models_dev_stub.py

"""Models.dev registry integration — primary database for providers and models.



Fetches from https://models.dev/api.json — a community-maintained database

of 4000+ models across 109+ providers.  Provides:



- **Provider metadata**: name, base URL, env vars, documentation link

- **Model metadata**: context window, max output, cost/M tokens, capabilities

  (reasoning, tools, vision, PDF, audio), modalities, knowledge cutoff,

  open-weights flag, family grouping, deprecation status



Data resolution order (like TypeScript OpenCode):

  1. Bundled snapshot (ships with the package — offline-first)

  2. Disk cache (~/.hermes/models_dev_cache.json)

  3. Network fetch (https://models.dev/api.json)

  4. Background refresh every 60 minutes



Other modules should import the dataclasses and query functions from here

rather than parsing the raw JSON themselves.

"""

import json
import logging
import time
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple

from utils import atomic_json_write

import requests

logger = logging.getLogger(__name__)

# HuggingMes: curated model allowlist
_CURATED_MODEL_IDS = ["meta-llama/llama-3.3-70b-instruct:free", "deepseek/deepseek-v4-flash:free", "z-ai/glm-4.5-air:free", "minimax/minimax-m2.5:free", "google/gemma-4-31b-it:free", "google/gemma-4-26b-a4b-it:free", "qwen/qwen3-coder:free", "qwen/qwen3-next-80b-a3b-instruct:free", "nousresearch/hermes-3-llama-3.1-405b:free", "openai/gpt-oss-120b:free", "openai/gpt-oss-20b:free", "baidu/cobuddy:free", "arcee-ai/trinity-large-thinking:free", "liquid/lfm-2.5-1.2b-thinking:free", "liquid/lfm-2.5-1.2b-instruct:free", "meta-llama/llama-3.2-3b-instruct:free", "cognitivecomputations/dolphin-mistral-24b-venice-edition:free", "nvidia/nemotron-3-super-120b-a12b:free", "nvidia/nemotron-3-nano-30b-a3b:free", "nvidia/nemotron-3-nano-omni-30b-a3b-reasoning:free", "nvidia/nemotron-nano-9b-v2:free", "nvidia/nemotron-nano-12b-v2-vl:free", "nvidia/llama-nemotron-embed-vl-1b-v2:free", "poolside/laguna-m.1:free", "poolside/laguna-xs.2:free", "openrouter/auto:free"]

MODELS_DEV_URL = "https://models.dev/api.json"
_MODELS_DEV_CACHE_TTL = 3600  # 1 hour in-memory

# In-memory cache
_models_dev_cache: Dict[str, Any] = {}
_models_dev_cache_time: float = 0


# ---------------------------------------------------------------------------
# Dataclasses — rich metadata for providers and models
# ---------------------------------------------------------------------------

@dataclass
class ModelInfo:
    """Full metadata for a single model from models.dev."""

    id: str
    name: str
    family: str
    provider_id: str        # models.dev provider ID (e.g. "anthropic")

    # Capabilities
    reasoning: bool = False
    tool_call: bool = False
    attachment: bool = False       # supports image/file attachments (vision)
    temperature: bool = False
    structured_output: bool = False
    open_weights: bool = False

    # Modalities
    input_modalities: Tuple[str, ...] = ()    # ("text", "image", "pdf", ...)
    output_modalities: Tuple[str, ...] = ()

    # Limits
    context_window: int = 0
    max_output: int = 0
    max_input: Optional[int] = None

    # Cost (per million tokens, USD)
    cost_input: float = 0.0
    cost_output: float = 0.0
    cost_cache_read: Optional[float] = None
    cost_cache_write: Optional[float] = None

    # Metadata
    knowledge_cutoff: str = ""
    release_date: str = ""
    status: str = ""          # "alpha", "beta", "deprecated", or ""
    interleaved: Any = False  # True or {"field": "reasoning_content"}

    def has_cost_data(self) -> bool:
        return self.cost_input > 0 or self.cost_output > 0

    def supports_vision(self) -> bool:
        return self.attachment or "image" in self.input_modalities

    def supports_pdf(self) -> bool:
        return "pdf" in self.input_modalities

    def supports_audio_input(self) -> bool:
        return "audio" in self.input_modalities

    def format_cost(self) -> str:
        """Human-readable cost string, e.g. '$3.00/M in, $15.00/M out'."""
        if not self.has_cost_data():
            return "unknown"
        parts = [f"${self.cost_input:.2f}/M in", f"${self.cost_output:.2f}/M out"]
        if self.cost_cache_read is not None:
            parts.append(f"cache read ${self.cost_cache_read:.2f}/M")
        return ", ".join(parts)

    def format_capabilities(self) -> str:
        """Human-readable capabilities, e.g. 'reasoning, tools, vision, PDF'."""
        caps = []
        if self.reasoning:
            caps.append("reasoning")
        if self.tool_call:
            caps.append("tools")
        if self.supports_vision():
            caps.append("vision")
        if self.supports_pdf():
            caps.append("PDF")
        if self.supports_audio_input():
            caps.append("audio")
        if self.structured_output:
            caps.append("structured output")
        if self.open_weights:
            caps.append("open weights")
        return ", ".join(caps) if caps else "basic"


