fix(llm): gate MiniMax reasoning_split by model capability (#826)

MinimaxChatOpenAI unconditionally set reasoning_split=True, but the
kwarg is only valid on M2.x reasoning models. The openai SDK's strict
kwarg validation raised TypeError for Coding Plan and any other non-
reasoning MiniMax model.

Adds requires_reasoning_split to ModelCapabilities, gates the payload
injection on it, and only sets True for _MINIMAX_THINKING (M2.x exact
IDs and the ^MiniMax-M\d forward-compat pattern). Same shape as the
existing supports_tool_choice gate.

Regression tests cover both halves: M2.x models still receive the flag,
non-reasoning MiniMax models do not.

tradingagents/llm_clients/openai_client.py

@@ -118,6 +118,11 @@ class MinimaxChatOpenAI(NormalizedChatOpenAI):
     ``reasoning_split=True`` in the request body redirects the thinking
     block into ``reasoning_details`` so ``content`` stays clean.
 
+    The flag is gated by ``ModelCapabilities.requires_reasoning_split``
+    because non-reasoning MiniMax endpoints (Coding Plan, MiniMax-Text-01)
+    reject the parameter via the openai SDK's strict kwarg validation
+    (#826).
+
     Tool-choice handling for M2.x — those models accept only the string
     enum ``{"none", "auto"}`` and reject langchain's function-spec dict —
     is handled by the capability dispatch in
@@ -126,7 +131,8 @@ class MinimaxChatOpenAI(NormalizedChatOpenAI):
 
     def _get_request_payload(self, input_, *, stop=None, **kwargs):
         payload = super()._get_request_payload(input_, stop=stop, **kwargs)
-        payload.setdefault("reasoning_split", True)
+        if get_capabilities(self.model_name).requires_reasoning_split:
+            payload.setdefault("reasoning_split", True)
         return payload