: List[Any], image_placeholder: str = IMAGE_PLACEHOLDER
+) -> Tuple[List[Any], List[Dict[str, Any]]]:
+    new_blocks: List[Any] = []
+    images: List[Dict[str, Any]] = []
+    for block in blocks:
+        if not isinstance(block, dict):
+            new_blocks.append(block)
+            continue
+        if _is_image_block(block):
+            new_blocks.append({"type": "text", "text": image_placeholder})
+            images.append(_extract_image(block))
+        elif block.get("type") == "tool_result" and isinstance(
+            block.get("content"), list
+        ):
+            block = copy.copy(block)
+            block["content"], nested_images = _process_image_blocks(
+                block["content"], image_placeholder
+            )
+            new_blocks.append(block)
+            images.extend(nested_images)
+        elif block.get("type") == "text":
+            text = block.get("text") or ""
+            if IMAGE_PLACEHOLDER in text:
+                raise ValueError(
+                    f"Text block contains image placeholder '{IMAGE_PLACEHOLDER}': "
+                    f"'{text[:100]}'. Images should be separate content blocks."
+                )
+            new_blocks.append(block)
+        else:
+            new_blocks.append(block)
+    return new_blocks, images
+
+
+def _validate_no_image_sp_tokens(msg: Dict[str, Any]) -> None:
+    content = msg.get("content")
+    if isinstance(content, str) and IMAGE_PLACEHOLDER in content:
+        raise ValueError(
+            f"Message content contains image special token '{IMAGE_PLACEHOLDER}'. "
+            "Images should be provided as image content blocks."
+        )
+    reasoning_content = msg.get("reasoning_content")
+    if isinstance(reasoning_content, str) and IMAGE_PLACEHOLDER in reasoning_content:
+        raise ValueError(
+            f"reasoning_content contains image special token '{IMAGE_PLACEHOLDER}'"
+        )
+
+
+def process_image_messages(
+    messages: List[Dict[str, Any]],
+) -> Tuple[List[Dict[str, Any]], List[Dict[str, Any]]]:
+    processed: List[Dict[str, Any]] = []
+    images: List[Dict[str, Any]] = []
+    for msg in messages:
+        msg = copy.deepcopy(msg)
+        _validate_no_image_sp_tokens(msg)
+
+        if isinstance(msg.get("content"), list) and "content_blocks" not in msg:
+            msg["content_blocks"] = msg.pop("content")
+
+        if msg.get("content_blocks"):
+            msg["content_blocks"], message_images = _process_image_blocks(
+                msg["content_blocks"]
+            )
+            images.extend(message_images)
+            if not isinstance(msg.get("content"), str):
+                texts = [
+                    block.get("text", "")
+                    for block in msg["content_blocks"]
+                    if isinstance(block, dict) and block.get("type") == "text"
+                ]
+                msg["content"] = "\n\n".join(texts)
+
+        processed.append(msg)
+    return processed, images
+
+
+# ============================================================
+# Main Encoding Function
+# ============================================================
+
+
+def _drop_thinking_messages(messages: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+    last_user_idx = find_last_user_index(messages)
+    result = []
+    keep_roles = {"user", "system", "tool", "latest_reminder", "direct_search_results"}
+
+    for idx, msg in enumerate(messages):
+        role = msg.get("role")
+        if role in keep_roles or idx >= last_user_idx:
+            result.append(msg)
+        elif role == "assistant":
+            msg = copy.copy(msg)
+            msg.pop("reasoning_content", None)
+            result.append(msg)
+        # developer and other roles before last_user_idx are dropped
+
+    return result
+
+
+def _encode_messages_text(
+    messages: List[Dict[str, Any]],
+    thinking_mode: str,
+    context: Optional[List[Dict[str, Any]]] = None,
+    drop_thinking: bool = True,
+    add_default_bos_token: bool = True,
+    reasoning_effort: Union[str, int, None] = None,
+) -> str:
+    """Encode preprocessed (text-only) messages into the V4.1 prompt format."""
+    context = context if context else []
+
+    # Preprocess: merge tool messages and sort tool results
+    messages = merge_tool_messages(messages)
+    messages = sort_tool_results_by_call_order(context + messages)[len(context) :]
+    if context:
+        context = merge_tool_messages(context)
+        context = sort_tool_results_by_call_order(context)
+
+    full_messages = context + messages
+
+    prompt = bos_token if add_default_bos_token and len(context) == 0 else ""
+
+    # Resolve drop_thinking: if any message has tools defined, don't drop thinking
+    effective_drop_thinking = drop_thinking
+    if any(m.get("tools") for m in full_messages):
+        effective_drop_thinking = False
+
+    if thinking_mode == "thinking" and effective_drop_thinking:
+        full_messages = _drop_thinking_messages(full_messages)
+        num_to_render = len(full_messages) - len(_drop_thinking_messages(context))
+        context_len = len(full_messages) - num_to_render
+    else:
+        num_to_render = len(messages)
+        context_len = len(context)
+
+    for idx in range(num_to_render):
+        prompt += render_message(
+            idx + context_len,
+            full_messages,
+            thinking_mode=thinking_mode,
+            drop_thinking=effective_drop_thinking,
+            reasoning_effort=reasoning_effort,
+        )
+
+    return prompt
+
+
+def encode_messages(
+    messages: List[Dict[str, Any]],
+    thinking_mode: str,
+    context: Optional[List[Dict[str, Any]]] = None,
+    drop_thinking: bool = True,
+    add_default_bos_token: bool = True,
+    reasoning_effort: Union[str, int, None] = None,
+    return_multi_modal_data: bool = False,
+) -> Any:
+    """Encode a list of messages into the DeepSeek-V4.1 prompt format.
+
+    Returns the prompt string, or ``(prompt, {"images": [...]})`` when
+    ``return_multi_modal_data`` is set; the image records are in prompt order.
+    """
+    context = context or []
+    processed_context, _ = process_image_messages(context) if context else ([], [])
+    processed_messages, images = process_image_messages(messages)
+    prompt = _encode_messages_text(
+        processed_messages,
+        thinking_mode=thinking_mode,
+        context=processed_context if processed_context else None,
+        drop_thinking=drop_thinking,
+        add_default_bos_token=add_default_bos_token,
+        reasoning_effort=reasoning_effort,
+    )
+    if return_multi_modal_data:
+        return prompt, {"images": images}
+    return prompt

