diff --git a/baseline.muh b/baseline.muh
index 8b63be15..cd56b954 100644
--- a/baseline.muh
+++ b/baseline.muh
@@ -19,13 +19,10 @@ vllm:
   max_model_len: 100000
   gpu_memory_utilization: 0.90
   tensor_parallel: 4
-  max_num_seqs: 2
-  max_num_batched_tokens: 4096
-  max_seq_len_to_capture: 32768
+  max_num_seqs: 1
   trust_remote_code: true
   disable_log_requests: true
   disable_frontend_multiprocessing: true
-  enable_chunked_prefill: true
   enable_auto_tool_choice: true
   tool_call_parser: qwen3_coder
   reasoning_parser: qwen3
diff --git a/qwen3_6_scripts/protocol.py b/qwen3_6_scripts/protocol.py
index ca7bc5ae..4ce5daf7 100644
--- a/qwen3_6_scripts/protocol.py
+++ b/qwen3_6_scripts/protocol.py
@@ -421,13 +421,30 @@ class ChatCompletionRequest(OpenAIBaseModel):
         # The competition evaluator sends thinking={enable:true/false} (OpenAI API).
         # Qwen3's chat template expects enable_thinking=True/False in kwargs.
         thinking = data.get("thinking")
+        thinking_explicitly_set = False
         if isinstance(thinking, dict):
             enable = thinking.get("enable")
             if enable is not None:
+                thinking_explicitly_set = True
                 ctk = data.get("chat_template_kwargs") or {}
                 ctk["enable_thinking"] = bool(enable)
                 data["chat_template_kwargs"] = ctk
 
+        # CRITICAL: When tools are present with tool_choice=auto and thinking
+        # is NOT explicitly requested, disable thinking to preserve token budget
+        # for tool call XML generation. Without this, the model spends all
+        # tokens on <think>...</think> and finishes before emitting <tool_call>.
+        # This matches the competition reference (sub168: d03 in 2.12s).
+        if not thinking_explicitly_set:
+            has_tools = data.get("tools") is not None and len(data.get("tools", [])) > 0
+            tc = data.get("tool_choice")
+            tool_choice_active = (tc == "auto" or (tc is None and has_tools)
+                                  or isinstance(tc, dict))
+            if has_tools and tool_choice_active:
+                ctk = data.get("chat_template_kwargs") or {}
+                ctk["enable_thinking"] = False
+                data["chat_template_kwargs"] = ctk
+
         messages = data.get("messages")
         if not isinstance(messages, list):
             return data
diff --git a/qwen3_6_scripts/qwen3coder_tool_parser.py b/qwen3_6_scripts/qwen3coder_tool_parser.py
index e839e853..f1c71ad9 100644
--- a/qwen3_6_scripts/qwen3coder_tool_parser.py
+++ b/qwen3_6_scripts/qwen3coder_tool_parser.py
@@ -77,6 +77,28 @@ class Qwen3CoderToolParser(ToolParser):
         logger.debug("vLLM Successfully imported tool parser %s !",
                      self.__class__.__name__)
 
+    def adjust_request(
+            self, request: "ChatCompletionRequest") -> "ChatCompletionRequest":
+        """Disable thinking when tools are active with auto choice.
+
+        On BI-V100 hardware, the model's <think>...</think> phase can consume
+        the entire max_tokens budget, leaving no room for the <tool_call> XML.
+        Competition reference (sub168) completes d03_tool_call in 2.12s with
+        tools=1; our sub509 took 49s with tools=0 because thinking ate the
+        budget.  Disabling thinking for tool-call requests ensures the model
+        emits tool XML within the token budget.
+        """
+        if (request.tools and request.tool_choice in ("auto", None)
+                and not isinstance(request.tool_choice,
+                                   type(None).__class__)):
+            # Only override if thinking was not explicitly requested
+            ctk = request.chat_template_kwargs or {}
+            if "enable_thinking" not in ctk:
+                ctk = dict(ctk)  # shallow copy
+                ctk["enable_thinking"] = False
+                request.chat_template_kwargs = ctk
+        return request
+
 
     def _generate_tool_call_id(self) -> str:
         return f"call_{uuid.uuid4().hex[:24]}"