@dataclass
class ProviderInfo:
    """Full metadata for a provider from models.dev."""

    id: str                         # models.dev provider ID
    name: str                       # display name
    env: Tuple[str, ...]            # env var names for API key
    api: str                        # base URL
    doc: str = ""                   # documentation URL
    model_count: int = 0


# ---------------------------------------------------------------------------
# Provider ID mapping: Hermes ↔ models.dev
# ---------------------------------------------------------------------------

# Hermes provider names → models.dev provider IDs
PROVIDER_TO_MODELS_DEV: Dict[str, str] = {
    "openrouter": "openrouter",
    "novita": "novita-ai",
    "anthropic": "anthropic",
    "openai": "openai",
    "openai-codex": "openai",
    "zai": "zai",
    "kimi": "kimi-for-coding",
    "kimi-coding": "kimi-for-coding",
    "moonshot": "kimi-for-coding",
    "stepfun": "stepfun",
    "kimi-coding-cn": "kimi-for-coding",
    "minimax": "minimax",
    "minimax-oauth": "minimax",
    "minimax-cn": "minimax-cn",
    "deepseek": "deepseek",
    "alibaba": "alibaba",
    "qwen-oauth": "alibaba",
    "copilot": "github-copilot",
    "ai-gateway": "vercel",
    "opencode-zen": "opencode",
    "opencode-go": "opencode-go",
    "kilocode": "kilo",
    "fireworks": "fireworks-ai",
    "huggingface": "huggingface",
    "gemini": "google",
    "google": "google",
    "xai": "xai",
    "xiaomi": "xiaomi",
    "nvidia": "nvidia",
    "groq": "groq",
    "mistral": "mistral",
    "togetherai": "togetherai",
    "perplexity": "perplexity",
    "cohere": "cohere",
    "ollama-cloud": "ollama-cloud",
}

# Reverse mapping: models.dev → Hermes (built lazily)
_MODELS_DEV_TO_PROVIDER: Optional[Dict[str, str]] = None



def _get_cache_path() -> Path:
    """Return path to disk cache file."""
    from hermes_constants import get_hermes_home
    return get_hermes_home() / "models_dev_cache.json"


def _load_disk_cache() -> Dict[str, Any]:
    """Load models.dev data from disk cache."""
    try:
        cache_path = _get_cache_path()
        if cache_path.exists():
            with open(cache_path, encoding="utf-8") as f:
                return json.load(f)
    except Exception as e:
        logger.debug("Failed to load models.dev disk cache: %s", e)
    return {}


def _disk_cache_age_seconds() -> Optional[float]:
    """Return age (in seconds) of the disk cache file, or None if missing.



    Used by ``fetch_models_dev`` to short-circuit the network probe when

    a recent on-disk cache exists. Errors (missing file, permission

    denied, weird filesystem) all return None — callers fall through

    to the network fetch path.

    """
    try:
        cache_path = _get_cache_path()
        if not cache_path.exists():
            return None
        mtime = cache_path.stat().st_mtime
        age = time.time() - mtime
        # Negative age means the file's mtime is in the future (clock skew
        # or system clock reset). Treat as "unknown freshness" → fall
        # through to network so we don't serve potentially-bad data
        # forever.
        if age < 0:
            return None
        return age
    except Exception as e:
        logger.debug("Failed to stat models.dev disk cache: %s", e)
        return None


def _save_disk_cache(data: Dict[str, Any]) -> None:
    """Save models.dev data to disk cache atomically."""
    try:
        cache_path = _get_cache_path()
        atomic_json_write(cache_path, data, indent=None, separators=(",", ":"))
    except Exception as e:
        logger.debug("Failed to save models.dev disk cache: %s", e)


def fetch_models_dev(force_refresh: bool = False) -> Dict[str, Any]:
    """Fetch models.dev registry. Cache hierarchy: in-mem → disk → network.



    Returns the full registry dict keyed by provider ID, or empty dict on failure.



