Warn when configured LLM is not frontier-recommended

.pre-commit-config.yaml

@@ -11,12 +11,13 @@ repos:
 
   # MyPy for static type checking
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.16.0
+    rev: v1.19.1
     hooks:
       - id: mypy
         additional_dependencies: [
           types-requests,
           types-python-dateutil,
+          types-Pygments,
           pydantic,
           fastapi,
           pytest,

strix/config/models.py

@@ -59,6 +59,42 @@ def _resolve_prefixed_model(
     ),
 )
 
+RECOMMENDED_MODEL_NAMES = (
+    "openai/gpt-5.5",
+    "openai/gpt-5.5-pro",
+    "openai/gpt-5.4",
+    "openai/gpt-5.4-pro",
+    "openai/gpt-5.3-codex",
+    "anthropic/claude-opus-4-8",
+    "anthropic/claude-sonnet-4-6",
+    "vertex_ai/gemini-3.1-pro-preview",
+    "gemini/gemini-3.1-pro-preview",
+    "xai/grok-4.3",
+    "deepseek/deepseek-v4-pro",
+    "deepseek/deepseek-reasoner",
+    "dashscope/qwen3-max-2026-01-23",
+    "moonshot/kimi-k2.7-code",
+    "moonshot/kimi-k2.6",
+    "mistral/mistral-medium-3-5",
+    "mistral/magistral-medium-latest",
+)
+
+_RECOMMENDED_MODEL_NAME_SET = frozenset(name.lower() for name in RECOMMENDED_MODEL_NAMES)
+
+FRONTIER_MODEL_FAMILIES = (
+    (("azure", "azure_ai", "bedrock_mantle", "openai"), ("gpt-5",)),
+    (
+        ("anthropic", "azure_ai", "bedrock", "claude", "databricks", "snowflake", "vertex_ai"),
+        ("claude-opus-4", "claude-sonnet-4"),
+    ),
+    (("google", "gemini", "vertex_ai"), ("gemini-3",)),
+    (("xai", "x-ai"), ("grok-4",)),
+    (("deepseek",), ("deepseek-v4", "deepseek-r1", "deepseek-reasoner")),
+    (("alibaba", "dashscope", "qwen"), ("qwen3.7", "qwen3.5", "qwen3-max")),
+    (("moonshot", "moonshotai", "kimi"), ("kimi-k2.7", "kimi-k2.6", "kimi-k2.5")),
+    (("mistral", "mistralai"), ("mistral-medium-3-5", "magistral-medium")),
+)
+
 
 def configure_sdk_model_defaults(settings: Settings) -> None:
     """Apply Strix config to SDK-native defaults."""
@@ -154,6 +190,78 @@ def model_supports_reasoning(model_name: str) -> bool:
     return bool(entry and entry.get("supports_reasoning"))
 
 
+def is_recommended_or_frontier_model(model_name: str) -> bool:
+    """Return whether a model is recommended or in a frontier model family."""
+    name = _normalized_model_name(model_name)
+    if not name:
+        return False
+    if name in _RECOMMENDED_MODEL_NAME_SET:
+        return True
+    provider_name, bare_model_name = _split_model_provider(name)
+    return any(
+        _matches_frontier_family(provider_name, bare_model_name, provider_markers, prefixes)
+        for provider_markers, prefixes in FRONTIER_MODEL_FAMILIES
+    )
+
+
+def _normalized_model_name(model_name: str) -> str:
+    name = model_name.strip().lower()
+    for prefix in ("litellm/", "any-llm/"):
+        if name.startswith(prefix):
+            name = name[len(prefix) :]
+            break
+    return name
+
+
+def _split_model_provider(model_name: str) -> tuple[str | None, str]:
+    if "/" not in model_name:
+        return None, model_name
+    provider_name, bare_model_name = model_name.rsplit("/", 1)
+    return provider_name, bare_model_name
+
+
+def _matches_frontier_family(
+    provider_name: str | None,
+    model_name: str,
+    provider_markers: tuple[str, ...],
+    model_prefixes: tuple[str, ...],
+) -> bool:
+    if not _matches_model_prefix(model_name, model_prefixes):
+        return False
+    if provider_name is None:
+        return True
+    return _contains_provider_marker(
+        provider_name, provider_markers, split_compound_names=True
+    ) or _contains_provider_marker(model_name, provider_markers)
+
+
+def _matches_model_prefix(model_name: str, model_prefixes: tuple[str, ...]) -> bool:
+    return any(
+        candidate.startswith(prefix)
+        for candidate in _model_name_candidates(model_name)
+        for prefix in model_prefixes
+    )
+
+
+def _model_name_candidates(model_name: str) -> tuple[str, ...]:
+    if "." not in model_name:
+        return (model_name,)
+    suffixes = tuple(
+        model_name.split(".", index)[-1] for index in range(1, model_name.count(".") + 1)
+    )
+    return (model_name, *suffixes)
+
+
+def _contains_provider_marker(
+    value: str, provider_markers: tuple[str, ...], *, split_compound_names: bool = False
+) -> bool:
+    parts = set(value.replace(".", "/").split("/"))
+    if split_compound_names:
+        for separator in ("_", "-"):
+            parts.update(piece for part in tuple(parts) for piece in part.split(separator))
+    return any(marker in parts for marker in provider_markers)
+
+
 def is_known_openai_bare_model(model_name: str) -> bool:
     import litellm
 

