diff --git a/astrbot/core/provider/sources/zhipu_source.py b/astrbot/core/provider/sources/zhipu_source.py
index ed4bc0bf89..91712b373b 100644
--- a/astrbot/core/provider/sources/zhipu_source.py
+++ b/astrbot/core/provider/sources/zhipu_source.py
@@ -1,10 +1,30 @@
 # This file was originally created to adapt to glm-4v-flash, which only supports one image in the context.
 # It is no longer specifically adapted to Zhipu's models. To ensure compatibility, this
 
+import re
+from typing import Any
 
+from openai.types.chat import ChatCompletion
+
+from astrbot.core.agent.tool import ToolSet
+
+from ..entities import LLMResponse
 from ..register import register_provider_adapter
 from .openai_source import ProviderOpenAIOfficial
 
+# GLM role/control tokens that may leak into response text.
+# e.g. <|endoftext|>, <|user|>, <|assistant|>, <|system|>, <|observation|>
+_GLM_ROLE_TOKEN_RE = re.compile(
+    r"<\|(?:endoftext|user|assistant|system|observation)\|>",
+    re.IGNORECASE,
+)
+
+# GLM's "null response" signal — the model outputs exactly <None> (capital N, like Python's
+# None literal) to indicate it has nothing to say.  We intentionally do NOT use re.IGNORECASE
+# here: GLM always emits <None> with a capital N, and a case-insensitive match could
+# accidentally remove unrelated HTML/XML-like content that merely starts with "none".
+_GLM_NULL_TOKEN_RE = re.compile(r"<None>")
+
 
 @register_provider_adapter("zhipu_chat_completion", "智谱 Chat Completion 提供商适配器")
 class ProviderZhipu(ProviderOpenAIOfficial):
@@ -14,3 +34,48 @@ def __init__(
         provider_settings: dict,
     ) -> None:
         super().__init__(provider_config, provider_settings)
+
+    @staticmethod
+    def _clean_glm_special_tokens(text: str) -> str:
+        """Remove GLM-specific non-standard special tokens from response text.
+
+        GLM models sometimes emit internal control tokens that are not meant to be
+        shown to users:
+
+        - ``<None>``  — model's signal for "no response needed"
+        - ``<|endoftext|>``, ``<|user|>``, ``<|assistant|>``, etc. — role / EOS tokens
+          that occasionally leak out of the model into the visible content.
+        """
+        text = _GLM_ROLE_TOKEN_RE.sub("", text)
+        text = _GLM_NULL_TOKEN_RE.sub("", text)
+        # Collapse multiple spaces left behind after token removal
+        text = re.sub(r"[ \t]{2,}", " ", text)
+        return text.strip()
+
+    @staticmethod
+    def _normalize_content(raw_content: Any, strip: bool = True) -> str:
+        """Normalize content and strip GLM-specific non-standard tokens."""
+        base = ProviderOpenAIOfficial._normalize_content(raw_content, strip)
+        return ProviderZhipu._clean_glm_special_tokens(base)
+
+    async def _parse_openai_completion(
+        self, completion: ChatCompletion, tools: ToolSet | None
+    ) -> LLMResponse:
+        """Parse completion and apply an extra GLM token-cleaning pass.
+
+        Even though ``_normalize_content`` is already overridden above, we do a
+        second cleaning pass here to handle cases where special tokens span
+        multiple streaming chunks and therefore survive the per-chunk normalization
+        but appear in the fully-assembled final text.
+        """
+        llm_response = await super()._parse_openai_completion(completion, tools)
+
+        # Apply GLM special token cleaning to the assembled completion text.
+        # Use the completion_text setter so that non-Plain components (e.g. tool calls)
+        # in the chain are preserved; only the Plain text segments are updated in-place.
+        if llm_response.completion_text:
+            cleaned = self._clean_glm_special_tokens(llm_response.completion_text)
+            if cleaned != llm_response.completion_text:
+                llm_response.completion_text = cleaned
+
+        return llm_response
diff --git a/tests/test_zhipu_source.py b/tests/test_zhipu_source.py
new file mode 100644
index 0000000000..ea0930eee5
--- /dev/null
+++ b/tests/test_zhipu_source.py
@@ -0,0 +1,299 @@
+"""Tests for ProviderZhipu GLM non-standard special token handling.
