diff --git a/evalharness/model/adapter.py b/evalharness/model/adapter.py
index a4153a4..603921a 100644
--- a/evalharness/model/adapter.py
+++ b/evalharness/model/adapter.py
@@ -115,6 +115,17 @@ def _key_for(api_base: str) -> str:
 
 
 
+def _payload_chars(payload: Dict[str, Any]) -> int:
+    """Rough prompt size in characters (~3-4 chars/token). Used to decide
+    the auto-stream path: gateways that buffer whole requests make a huge
+    INPUT as hang-prone as a huge output budget."""
+    n = 0
+    for m in payload.get('messages') or []:
+        c = m.get('content')
+        n += len(c) if isinstance(c, str) else 256
+    return n
+
+
 def _parse_text_tool_calls(text: str) -> list:
     """Extract tool calls from a text reply. Handles both shapes:
     - JSON array:  [{"name":..,"arguments":{..}}]
@@ -209,9 +220,15 @@ class OpenAICompatible(ModelAdapter):
                 if stream:
                     out = await self._post_stream_perf(
                         f'{self.api_base}/chat/completions', payload, headers, t0)
-                elif int(payload.get('max_tokens') or 0) > 100000 \
+                elif (int(payload.get('max_tokens') or 0) > 100000
+                      or _payload_chars(payload) > 300_000) \
                         and not os.environ.get('EVALHARNESS_NO_AUTOSTREAM'):
-                    # long generation: stream and aggregate (gateway-safe).
+                    # long generation OR LONG INPUT: stream and aggregate
+                    # (gateway-safe). Some gateways buffer the whole request
+                    # before answering on the non-stream path -- a 128k-token
+                    # longbench_v2 prompt sat there past every read timeout;
+                    # streaming starts emitting immediately, so a stuck
+                    # endpoint surfaces in ~60s instead of after 20 minutes.
                     # Some gateways drop chat_template_kwargs on the STREAM
                     # path only (non-stream honors it) -- append the /no_think
                     # soft switch into the prompt itself as a belt-and-braces