feat(admin): Modell-Browser (Cloud) mit Tool-Badge, Preisen & $/Antwort

Phase 1 (Cloud) des Modell-Auswahlmenues:
- Token je Turn im CostMeter (record_openrouter_cost liest prompt/completion_
  tokens) -> globales Turn-Profil (turn_profile, EMA) fuer die $/Antwort-Schaetzung.
- app/model_menu.py: gecachter OpenRouter-Katalog -> Chat-Modelle mit tool_supported
  (supported_parameters), eval_verified (kuratiert), Up/Down-Preisen pro Mio. Token
  und geschaetzter $/Antwort (Profil x Modellpreise).
- GET /api/admin/model-menu; durchsuchbarer Browser im System-Tab, "Verwenden"
  setzt openrouter_llm_model (Freitext/Preset bleiben).
Lokale Modelle (Durchsatz/Strom): Phase 2. Konzept in Ideen/.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

app/model_menu.py

@@ -0,0 +1,96 @@
+"""Modell-Auswahlmenü (Phase 1: Cloud/OpenRouter).
+
+Liefert die benutzbaren Chat-Modelle aus dem OpenRouter-Katalog mit Tool-Fähigkeit,
+⬆/⬇-Preisen pro Mio. Token und einer geschätzten „$/Antwort" (aus dem live
+gemessenen Turn-Token-Profil). Katalog wird gecacht. Lokale Modelle: Phase 2.
+
+Siehe Ideen/MODELL_MENUE_KONZEPT.md.
+"""
+import time
+
+import httpx
+
+from app.runtime_config import runtime_settings
+
+_CATALOG_URL = "https://openrouter.ai/api/v1/models"
+_TTL = 3600.0  # Katalog ändert sich selten
+_cache: dict = {"t": 0.0, "data": None}
+
+# Im eval/tool_calling als web-suchtauglich bestätigt (✅). Klein halten/pflegen.
+EVAL_VERIFIED = {
+    "mistralai/mistral-small-3.2-24b-instruct",
+}
+
+# Annahmen, bis echte Turn-Messdaten vorliegen.
+_DEFAULT_PROMPT_TOKENS = 700
+_DEFAULT_COMPLETION_TOKENS = 130
+
+
+def invalidate_cache() -> None:
+    _cache["data"] = None
+
+
+def _f(x) -> float:
+    try:
+        return float(x)
+    except (TypeError, ValueError):
+        return 0.0
+
+
+async def _fetch_catalog(api_key: str) -> list:
+    now = time.monotonic()
+    if _cache["data"] is not None and now - _cache["t"] < _TTL:
+        return _cache["data"]
+    headers = {"Authorization": f"Bearer {api_key}"} if api_key else {}
+    async with httpx.AsyncClient(timeout=30.0) as client:
+        resp = await client.get(_CATALOG_URL, headers=headers)
+        resp.raise_for_status()
+        data = resp.json().get("data", [])
+    _cache["data"] = data
+    _cache["t"] = now
+    return data
+
+
+async def build_model_menu(store=None, cfg=None) -> dict:
+    cfg = cfg or runtime_settings
+    try:
+        catalog = await _fetch_catalog(cfg.openrouter_api_key)
+    except Exception as exc:  # noqa: BLE001 — graceful, UI zeigt Fehler
+        return {"available": False, "error": str(exc), "models": [], "profile": {}}
+
+    prof = store.get_turn_profile() if store is not None else {}
+    avg_p = prof.get("avg_prompt") or _DEFAULT_PROMPT_TOKENS
+    avg_c = prof.get("avg_completion") or _DEFAULT_COMPLETION_TOKENS
+
+    models = []
+    for m in catalog:
+        modality = (m.get("architecture") or {}).get("modality") or ""
+        if not modality.endswith("->text"):   # nur Chat-/Completion-Modelle
+            continue
+        pricing = m.get("pricing") or {}
+        p_in = _f(pricing.get("prompt"))        # $/Token
+        p_out = _f(pricing.get("completion"))
+        mid = m.get("id")
+        models.append({
+            "id": mid,
+            "name": m.get("name") or mid,
+            "context_length": m.get("context_length"),
+            "price_in_per_mtok": round(p_in * 1_000_000, 4),
+            "price_out_per_mtok": round(p_out * 1_000_000, 4),
+            "tool_supported": "tools" in (m.get("supported_parameters") or []),
+            "eval_verified": mid in EVAL_VERIFIED,
+            "est_cost_per_answer": round(avg_p * p_in + avg_c * p_out, 6),
+        })
+    # Sortierung: eval-bestätigt zuerst, dann tool-fähig, dann alphabetisch.
+    models.sort(key=lambda x: (not x["eval_verified"], not x["tool_supported"], x["name"].lower()))
+
+    return {
+        "available": True,
+        "models": models,
+        "profile": {
+            "avg_prompt": round(avg_p),
+            "avg_completion": round(avg_c),
+            "measured": bool(prof.get("samples")),
+        },
+        "current": cfg.openrouter_llm_model,
+    }