+
+Covers the three layers of cleaning introduced to fix issue #5556:
+1. ``_clean_glm_special_tokens`` — pure regex-based cleaner
+2. ``_normalize_content``         — overrides the base static method
+3. ``_parse_openai_completion``   — second-pass cleaning on assembled text
+"""
+
+from collections.abc import AsyncGenerator
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+import pytest_asyncio
+
+from astrbot.core.agent.tool import (
+    ToolSet,  # noqa: F401 – ensures the module is importable
+)
+from astrbot.core.message.message_event_result import MessageChain
+from astrbot.core.provider.entities import LLMResponse
+from astrbot.core.provider.sources.openai_source import ProviderOpenAIOfficial
+from astrbot.core.provider.sources.zhipu_source import ProviderZhipu
+
+# ──────────────────────────────────────────────────────────────────────────────
+# Helpers
+# ──────────────────────────────────────────────────────────────────────────────
+
+
+def _make_provider() -> ProviderZhipu:
+    return ProviderZhipu(
+        provider_config={
+            "id": "test-zhipu",
+            "type": "zhipu_chat_completion",
+            "model": "glm-4.6v-flash",
+            "key": ["test-key"],
+        },
+        provider_settings={},
+    )
+
+
+def _make_llm_response(text: str) -> LLMResponse:
+    """Return an LLMResponse whose completion_text equals *text*."""
+    r = LLMResponse("assistant")
+    r.result_chain = MessageChain().message(text)
+    return r
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# _clean_glm_special_tokens
+# ──────────────────────────────────────────────────────────────────────────────
+
+
+class TestCleanGLMSpecialTokens:
+    """Unit tests for the pure-function token cleaner."""
+
+    # <None> — null-response signal ----------------------------------------
+
+    def test_null_token_alone(self):
+        assert ProviderZhipu._clean_glm_special_tokens("<None>") == ""
+
+    def test_null_token_with_leading_newline(self):
+        # Exact pattern observed from glm-4.6v-flash: content='\n<None>'
+        assert ProviderZhipu._clean_glm_special_tokens("\n<None>") == ""
+
+    def test_null_token_surrounded_by_whitespace(self):
+        assert ProviderZhipu._clean_glm_special_tokens("  <None>  ") == ""
+
+    def test_null_token_case_insensitive_lower(self):
+        # Without re.IGNORECASE, lowercase <none> is not a GLM token and must be preserved.
+        assert ProviderZhipu._clean_glm_special_tokens("<none>") == "<none>"
+
+    def test_null_token_case_insensitive_upper(self):
+        # Without re.IGNORECASE, uppercase <NONE> is not a GLM token and must be preserved.
+        assert ProviderZhipu._clean_glm_special_tokens("<NONE>") == "<NONE>"
+
+    def test_null_token_in_middle_of_text(self):
+        result = ProviderZhipu._clean_glm_special_tokens("hello <None> world")
+        # Token itself must be gone; surrounding spaces are collapsed to one
+        assert "<None>" not in result
+        assert "hello" in result and "world" in result
+
+    # Role / control tokens ------------------------------------------------
+
+    def test_endoftext_token(self):
+        assert ProviderZhipu._clean_glm_special_tokens("<|endoftext|>") == ""
+
+    def test_user_role_token(self):
+        assert ProviderZhipu._clean_glm_special_tokens("<|user|>") == ""
+
+    def test_assistant_role_token(self):
+        assert ProviderZhipu._clean_glm_special_tokens("<|assistant|>") == ""
+
+    def test_system_role_token(self):
+        assert ProviderZhipu._clean_glm_special_tokens("<|system|>") == ""
+
+    def test_observation_role_token(self):
+        assert ProviderZhipu._clean_glm_special_tokens("<|observation|>") == ""
+
+    def test_role_token_prefix_removed(self):
+        result = ProviderZhipu._clean_glm_special_tokens(
+            "<|assistant|>Hello, how can I help?"