python/sglang/srt/entrypoints/openai/protocol.py

@@ -914,7 +914,7 @@ class ChatCompletionRequest(BaseModel):
         description="DeepSeek-V4 quick instruction task. When set, the last "
         "user/developer message is treated as a single-shot classification prompt "
         "and the corresponding task special token (e.g. `<｜domain｜>`) is appended "
-        "before generation. Only honored by the dsv4 chat encoder; ignored otherwise.",
+        "before generation. Only honored by the dsv4/dsv41 chat encoders; ignored otherwise.",
     )
 
     # Extra parameters for SRT backend only and will be ignored by OpenAI models.

python/sglang/srt/entrypoints/openai/serving_chat.py

@@ -39,7 +39,12 @@ _CHAT_TEMPLATE_CLIENT_ERRORS: tuple[type[BaseException], ...] = (
 from fastapi.responses import ORJSONResponse, StreamingResponse
 from jsonschema import Draft202012Validator, SchemaError
 
-from sglang.srt.entrypoints.openai import chat_encoding, encoding_dsv4, encoding_dsv32
+from sglang.srt.entrypoints.openai import (
+    chat_encoding,
+    encoding_dsv4,
+    encoding_dsv32,
+    encoding_dsv41,
+)
 from sglang.srt.entrypoints.openai.protocol import (
     ChatCompletionMessageContentTextPart,
     ChatCompletionMessageContentVideoPart,
@@ -104,6 +109,7 @@ from sglang.srt.parser.reasoning_parser import ReasoningParser
 from sglang.srt.sampling.sampling_params import (
     set_request_reasoning_end_token_ids,
 )
+from sglang.srt.utils import ImageData
 from sglang.srt.utils.weight_versions import build_endpoint_weight_version_metadata
 
 if TYPE_CHECKING:
@@ -338,6 +344,13 @@ class OpenAIServingChat(OpenAIServingBase):
             if self.chat_encoding_spec == "inkling"
             else None
         )
+        self._dsv41_default_reasoning_effort: Optional[Union[str, int]] = (
+            chat_encoding.default_dsv41_reasoning_effort_from_env(
+                envs.SGLANG_DSV41_REASONING_EFFORT.get()
+            )
+            if self.chat_encoding_spec == "dsv41"
+            else None
+        )
 