strix/core/hooks.py

@@ -28,7 +28,10 @@ class ReportUsageHooks(RunHooks[dict[str, Any]]):
 
     def __init__(self, *, model: str, max_budget_usd: float | None = None) -> None:
         import math
-        if max_budget_usd is not None and (not math.isfinite(max_budget_usd) or max_budget_usd <= 0):
+
+        if max_budget_usd is not None and (
+            not math.isfinite(max_budget_usd) or max_budget_usd <= 0
+        ):
             raise ValueError("max_budget_usd must be a finite number greater than 0")
         self._model = model
         self._max_budget_usd = max_budget_usd

strix/interface/main.py

@@ -23,9 +23,11 @@
     persist_current,
 )
 from strix.config.models import (
+    RECOMMENDED_MODEL_NAMES,
     StrixProvider,
     configure_sdk_model_defaults,
     is_known_openai_bare_model,
+    is_recommended_or_frontier_model,
 )
 from strix.core.paths import run_dir_for, runtime_state_dir
 from strix.interface.cli import run_cli
@@ -254,6 +256,32 @@ async def warm_up_llm() -> None:
             )
             sys.exit(1)
 
+        if raw_model and not is_recommended_or_frontier_model(raw_model):
+            warn_text = Text()
+            warn_text.append("MODEL QUALITY WARNING", style="bold yellow")
+            warn_text.append("\n\n", style="white")
+            warn_text.append(f"'{raw_model}'", style="bold cyan")
+            warn_text.append(
+                " is not a recommended frontier model for Strix.\nSecurity scans work best with:\n",
+                style="white",
+            )
+            for recommended_model in RECOMMENDED_MODEL_NAMES:
+                warn_text.append(f"• {recommended_model}\n", style="bold cyan")
+            warn_text.append(
+                "\nYou can continue, but weaker models may miss vulnerabilities "
+                "or produce lower-quality findings.",
+                style="white",
+            )
+            console.print(
+                Panel(
+                    warn_text,
+                    title="[bold white]STRIX",
+                    title_align="left",
+                    border_style="yellow",
+                    padding=(1, 2),
+                ),
+            )
+
         model = StrixProvider().get_model(raw_model)
         await asyncio.wait_for(
             model.get_response(
@@ -310,6 +338,7 @@ def _positive_budget(value: str) -> float:
     except ValueError as exc:
         raise argparse.ArgumentTypeError(f"invalid float value: {value!r}") from exc
     import math
+
     if not math.isfinite(budget) or budget <= 0:
         raise argparse.ArgumentTypeError("must be a finite number greater than 0")
     return budget

strix/interface/tui/app.py

@@ -83,7 +83,7 @@ def _on_key(self, event: events.Key) -> None:
 
         super()._on_key(event)
 