+        )
+        assert result == "Hello, how can I help?"
+
+    def test_multiple_role_tokens(self):
+        result = ProviderZhipu._clean_glm_special_tokens("<|user|>Hi<|assistant|>Hello")
+        assert result == "HiHello"
+
+    def test_endoftext_at_end_of_reply(self):
+        result = ProviderZhipu._clean_glm_special_tokens(
+            "Python 最新版本是 3.13。<|endoftext|>"
+        )
+        assert result == "Python 最新版本是 3.13。"
+
+    # Normal text must not be affected ------------------------------------
+
+    def test_normal_text_unchanged(self):
+        text = "我是 GLM，很高兴认识你！"
+        assert ProviderZhipu._clean_glm_special_tokens(text) == text
+
+    def test_empty_string(self):
+        assert ProviderZhipu._clean_glm_special_tokens("") == ""
+
+    def test_angle_bracket_in_normal_text_unchanged(self):
+        """Angle brackets that are not special tokens must survive."""
+        text = "if a < b and b > 0: pass"
+        assert ProviderZhipu._clean_glm_special_tokens(text) == text
+
+    def test_html_like_tag_unchanged(self):
+        """HTML-style tags (not GLM tokens) must not be stripped."""
+        text = "Use <strong>bold</strong> for emphasis."
+        assert ProviderZhipu._clean_glm_special_tokens(text) == text
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# _normalize_content (static override)
+# ──────────────────────────────────────────────────────────────────────────────
+
+
+class TestNormalizeContent:
+    """Verify that ProviderZhipu._normalize_content applies GLM cleaning on top
+    of the base ProviderOpenAIOfficial normalisation."""
+
+    def test_null_token_string_returns_empty(self):
+        assert ProviderZhipu._normalize_content("\n<None>") == ""
+
+    def test_normal_string_unchanged(self):
+        text = "Hello, world!"
+        assert ProviderZhipu._normalize_content(text) == text
+
+    def test_list_content_null_token(self):
+        raw = [{"type": "text", "text": "<None>"}]
+        assert ProviderZhipu._normalize_content(raw) == ""
+
+    def test_list_content_normal_text(self):
+        raw = [{"type": "text", "text": "Hello"}]
+        assert ProviderZhipu._normalize_content(raw) == "Hello"
+
+    def test_list_content_endoftext(self):
+        raw = [{"type": "text", "text": "Done<|endoftext|>"}]
+        assert ProviderZhipu._normalize_content(raw) == "Done"
+
+    def test_dict_content_null_token(self):
+        raw = {"type": "text", "text": "<None>"}
+        assert ProviderZhipu._normalize_content(raw) == ""
+
+    def test_override_is_distinct_from_base(self):
+        """The Zhipu override should differ from the base when GLM tokens are present."""
+        text = "\n<None>"
+        base_result = ProviderOpenAIOfficial._normalize_content(text)
+        zhipu_result = ProviderZhipu._normalize_content(text)
+        # Base keeps "<None>" after strip; Zhipu must remove it
+        assert "<None>" not in zhipu_result
+        assert zhipu_result == ""
+        # Confirm the base does NOT clean it (so the override is meaningful)
+        assert base_result == "<None>"
+
+
+# ──────────────────────────────────────────────────────────────────────────────
+# _parse_openai_completion  — second-pass cleaning
+# ──────────────────────────────────────────────────────────────────────────────
+
+
+class TestParseOpenAICompletionCleaning:
+    """Integration tests for the post-processing pass in _parse_openai_completion.
+
+    We patch ProviderOpenAIOfficial._parse_openai_completion so that we can
+    control what the base class "returns" and verify that ProviderZhipu
+    correctly applies the extra GLM cleaning pass on top.