         # Per-request response parser for custom decoding (set by _encode_messages)
         self._response_parser: Optional[ResponseParserProtocol] = None
@@ -685,6 +698,21 @@ class OpenAIServingChat(OpenAIServingBase):
             raise ValueError("Inkling reasoning_effort must be in [0.0, 0.99]")
         return parsed
 
+    def _resolve_dsv41_reasoning_effort(self, value: Any) -> Union[str, int]:
+        """Request effort for the V4.1 encoder; unsupported values warn and fall back."""
+        effort = chat_encoding.parse_dsv41_reasoning_effort(value)
+        if effort is not None:
+            return effort
+        if value is not None and value != "none":
+            logger.warning(
+                "DeepSeek-V4.1 does not support reasoning_effort=%r; using the "
+                "default %r (low/high/xhigh/max, a float in [0, 0.99], or an "
+                "integer budget in [1, 100] via chat_template_kwargs are accepted).",
+                value,
+                self._dsv41_default_reasoning_effort,
+            )
+        return self._dsv41_default_reasoning_effort
+
     @staticmethod
     def _get_inkling_default_reasoning_effort() -> float:
         """Read the default Inkling reasoning effort from the environment."""
@@ -1407,43 +1435,54 @@ class OpenAIServingChat(OpenAIServingBase):
                         modalities,
                     )
         elif self.chat_encoding_spec is not None:
-            # dsv4/dsv32 encoding path
+            # dsv4/dsv41/dsv32 encoding path
             messages = copy.deepcopy(messages)
-
-            # dsv4/dsv32 are text-only and consume string content; flatten
-            # OpenAI parts-list content here so the encoder sees a plain string.
-            for i, msg in enumerate(messages):
-                if isinstance(msg.get("content"), list):
-                    messages[i] = process_content_for_template_format(
-                        msg, "string", [], [], [], []
-                    )
-
+            is_dsv41 = self.chat_encoding_spec == "dsv41"
             for msg in messages:
                 if msg.get("content") is None:
                     msg["content"] = ""
-                processed_msg = process_content_for_template_format(
-                    msg,
-                    template_content_format,
-                    image_data,
-                    video_data,
-                    audio_data,
-                    modalities,
-                    use_dpsk_v32_encoding=self.chat_encoding_spec == "dsv32",
-                )
-                msg.update(processed_msg)
+
+            # The V4.1 encoder consumes OpenAI parts lists itself; dsv4/dsv32
+            # are text-only, so their parts-list content is flattened first.
+            if not is_dsv41:
+                for i, msg in enumerate(messages):
+                    if isinstance(msg.get("content"), list):
+                        messages[i] = process_content_for_template_format(
+                            msg, "string", [], [], [], []
+                        )
+
+                for msg in messages:
+                    processed_msg = process_content_for_template_format(
+                        msg,
+                        template_content_format,
+                        image_data,
+                        video_data,
+                        audio_data,
+                        modalities,
+                        use_dpsk_v32_encoding=self.chat_encoding_spec == "dsv32",
+                    )
+                    msg.update(processed_msg)
 
             # Handle continue_final_message: separate final assistant message
             messages, assistant_prefix = self._handle_last_assistant_message(
                 messages, request
             )
 
-            if messages[0]["role"] != "system":
-                # insert an empty system prompt to help render tool system prompt
+            # An empty system message hosts the request tools; dsv41 renders a
+            # system token for it, so it only gets one when tools need the host.
+            if messages[0]["role"] != "system" and (request.tools or not is_dsv41):
                 messages.insert(0, {"role": "system", "content": ""})
             if request.tools:
-                messages[0]["tools"] = [tool.model_dump() for tool in request.tools]
+                messages[0]["tools"] = [
+                    (
+                        chat_encoding.dsv41_tool_payload(tool)
+                        if is_dsv41
+                        else tool.model_dump()
+                    )
+                    for tool in request.tools
+                ]
 