-    @on(TextArea.Changed)  # type: ignore[misc]
+    @on(TextArea.Changed)  # type: ignore[untyped-decorator]
     def _update_height(self, _event: TextArea.Changed | None = None) -> None:
         if not self.parent:
             return
@@ -1549,7 +1549,7 @@ def _render_chat_content(self, msg_data: dict[str, Any]) -> Any:
 
         return AgentMessageRenderer.render_simple(content)
 
-    @on(Tree.NodeHighlighted)  # type: ignore[misc]
+    @on(Tree.NodeHighlighted)  # type: ignore[untyped-decorator]
     def handle_tree_highlight(self, event: Tree.NodeHighlighted) -> None:
         if len(self.screen_stack) > 1 or self.show_splash:
             return
@@ -1569,7 +1569,7 @@ def handle_tree_highlight(self, event: Tree.NodeHighlighted) -> None:
             if agent_id:
                 self.selected_agent_id = agent_id
 
-    @on(Tree.NodeSelected)  # type: ignore[misc]
+    @on(Tree.NodeSelected)  # type: ignore[untyped-decorator]
     def handle_tree_node_selected(self, event: Tree.NodeSelected) -> None:
         if len(self.screen_stack) > 1 or self.show_splash:
             return

strix/runtime/docker_client.py

@@ -48,7 +48,7 @@
 class StrixDockerSandboxClient(DockerSandboxClient):
     # Host directories to bind-mount into the container, set by the docker
     # backend before ``create()``. Each item is ``{source, target, read_only}``.
-    strix_bind_mounts: list[dict[str, Any]] = []  # overridden per-instance in backends.py
+    strix_bind_mounts: list[dict[str, Any]] | None = None
 
     async def _create_container(
         self,

tests/test_models.py

@@ -0,0 +1,62 @@
+"""Tests for LLM model recommendation helpers."""
+
+from __future__ import annotations
+
+import pytest
+
+from strix.config.models import RECOMMENDED_MODEL_NAMES, is_recommended_or_frontier_model
+
+
+@pytest.mark.parametrize("model_name", RECOMMENDED_MODEL_NAMES)
+def test_recommended_models_are_accepted(model_name: str) -> None:
+    assert is_recommended_or_frontier_model(model_name)
+
+
+def test_recommended_models_are_matched_case_insensitively() -> None:
+    assert is_recommended_or_frontier_model("Vertex_AI/Gemini-3-Pro-Preview")
+
+
+@pytest.mark.parametrize(
+    "model_name",
+    [
+        "gpt-5.5",
+        "litellm/openai/gpt-5.4-pro",
+        "azure_ai/gpt-5.5-pro",
+        "bedrock_mantle/openai.gpt-5.5",
+        "anthropic/claude-opus-4-8",
+        "anthropic.claude-opus-4-8",
+        "vertex_ai/claude-sonnet-4-6@default",
+        "any-llm/anthropic/claude-sonnet-4-6",
+        "vertex_ai/gemini-3.1-pro-preview",
+        "openrouter/google/gemini-3.1-pro-preview",
+        "xai/grok-4.3",
+        "openrouter/x-ai/grok-4",
+        "deepseek/deepseek-v4-pro",
+        "deepseek/deepseek-r1-0528",
+        "deepseek/deepseek-reasoner",
+        "dashscope/qwen3-max-2026-01-23",
+        "qwen3.7-max",
+        "moonshot/kimi-k2.6",
+        "kimi-k2.7-code",
+        "mistral/mistral-medium-3-5",
+        "mistral/magistral-medium-latest",
+    ],
+)
+def test_frontier_model_families_are_accepted(model_name: str) -> None:
+    assert is_recommended_or_frontier_model(model_name)
+
+
+@pytest.mark.parametrize(
+    "model_name",
+    [
+        "",
+        "openai/gpt-4.1",
+        "anthropic/claude-3-5-sonnet-latest",
+        "ollama/llama3.1",
+        "deepseek/deepseek-chat",
+        "custom-ollama/gpt-5-mini-local",
+        "custom-provider/claude-opus-4-local",
+    ],
+)
+def test_non_frontier_models_are_rejected(model_name: str) -> None:
+    assert not is_recommended_or_frontier_model(model_name)