refactor(llm): streamline reasoning effort handling and remove unused patterns

strix/llm/llm.py

@@ -4,7 +4,6 @@ import os
 from collections.abc import AsyncIterator
 from dataclasses import dataclass
 from enum import Enum
-from fnmatch import fnmatch
 from pathlib import Path
 from typing import Any
 
@@ -14,7 +13,7 @@ from jinja2 import (
     FileSystemLoader,
     select_autoescape,
 )
-from litellm import completion_cost, stream_chunk_builder
+from litellm import completion_cost, stream_chunk_builder, supports_reasoning
 from litellm.utils import supports_prompt_caching, supports_vision
 
 from strix.llm.config import LLMConfig
@@ -63,57 +62,6 @@ class LLMRequestFailedError(Exception):
         self.details = details
 
 
-SUPPORTS_STOP_WORDS_FALSE_PATTERNS: list[str] = [
-    "o1*",
-    "grok-4-0709",
-    "grok-code-fast-1",
-    "deepseek-r1-0528*",
-]
-
-REASONING_EFFORT_PATTERNS: list[str] = [
-    "o1-2024-12-17",
-    "o1",
-    "o3",
-    "o3-2025-04-16",
-    "o3-mini-2025-01-31",
-    "o3-mini",
-    "o4-mini",
-    "o4-mini-2025-04-16",
-    "gemini-2.5-flash",
-    "gemini-2.5-pro",
-    "gpt-5*",
-    "deepseek-r1-0528*",
-    "claude-sonnet-4-5*",
-    "claude-haiku-4-5*",
-]
-
-
-def normalize_model_name(model: str) -> str:
-    raw = (model or "").strip().lower()
-    if "/" in raw:
-        name = raw.split("/")[-1]
-        if ":" in name:
-            name = name.split(":", 1)[0]
-    else:
-        name = raw
-    if name.endswith("-gguf"):
-        name = name[: -len("-gguf")]
-    return name
-
-
-def model_matches(model: str, patterns: list[str]) -> bool:
-    raw = (model or "").strip().lower()
-    name = normalize_model_name(model)
-    for pat in patterns:
-        pat_l = pat.lower()
-        if "/" in pat_l:
-            if fnmatch(raw, pat_l):
-                return True
-        elif fnmatch(name, pat_l):
-            return True
-    return False
-
-
 class StepRole(str, Enum):
     AGENT = "agent"
     USER = "user"
@@ -421,17 +369,13 @@ class LLM:
             "supported": supports_prompt_caching(self.config.model_name),
         }
 
-    def _should_include_stop_param(self) -> bool:
-        if not self.config.model_name:
-            return True
-
-        return not model_matches(self.config.model_name, SUPPORTS_STOP_WORDS_FALSE_PATTERNS)
-
     def _should_include_reasoning_effort(self) -> bool:
         if not self.config.model_name:
             return False
-
+        try:
-        return model_matches(self.config.model_name, REASONING_EFFORT_PATTERNS)
+            return bool(supports_reasoning(model=self.config.model_name))
+        except Exception:  # noqa: BLE001
+            return False
 
     def _model_supports_vision(self) -> bool:
         if not self.config.model_name:
@@ -499,8 +443,7 @@ class LLM:
         if _LLM_API_BASE:
             completion_args["api_base"] = _LLM_API_BASE
 
-        if self._should_include_stop_param():
+        completion_args["stop"] = ["</function>"]
-            completion_args["stop"] = ["</function>"]
 
         if self._should_include_reasoning_effort():
             completion_args["reasoning_effort"] = "high"