-            # Default encoding (dsv4/dsv32)
+            # Default encoding (dsv4/dsv41/dsv32)
             if self.chat_encoding_spec == "dsv4":
                 effort_source = request.reasoning_effort
                 if effort_source is None:
@@ -1469,6 +1508,33 @@ class OpenAIServingChat(OpenAIServingBase):
                     reasoning_effort_profile=reasoning_effort_profile,
                 )
                 prompt_ids = self.tokenizer_manager.tokenizer.encode(real_input)
+            elif is_dsv41:
+                if request.task is not None:
+                    encoding_dsv41.attach_task_to_last_user_message(
+                        messages, request.task
+                    )
+                real_input, media = encoding_dsv41.encode_messages(
+                    messages,
+                    thinking_mode=thinking_mode,
+                    reasoning_effort=self._resolve_dsv41_reasoning_effort(
+                        request.reasoning_effort
+                    ),
+                    return_multi_modal_data=True,
+                )
+                if media["images"]:
+                    if not is_multimodal:
+                        raise ValueError("image input is not supported for this model")
+                    image_data.extend(
+                        ImageData(url=image["url"]) for image in media["images"]
+                    )
+                    tokenizer = self.tokenizer_manager.tokenizer
+                    real_input = real_input.replace(
+                        encoding_dsv41.IMAGE_PLACEHOLDER,
+                        tokenizer.convert_ids_to_tokens(
+                            self.tokenizer_manager.image_token_id
+                        ),
+                    )
+                prompt_ids = self.tokenizer_manager.tokenizer.encode(real_input)
             else:
                 real_input = encoding_dsv32.encode_messages(
                     messages, thinking_mode=thinking_mode

python/sglang/srt/environ.py

@@ -1457,6 +1457,9 @@ class Envs:
     SGLANG_DSV4_FP4_DEQUANT = EnvBool(False)
     # Flash-0731 also accepts "low"; the active profile is checkpoint-resolved.
     SGLANG_DSV4_REASONING_EFFORT = EnvStr("")
+    # DeepSeek-V4.1 default when a request carries no reasoning_effort: one of
+    # low/high/xhigh/max or an integer budget in [1, 100]; unset -> the encoder default.
+    SGLANG_DSV41_REASONING_EFFORT = EnvStr(None)
     # Quantize the SWA fp8 KV cache from bf16-rounded values (matches
     # trainer-side QAT and the DSA-CP path) instead of fp32 registers.
     SGLANG_DSV4_USE_BF16_KV_QUANT_SOURCE = EnvBool(False)

python/sglang/srt/function_call/deepseekv32_detector.py

@@ -70,33 +70,54 @@ class DeepSeekV32Detector(BaseFormatDetector):
     Reference: DeepSeek V3.2 format specification
     """
 
