Optimize model selection for cost-efficiency on structured extraction

The task profile is search tool invocation + JSON extraction — not
reasoning or creative work. Mini models handle this equally well at
3-5x lower cost per call.

OpenAI changes:
- Rename is_mainline_openai_model -> is_search_capable_model
- Include mini variants (gpt-5-mini, gpt-4.1-mini) in candidate pool
- Exclude gpt-4o-mini (no domain filtering) and nano (no web_search)
- select_openai_model() now prefers mini within newest generation
- OPENAI_FALLBACK_MODELS: gpt-5-mini first, mainline as last resort
- MODEL_FALLBACK_ORDER: same mini-first ordering

xAI changes:
- Switch alias from grok-4-1-fast (reasoning) to
  grok-4-1-fast-non-reasoning — same token price, faster response,
  no wasted reasoning tokens for structured extraction

Cost per Reddit search call: ~$0.015 (gpt-5-mini) vs ~$0.044 (gpt-4.1)

tests/test_openai_reddit.py

@@ -64,13 +64,18 @@ class TestIsModelAccessError(unittest.TestCase):
 class TestModelFallbackOrder(unittest.TestCase):
     """Tests for MODEL_FALLBACK_ORDER constant."""
 
-    def test_contains_gpt4o(self):
-        """Fallback list should include gpt-4o."""
+    def test_mini_first(self):
+        """Mini models should come first (cost-efficient for structured extraction)."""
+        self.assertEqual(MODEL_FALLBACK_ORDER[0], "gpt-5-mini")
+
+    def test_contains_mainline_fallbacks(self):
+        """Fallback list should include mainline models as last resort."""
+        self.assertIn("gpt-4.1", MODEL_FALLBACK_ORDER)
         self.assertIn("gpt-4o", MODEL_FALLBACK_ORDER)
 
-    def test_gpt41_is_first(self):
-        """gpt-4.1 should be the first fallback option."""
-        self.assertEqual(MODEL_FALLBACK_ORDER[0], "gpt-4.1")
+    def test_no_gpt4o_mini(self):
+        """gpt-4o-mini should NOT be in fallback (no domain filtering support)."""
+        self.assertNotIn("gpt-4o-mini", MODEL_FALLBACK_ORDER)
 
 
 if __name__ == "__main__":