diff --git a/test/unit/test_llm_server.py b/test/unit/test_llm_server.py
index b486034643..1afa9ef952 100644
--- a/test/unit/test_llm_server.py
+++ b/test/unit/test_llm_server.py
@@ -3,10 +3,26 @@ from unittest.mock import patch
 from tinygrad import Tensor, UOp
 from tinygrad.schedule import schedule_cache
 from tinygrad.llm.model import Transformer, TransformerConfig
+from tinygrad.llm.serve import StreamRouter
 
 TEST_CONFIG = TransformerConfig(num_blocks=1, dim=64, hidden_dim=128, n_heads=2, n_kv_heads=2,
                            norm_eps=1e-5, vocab_size=100, head_dim=32, rope_theta=10000.0, rope_dim=32, v_head_dim=32, max_context=32)
 
+class TestStreamRouter(unittest.TestCase):
+  @staticmethod
+  def route(router:StreamRouter, *pieces:str) -> dict[str, str]:
+    routed = [x for piece in pieces for x in router.route(piece)]
+    routed += list(router.route("", final=True))
+    return {field:"".join(text for f, text in routed if f == field) for field in {x[0] for x in routed}}
+
+  def test_generated_reasoning_tag(self):
+    self.assertEqual(self.route(StreamRouter(), "<thi", "nk>reason", "</thi", "nk>answer"),
+                     {"reasoning_content":"reason", "content":"answer"})
+
+  def test_prompt_opened_reasoning(self):
+    self.assertEqual(self.route(StreamRouter(reasoning=True), "reason", "</thi", "nk>answer"),
+                     {"reasoning_content":"reason", "content":"answer"})
+
 class TestTransformerGenerate(unittest.TestCase):
   def test_kv_cache_reuse(self):
     """Test that generate reuses the KV cache when tokens extend the cached prefix."""
diff --git a/tinygrad/llm/serve.py b/tinygrad/llm/serve.py
index 15dd813acc..768e6da148 100644
--- a/tinygrad/llm/serve.py
+++ b/tinygrad/llm/serve.py
@@ -34,9 +34,9 @@ def normalize_messages(messages:list[dict]) -> None:
 
 class StreamRouter:
   # routes streamed output text to (field, text) deltas, keeping tool_call regions in .buf for the final parse
-  def __init__(self):
+  def __init__(self, reasoning:bool=False):
     self.buf = ""
-    self.mode = "undecided"  # output inside a think block is sent as reasoning_content
+    self.mode = "reasoning" if reasoning else "undecided"  # output inside a think block is sent as reasoning_content
   def split(self, tag:str, final:bool) -> tuple[str, bool]:
     # split buf on the first full tag, holding back a partial tag at the end unless final
     if tag in self.buf:
@@ -66,7 +66,8 @@ class Handler(HTTPRequestHandler):
   def do_GET(self):
     if self.path == "/v1/models": self.send_data(json.dumps({"object":"list","data":[{"id":self.server.model_name,"object":"model"}]}).encode())
     else: self.send_data((pathlib.Path(__file__).parent / "chat.html").read_bytes(), content_type="text/html")
-  def run_model(self, ids:list[int], model_name:str, include_usage=False, max_tokens:int|None=None, temperature:float=0.0):
+  def run_model(self, ids:list[int], model_name:str, include_usage=False, max_tokens:int|None=None, temperature:float=0.0,
+                reasoning:bool=False):
     model, tok = self.server.model, self.server.tok
     prompt_tokens = len(ids)
     cache_start_pos = model.get_start_pos(ids)
@@ -78,7 +79,7 @@ class Handler(HTTPRequestHandler):
     finish_reason = "stop"
     st = time.perf_counter()
     dec = tok.stream_decoder()
-    router = StreamRouter()
+    router = StreamRouter(reasoning)
     for next_id in model.generate(ids, temperature=temperature):
       if len(out) == 0: stderr_log(f"prefill:{(prompt_tokens-cache_start_pos)/((pt:=time.perf_counter())-st):4.0f} tok/s  {colored('--', 'BLACK')}  ")
       if tok.is_end(next_id): break
@@ -129,7 +130,8 @@ class Handler(HTTPRequestHandler):
       # reply
       max_tokens = body.get("max_completion_tokens") or body.get("max_tokens")
       chunks = self.run_model(ids, body["model"], not body.get("stream") or body.get("stream_options",{}).get("include_usage", False),
-                              max_tokens=max_tokens, temperature=float(body.get("temperature", 0.0)))
+                              max_tokens=max_tokens, temperature=float(body.get("temperature", 0.0)),
+                              reasoning=rendered.rstrip().endswith("<think>"))
       if body.get("stream"): self.stream_json(chunks)
       else:
         out, reasoning, tool_calls, finish_reason = [], [], [], "stop"