+    # Tag names after the DSML marker; subclasses override for newer formats.
+    dsml_token = "｜DSML｜"
+    tool_calls_block_name = "function_calls"
+    invoke_tag_name = "invoke"
+    parameter_tag_name = "parameter"
+
     def __init__(self):
         super().__init__()
-        self.bot_token = "<｜DSML｜function_calls>"
-        self.eot_token = "</｜DSML｜function_calls>"
-        self.invoke_end_token = "</｜DSML｜invoke>"
-        self.parameter_regex = r'<｜DSML｜parameter\s+name="([^"]+)"\s+string="([^"]+)"\s*>(.*?)</｜DSML｜parameter>'
+        block = f"{self.dsml_token}{self.tool_calls_block_name}"
+        invoke = f"{self.dsml_token}{self.invoke_tag_name}"
+        parameter = f"{self.dsml_token}{self.parameter_tag_name}"
+        self.bot_token = f"<{block}>"
+        self.eot_token = f"</{block}>"
+        self.invoke_start_token = f"<{invoke}"
+        self.invoke_end_token = f"</{invoke}>"
+        self.parameter_regex = (
+            rf'<{parameter}\s+name="([^"]+)"\s+string="([^"]+)"\s*>(.*?)</{parameter}>'
+        )
         self.partial_parameter_regex = (
-            r'<｜DSML｜parameter\s+name="([^"]+)"\s+string="([^"]+)"\s*>(.*)$'
-        )
-        self.function_calls_regex = (
-            r"<｜DSML｜function_calls>(.*?)</｜DSML｜function_calls>"
+            rf'<{parameter}\s+name="([^"]+)"\s+string="([^"]+)"\s*>(.*)$'
         )
+        self.function_calls_regex = rf"<{block}>(.*?)</{block}>"
         # Long-form `<｜DSML｜invoke name="x">...</｜DSML｜invoke>` and the
         # self-closing `<｜DSML｜invoke name="x"/>` shape V4 emits for zero-arg
         # tools. The `end` group is empty when the closer hasn't streamed in.
         self.invoke_regex = (
-            r'<｜DSML｜invoke\s+name="(?P<name>[^"]+)"\s*'
+            rf'<{invoke}\s+name="(?P<name>[^"]+)"\s*'
             r"(?:(?P<self_close>/>)"
-            r"|>(?P<body>.*?)(?P<end>(?:</｜DSML｜invoke>|$)))"
+            rf"|>(?P<body>.*?)(?P<end>(?:</{invoke}>|$)))"
         )
-        self.prefix_parameter_end_call = ["</", "｜DSML｜", "parameter"]
-        self.prefix_invoke_end_call = ["</", "｜DSML｜", "inv", "oke"]
+        # Consumed right-to-left by rstrip (a character set, not a suffix), so the
+        # invoke name is split to limit how much of a partial value gets eaten.
+        self.prefix_parameter_end_call = [
+            "</",
+            self.dsml_token,
+            self.parameter_tag_name,
+        ]
+        self.prefix_invoke_end_call = [
+            "</",
+            self.dsml_token,
+            self.invoke_tag_name[:-3],
+            self.invoke_tag_name[-3:],
+        ]
         self.current_tool_id = -1
 
     def has_tool_call(self, text: str) -> bool:
         """Check if the text contains a deepseek v32 format tool call."""
-        return self.bot_token in text or "<｜DSML｜invoke" in text
+        return self.bot_token in text or self.invoke_start_token in text
 
     @staticmethod
     def _unpack_invoke_match(m: "re.Match[str]") -> tuple[str, str, bool]:
@@ -379,9 +400,9 @@ class DeepSeekV32Detector(BaseFormatDetector):
 
     def structure_info(self) -> _GetInfoFunc:
         return lambda name: StructureInfo(
-            begin=f'<｜DSML｜invoke name="{name}">',
-            end="</｜DSML｜invoke>",
-            trigger="<｜DSML｜invoke",
+            begin=f'{self.invoke_start_token} name="{name}">',
+            end=self.invoke_end_token,
+            trigger=self.invoke_start_token,
         )
 
     def get_structural_tag_name(self) -> str:

