diff --git a/litellm/llms/anthropic/chat/handler.py b/litellm/llms/anthropic/chat/handler.py
index 8c4facc1ba2..a842985dc4d 100644
--- a/litellm/llms/anthropic/chat/handler.py
+++ b/litellm/llms/anthropic/chat/handler.py
@@ -1048,24 +1048,36 @@ class ModelResponseIterator:
         This fixes network fragmentation issues where SSE data chunks may be split
         across TCP packets. See: https://github.com/BerriAI/litellm/issues/17473
 
+        Anthropic SSE payloads are always JSON objects, so a buffer that does not
+        start with "{" can never become valid. Those payloads (heartbeats, an
+        OpenAI-style "[DONE]" sentinel emitted by Anthropic-compatible gateways,
+        any other junk) are discarded so the stream resynchronizes on the next
+        event instead of silently swallowing the rest of the response.
+
         Args:
             data_str: The JSON string to parse (without "data:" prefix)
 
         Returns:
             ModelResponseStream if JSON is complete, None if still accumulating
         """
-        # Accumulate JSON data
         self.accumulated_json += data_str
 
-        # Try to parse the accumulated JSON
+        if not self.accumulated_json.lstrip().startswith("{"):
+            self._reset_json_accumulation()
+            return None
+
         try:
             data_json: Final = json.loads(self.accumulated_json)
-            self.accumulated_json = ""  # Reset after successful parsing
-            return self.chunk_parser(chunk=data_json)
         except json.JSONDecodeError:
-            # If it's not valid JSON yet, continue to the next chunk
             return None
 
+        self._reset_json_accumulation()
+        return self.chunk_parser(chunk=data_json)
+
+    def _reset_json_accumulation(self) -> None:
+        self.accumulated_json = ""
+        self.chunk_type = "valid_json"
+
     def _parse_sse_data(self, str_line: str) -> ModelResponseStream | None:
         """
         Parse SSE data line, handling both complete and partial JSON chunks.
diff --git a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_handler.py b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_handler.py
index f934c7184f8..299d5f2cbc1 100644
--- a/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_handler.py
+++ b/tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_handler.py
@@ -2045,3 +2045,79 @@ def test_non_bash_tool_result_skipped():
     assert (
         len(code_results) == 0
     ), f"Expected 0 code_interpreter_results for text_editor result, got {len(code_results)}"
+
+
+def test_non_json_sse_payload_does_not_swallow_rest_of_stream():
+    """
+    A single non-JSON SSE payload (e.g. the OpenAI-style "[DONE]" sentinel some
+    Anthropic-compatible gateways emit) used to put the iterator into permanent
+    accumulation mode, silently dropping every later event: the tool_use block and
+    the stop_reason disappeared while the already-streamed text survived.
+
+    See: https://github.com/BerriAI/litellm/issues/36262
+    """
+    events = [
+        {
+            "type": "content_block_start",
+            "index": 0,
+            "content_block": {"type": "text", "text": ""},
+        },
+        {
+            "type": "content_block_delta",
+            "index": 0,
+            "delta": {"type": "text_delta", "text": "I will call the tool."},
+        },
+        {"type": "content_block_stop", "index": 0},
+        "[DONE]",
+        {
+            "type": "content_block_start",
+            "index": 1,
+            "content_block": {
+                "type": "tool_use",
+                "id": "toolu_1",
+                "name": "submit_result",
+                "input": {},
+            },
+        },
+        {
+            "type": "content_block_delta",
+            "index": 1,
+            "delta": {"type": "input_json_delta", "partial_json": '{"value": 1}'},
+        },
+        {"type": "content_block_stop", "index": 1},
+        {
+            "type": "message_delta",
+            "delta": {"stop_reason": "tool_use"},
+            "usage": {"output_tokens": 20},
+        },
+    ]
+    lines = [
+        f"data: {event if isinstance(event, str) else json.dumps(event)}\n".encode()
+        for event in events
+    ]
+
+    iterator = ModelResponseIterator(
+        streaming_response=iter(lines), sync_stream=True, json_mode=False
+    )
+    parsed = list(iterator)
+
+    tool_call_names = [
+        tool_call["function"]["name"]
+        for chunk in parsed
+        for tool_call in (chunk.choices[0].delta.tool_calls or [])
+        if tool_call["function"]["name"]
+    ]
+    tool_call_args = "".join(
+        tool_call["function"]["arguments"] or ""
+        for chunk in parsed
+        for tool_call in (chunk.choices[0].delta.tool_calls or [])
+    )
+    finish_reasons = [
+        chunk.choices[0].finish_reason
+        for chunk in parsed
+        if chunk.choices[0].finish_reason
+    ]
+
+    assert tool_call_names == ["submit_result"]
+    assert tool_call_args == '{"value": 1}'
+    assert finish_reasons == ["tool_calls"]