feat(llm): full multi-tier S/A/B/C/D with routing + pricing

Estende ModelTier a 5 livelli (S/A/B/C/D) con routing automatico:
S/A/B via Anthropic SDK, C/D via OpenRouter (OpenAI SDK). Aggiunge
prezzi per tier S (Opus), A (Sonnet placeholder) e D (Llama). Refactor
LLMClient.complete con dispatch tramite tier_models map e helper
_call_anthropic / _call_openrouter. Settings esposte per tutti e 5
i modelli env-configurabili.

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

tests/unit/test_cost_tracker.py

@@ -30,3 +30,34 @@ def test_tracker_per_tier_breakdown():
     summary = t.summary()
     assert "C" in summary["by_tier"]
     assert "B" in summary["by_tier"]
+
+
+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
+
+
+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
+
+
+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
+
+
+def test_tracker_summary_contains_all_five_tiers():
+    t = CostTracker()
+    for tier in (ModelTier.S, ModelTier.A, ModelTier.B, ModelTier.C, ModelTier.D):
+        t.record(
+            input_tokens=1_000,
+            output_tokens=1_000,
+            tier=tier,
+            run_id="r",
+            agent_id=f"a-{tier.value}",
+        )
+    summary = t.summary()
+    for tier_letter in ("S", "A", "B", "C", "D"):
+        assert tier_letter in summary["by_tier"]
+        assert summary["by_tier"][tier_letter]["calls"] == 1