python/sglang/srt/function_call/deepseekv41_detector.py

@@ -0,0 +1,103 @@
+from typing import List, Literal, Optional, Union
+
+from xgrammar.structural_tag import (
+    AnyTextFormat,
+    ConstStringFormat,
+    JSONSchemaFormat,
+    OrFormat,
+    SequenceFormat,
+    TagFormat,
+    TagsWithSeparatorFormat,
+    TriggeredTagsFormat,
+)
+
+from sglang.srt.entrypoints.openai.protocol import Tool, ToolChoice
+from sglang.srt.function_call.base_format_detector import StructuralTag
+from sglang.srt.function_call.deepseekv32_detector import DeepSeekV32Detector
+
+
+class DeepSeekV41Detector(DeepSeekV32Detector):
+    """DeepSeek V4.1 DSML detector.
+
+    The leading space in each tag name below is intentional, not a typo.
+    """
+
+    tool_calls_block_name = " calls"
+    invoke_tag_name = " invoke"
+    parameter_tag_name = " parameter"
+
+    # The encoder joins an assistant turn's content and its calls block with a
+    # blank line, and renders it even when there is no content.
+    tool_calls_prefix = "\n\n"
+    think_end_token = "</think>"
+
+    def get_structural_tag_name(self) -> Optional[str]:
+        # xgrammar's builtin "deepseek_v4" tag hardcodes the unspaced names,
+        # so the V4.1 tag is assembled in get_structural_tag instead.
+        return None
+
+    def get_structural_tag(
+        self,
+        tools: Union[List[Tool], None] = None,
+        tool_choice: Union[ToolChoice, Literal["auto", "required"]] = "auto",
+        thinking_mode: bool = False,
+        parallel_tool_calls: bool = True,
+    ) -> Optional[StructuralTag]:
+        """The builtin "deepseek_v4" shape with the spaced tag names.
+
+        Bodies are JSON: xgrammar's "deepseek_xml" body style also hardcodes
+        the unspaced "parameter" name, and the V3.2-lineage parser accepts a
+        JSON body inside an invoke.
+        """
+        tools = list(tools or [])
+        if isinstance(tool_choice, ToolChoice):
+            tools = [
+                tool
+                for tool in tools
+                if tool.function.name == tool_choice.function.name
+            ]
+            if len(tools) != 1:
+                return None
+        if not tools:
+            return None
+
+        def invoke_tag(tool: Tool) -> TagFormat:
+            function = tool.function
+            schema = function.parameters if function.strict else True
+            if schema is None:
+                schema = True
+            return TagFormat(
+                begin=f'{self.invoke_start_token} name="{function.name}">',
+                content=JSONSchemaFormat(json_schema=schema),
+                end=f"{self.invoke_end_token}\n",
+            )
+
+        tags = [invoke_tag(tool) for tool in tools]
+        if isinstance(tool_choice, ToolChoice):
+            calls = tags[0]
+        elif parallel_tool_calls:
+            calls = TagsWithSeparatorFormat(tags=tags, separator="", at_least_one=True)
+        else:
+            calls = OrFormat(elements=tags)
+        block_begin = f"{self.bot_token}\n"
+
+        if tool_choice == "auto":
+            body = TriggeredTagsFormat(
+                triggers=[self.bot_token],
+                tags=[TagFormat(begin=block_begin, content=calls, end=self.eot_token)],
+                excludes=["<think>", self.think_end_token],
+            )
+        else:
+            body = SequenceFormat(
+                elements=[
+                    ConstStringFormat(value=self.tool_calls_prefix + block_begin),
+                    calls,
+                    ConstStringFormat(value=self.eot_token),
+                ]
+            )
+        if not thinking_mode:
+            return StructuralTag(format=body)
+        reasoning = TagFormat(
+            begin="", content=AnyTextFormat(), end=self.think_end_token
+        )
+        return StructuralTag(format=SequenceFormat(elements=[reasoning, body]))

python/sglang/srt/function_call/deepseekv4_detector.py

@@ -57,11 +57,7 @@ class DeepSeekV4Detector(DeepSeekV32Detector):
     Reference: DeepSeek V4 format specification
     """
 
-    def __init__(self):
-        super().__init__()
-        self.bot_token = "<｜DSML｜tool_calls>"
-        self.eot_token = "</｜DSML｜tool_calls>"
-        self.function_calls_regex = r"<｜DSML｜tool_calls>(.*?)</｜DSML｜tool_calls>"
+    tool_calls_block_name = "tool_calls"
 
     def get_structural_tag_name(self) -> str:
         return "deepseek_v4"

python/sglang/srt/function_call/function_call_parser.py

@@ -19,6 +19,7 @@ from sglang.srt.function_call.deepseekv3_detector import DeepSeekV3Detector
 from sglang.srt.function_call.deepseekv4_detector import DeepSeekV4Detector
 from sglang.srt.function_call.deepseekv31_detector import DeepSeekV31Detector
 from sglang.srt.function_call.deepseekv32_detector import DeepSeekV32Detector
+from sglang.srt.function_call.deepseekv41_detector import DeepSeekV41Detector
 from sglang.srt.function_call.dots_detector import DotsToolDetector
 from sglang.srt.function_call.gemma4_detector import Gemma4Detector
 from sglang.srt.function_call.gigachat3_detector import GigaChat3Detector
@@ -76,6 +77,7 @@ class FunctionCallParser:
         "deepseekv31": DeepSeekV31Detector,
         "deepseekv32": DeepSeekV32Detector,
         "deepseekv4": DeepSeekV4Detector,
+        "deepseekv41": DeepSeekV41Detector,
         "dots": DotsToolDetector,
         "glm": Glm4MoeDetector,
         "glm45": Glm4MoeDetector,

python/sglang/srt/parser/reasoning_parser.py

@@ -2166,6 +2166,7 @@ class ReasoningParser:
         "deepseek-r1": DeepSeekR1Detector,
         "deepseek-v3": _DeepSeekV3Detector,
         "deepseek-v4": DeepSeekV4Detector,
+        "deepseek-v41": DeepSeekV4Detector,
         "dots": Qwen3Detector,
         "glm45": Glm45Detector,
         "ling3": Ling3Detector,

python/sglang/srt/parser/template_detection.py

@@ -400,6 +400,10 @@ def _is_deepseek_v4(ctx):
     return ctx.has_text("<｜DSML｜tool_calls>")
 
