feat(config): align tier defaults to cost-conscious models + qwen3-235b on tier C

- Tier S → google/gemini-3-flash-preview ($0.50/$3.00)
- Tier A/B → deepseek/deepseek-v4-flash ($0.14/$0.28)
- Tier C → qwen/qwen3-235b-a22b-2507 ($0.071/$0.10) — Phase 2 target
- Tier D → openai/gpt-oss-20b ($0.03/$0.14)

Aggiornato cost_tracker con prezzi reali per tier. Defaults config.py
ora rispecchiano .env corrente per evitare divergenze dead-code.

Tier S/A/B/D restano cablati ma non ancora invocati nel loop Phase 2
(solo Hypothesis tier C attivo).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

tests/unit/test_config.py

@@ -73,9 +73,9 @@ def test_settings_llm_model_defaults(monkeypatch: pytest.MonkeyPatch) -> None:
 
     s = Settings(_env_file=None)  # type: ignore[call-arg]
 
-    assert s.llm_model_tier_s == "anthropic/claude-opus-4-7"
-    assert s.llm_model_tier_a == "anthropic/claude-sonnet-4-6"
-    assert s.llm_model_tier_b == "anthropic/claude-sonnet-4-6"
-    assert s.llm_model_tier_c == "qwen/qwen-2.5-72b-instruct"
-    assert s.llm_model_tier_d == "meta-llama/llama-3.3-70b-instruct"
+    assert s.llm_model_tier_s == "google/gemini-3-flash-preview"
+    assert s.llm_model_tier_a == "deepseek/deepseek-v4-flash"
+    assert s.llm_model_tier_b == "deepseek/deepseek-v4-flash"
+    assert s.llm_model_tier_c == "qwen/qwen3-235b-a22b-2507"
+    assert s.llm_model_tier_d == "openai/gpt-oss-20b"
     assert s.openrouter_base_url == "https://openrouter.ai/api/v1"