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_cost_tracker.py

@@ -4,12 +4,12 @@ from multi_swarm.llm.cost_tracker import CostTracker, estimate_cost
 
 def test_estimate_cost_tier_c():
     cost = estimate_cost(input_tokens=1_000_000, output_tokens=1_000_000, tier=ModelTier.C)
-    assert cost == 0.40 + 0.40
+    assert cost == 0.071 + 0.10
 
 
 def test_estimate_cost_tier_b():
     cost = estimate_cost(input_tokens=1_000_000, output_tokens=1_000_000, tier=ModelTier.B)
-    assert cost == 3.00 + 15.00
+    assert cost == 0.14 + 0.28
 
 
 def test_tracker_accumulates():
@@ -34,17 +34,17 @@ def test_tracker_per_tier_breakdown():
 
 def test_estimate_cost_tier_s():
     cost = estimate_cost(input_tokens=1_000_000, output_tokens=1_000_000, tier=ModelTier.S)
-    assert cost == 15.00 + 75.00
+    assert cost == 0.50 + 3.00
 
 
 def test_estimate_cost_tier_a():
     cost = estimate_cost(input_tokens=1_000_000, output_tokens=1_000_000, tier=ModelTier.A)
-    assert cost == 3.00 + 15.00
+    assert cost == 0.14 + 0.28
 
 
 def test_estimate_cost_tier_d():
     cost = estimate_cost(input_tokens=1_000_000, output_tokens=1_000_000, tier=ModelTier.D)
-    assert cost == 0.10 + 0.30
+    assert cost == 0.03 + 0.14
 
 
 def test_tracker_summary_contains_all_five_tiers():