+    """
+
+    @pytest_asyncio.fixture
+    async def provider(self) -> AsyncGenerator[ProviderZhipu, None]:
+        p = _make_provider()
+        yield p
+        await p.terminate()
+
+    @pytest.mark.asyncio
+    async def test_null_token_content_becomes_empty(self, provider: ProviderZhipu):
+        """content='\\n<None>' (real API response) should produce an empty reply."""
+        fake_completion = MagicMock()
+        parent_response = _make_llm_response("\n<None>")
+
+        with patch.object(
+            ProviderOpenAIOfficial,
+            "_parse_openai_completion",
+            new=AsyncMock(return_value=parent_response),
+        ):
+            result = await provider._parse_openai_completion(fake_completion, None)
+
+        assert result.completion_text == ""
+
+    @pytest.mark.asyncio
+    async def test_endoftext_token_stripped_from_end(self, provider: ProviderZhipu):
+        parent_response = _make_llm_response("当然可以！<|endoftext|>")
+
+        with patch.object(
+            ProviderOpenAIOfficial,
+            "_parse_openai_completion",
+            new=AsyncMock(return_value=parent_response),
+        ):
+            result = await provider._parse_openai_completion(MagicMock(), None)
+
+        assert result.completion_text == "当然可以！"
+
+    @pytest.mark.asyncio
+    async def test_assistant_role_token_prefix_stripped(self, provider: ProviderZhipu):
+        parent_response = _make_llm_response("<|assistant|>我是一个AI助手。")
+
+        with patch.object(
+            ProviderOpenAIOfficial,
+            "_parse_openai_completion",
+            new=AsyncMock(return_value=parent_response),
+        ):
+            result = await provider._parse_openai_completion(MagicMock(), None)
+
+        assert result.completion_text == "我是一个AI助手。"
+
+    @pytest.mark.asyncio
+    async def test_normal_content_unchanged(self, provider: ProviderZhipu):
+        """Normal GLM replies must not be modified."""
+        normal = "好的，我来帮你解答这个问题。"
+        parent_response = _make_llm_response(normal)
+
+        with patch.object(
+            ProviderOpenAIOfficial,
+            "_parse_openai_completion",
+            new=AsyncMock(return_value=parent_response),
+        ):
+            result = await provider._parse_openai_completion(MagicMock(), None)
+
+        assert result.completion_text == normal
+
+    @pytest.mark.asyncio
+    async def test_empty_completion_text_not_modified(self, provider: ProviderZhipu):
+        """When the base class returns empty completion_text, don't error out."""
+        parent_response = LLMResponse("assistant")
+        parent_response.result_chain = None
+        parent_response._completion_text = ""
+
+        with patch.object(
+            ProviderOpenAIOfficial,
+            "_parse_openai_completion",
+            new=AsyncMock(return_value=parent_response),
+        ):
+            result = await provider._parse_openai_completion(MagicMock(), None)
+
+        assert result.completion_text == ""
+
+    @pytest.mark.asyncio
+    async def test_reasoning_content_preserved(self, provider: ProviderZhipu):
+        """Cleaning must not touch reasoning_content."""
+        parent_response = _make_llm_response("\n<None>")
+        parent_response.reasoning_content = "思考过程：用户打了招呼，不需要回复。"
+
+        with patch.object(
+            ProviderOpenAIOfficial,
+            "_parse_openai_completion",
+            new=AsyncMock(return_value=parent_response),
+        ):
+            result = await provider._parse_openai_completion(MagicMock(), None)
+
+        assert result.completion_text == ""
+        assert "思考过程" in result.reasoning_content
+
+    @pytest.mark.asyncio
+    async def test_other_response_fields_preserved(self, provider: ProviderZhipu):
+        """id, usage and other metadata must survive the cleaning pass."""
+        parent_response = _make_llm_response("普通回复")
+        parent_response.id = "cmp-test-id-123"
+
+        with patch.object(
+            ProviderOpenAIOfficial,
+            "_parse_openai_completion",
+            new=AsyncMock(return_value=parent_response),
+        ):
+            result = await provider._parse_openai_completion(MagicMock(), None)
+
+        assert result.id == "cmp-test-id-123"
+        assert result.completion_text == "普通回复"