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)

scripts/lib/openai_reddit.py

@@ -7,9 +7,10 @@ from typing import Any, Dict, List, Optional
 
 from . import http, env
 
-# Fallback models when the selected model isn't accessible (e.g., org not verified for GPT-5)
-# Note: gpt-4o-mini does NOT support web_search with filters param, so exclude it
-MODEL_FALLBACK_ORDER = ["gpt-4.1", "gpt-4o"]
+# Fallback models when the selected model isn't accessible (e.g., org not verified).
+# Ordered by cost-efficiency: mini models handle structured extraction equally well.
+# Note: gpt-4o-mini does NOT support web_search with filters — excluded.
+MODEL_FALLBACK_ORDER = ["gpt-5-mini", "gpt-4.1-mini", "gpt-4.1", "gpt-4o"]
 
 
 def _log_error(msg: str):