 
+def _is_deepseek_v41(ctx):
+    return ctx.has_text("<｜DSML｜ calls>")
+
+
 def _is_hunyuan(ctx):
     # The shipping Hy3 tokenizer appends a shared suffix to each special token
     # (e.g. ``<tool_calls:opensource>``), so match the bare or suffixed form.
@@ -547,6 +551,9 @@ REASONING_PARSER_RULES = (
     DetectionRule(name="step3", value="step3", predicate=_is_step3),
     DetectionRule(name="ling3", value="ling3", predicate=_is_ling3),
     DetectionRule(name="qwen3", value="qwen3", predicate=_is_qwen3),
+    DetectionRule(
+        name="deepseek_v41", value="deepseek-v41", predicate=_is_deepseek_v41
+    ),
     DetectionRule(name="deepseek_v4", value="deepseek-v4", predicate=_is_deepseek_v4),
     DetectionRule(name="deepseek_v3", value="deepseek-v3", predicate=_is_deepseek_v3),
     DetectionRule(
@@ -573,6 +580,7 @@ TOOL_CALL_PARSER_RULES = (
     DetectionRule(name="minimax", value="minimax-m2", predicate=_is_minimax),
     DetectionRule(name="interns1", value="interns1", predicate=_is_interns1),
     DetectionRule(name="mistral", value="mistral", predicate=_is_mistral),
+    DetectionRule(name="deepseek_v41", value="deepseekv41", predicate=_is_deepseek_v41),
     DetectionRule(name="deepseek_v4", value="deepseekv4", predicate=_is_deepseek_v4),
     DetectionRule(name="deepseek_v32", value="deepseekv32", predicate=_is_deepseek_v32),
     DetectionRule(name="deepseek_v31", value="deepseekv31", predicate=_is_deepseek_v31),
@@ -765,6 +773,7 @@ def _log_undetected_parser(attr: str, label: str) -> None:
 
 def _architecture_auto_parsers(server_args, needs: Tuple[str, ...]) -> Dict[str, str]:
     """The parsers the model architecture implies, for the fields still on auto."""
+    from sglang.srt.entrypoints.openai.chat_encoding import is_deepseek_v41_arch
     from sglang.srt.utils.hf_transformers_utils import get_config
 
     cfg = resolving_view(server_args)
@@ -785,6 +794,8 @@ def _architecture_auto_parsers(server_args, needs: Tuple[str, ...]) -> Dict[str,
         "BailingMoeV3VLForConditionalGeneration",
     ) or model_type in ("bailing_hybrid", "bailing_moe_v3_vl"):
         reasoning_parser, tool_call_parser = "ling3", "ling3"
+    elif is_deepseek_v41_arch(arch=arch, model_type=model_type):
+        reasoning_parser, tool_call_parser = "deepseek-v41", "deepseekv41"
     elif "DeepseekV4" in arch:
         reasoning_parser, tool_call_parser = "deepseek-v4", "deepseekv4"
     elif "DeepseekV3" in arch:

test/registered/unit/entrypoints/openai/test_serving_chat.py

@@ -54,7 +54,7 @@ from sglang.test.ci.ci_register import register_cpu_ci
 register_cpu_ci(est_time=13, suite="base-a-test-cpu")
 
 # Every spec resolve_chat_encoding_spec can return; pinned by the guard below.
-_ALL_CHAT_ENCODING_SPECS = ("dsv4", "dsv32", "inkling", "kimi_k3")
+_ALL_CHAT_ENCODING_SPECS = ("dsv41", "dsv4", "dsv32", "inkling", "kimi_k3")
 
 
 def _spec_result(index):