    Cache hierarchy (when ``force_refresh=False``):

      1. In-memory cache, populated and < TTL old → return immediately.

      2. **Disk cache file < TTL old by mtime → load, populate in-mem, return.**

         No network call. Saves ~500 ms per cold-start agent construction;

         ``models.dev`` only changes when providers add new models, so a

         1 hour staleness window is acceptable (same TTL as in-mem cache).

      3. Network fetch → on success, save to disk + in-mem and return.

      4. Network fails → fall back to ANY available disk cache (even stale)

         with a short 5 min in-mem grace period before retrying network.



    When ``force_refresh=True`` (used by ``hermes config refresh``, the

    \"refresh model catalog\" code path), stages 1 and 2 are skipped. The

    function always hits the network and only falls back to disk if the

    network call fails.

    """
    global _models_dev_cache, _models_dev_cache_time

    # Stage 1: fresh in-memory cache wins. This is the hot path on
    # long-lived processes — no I/O, no system calls.
    if (
        not force_refresh
        and _models_dev_cache
        and (time.time() - _models_dev_cache_time) < _MODELS_DEV_CACHE_TTL
    ):
        return _models_dev_cache

    # Stage 2: fresh-by-mtime disk cache short-circuits the network call.
    # Only kicks in on cold-start processes (in-mem cache is empty or
    # expired) and only when the user hasn't asked for a forced refresh.
    # Skipped if the disk cache file is missing, unreadable, or older
    # than _MODELS_DEV_CACHE_TTL.
    if not force_refresh:
        disk_age = _disk_cache_age_seconds()
        if disk_age is not None and disk_age < _MODELS_DEV_CACHE_TTL:
            disk_data = _load_disk_cache()
            if disk_data:
                _models_dev_cache = disk_data
                # Anchor in-mem TTL to the disk file's age so we don't
                # extend an already-aging cache by another full hour.
                _models_dev_cache_time = time.time() - disk_age
                logger.debug(
                    "Loaded models.dev from fresh disk cache "
                    "(%d providers, age=%.0fs)", len(disk_data), disk_age,
                )
                return _models_dev_cache

    # Stage 3: network fetch.
    try:
        response = requests.get(MODELS_DEV_URL, timeout=15)
        response.raise_for_status()
        data = response.json()
        if isinstance(data, dict) and data:
            _models_dev_cache = data
            _models_dev_cache_time = time.time()
            _save_disk_cache(data)
            logger.debug(
                "Fetched models.dev registry: %d providers, %d total models",
                len(data),
                sum(len(p.get("models", {})) for p in data.values() if isinstance(p, dict)),
            )
            return data
    except Exception as e:
        logger.debug("Failed to fetch models.dev: %s", e)

    # Stage 4: network failed — fall back to whatever disk cache exists,
    # even if it's stale. Give it a short 5 min in-mem TTL so we retry
    # the network soon instead of serving stale data for a full hour.
    if not _models_dev_cache:
        _models_dev_cache = _load_disk_cache()
        if _models_dev_cache:
            _models_dev_cache_time = time.time() - _MODELS_DEV_CACHE_TTL + 300
            logger.debug("Loaded models.dev from disk cache (%d providers)", len(_models_dev_cache))

    return _models_dev_cache


def lookup_models_dev_context(provider: str, model: str) -> Optional[int]:
    """Look up context_length for a provider+model combo in models.dev.



    Returns the context window in tokens, or None if not found.

    Handles case-insensitive matching and filters out context=0 entries.

    """
    mdev_provider_id = PROVIDER_TO_MODELS_DEV.get(provider)
    if not mdev_provider_id:
        return None

    data = fetch_models_dev()
    provider_data = data.get(mdev_provider_id)
    if not isinstance(provider_data, dict):
        return None

    models = provider_data.get("models", {})
    if not isinstance(models, dict):
        return None

    # Exact match
    entry = models.get(model)
    if entry:
        ctx = _extract_context(entry)
        if ctx:
            return ctx

    # Case-insensitive match
    model_lower = model.lower()
    for mid, mdata in models.items():
        if mid.lower() == model_lower:
            ctx = _extract_context(mdata)
            if ctx:
                return ctx

    # Suffix-aware fallback: some providers (e.g. ollama-cloud) store
    # model IDs with :cloud / -cloud suffixes in models.dev while the
    # live API returns bare names.  Without this, kimi-k2.6 misses the
    # kimi-k2.6:cloud entry and falls through to stale OpenRouter metadata
    # reporting 32768 — tripping the 64k minimum-context guard.
    # The suffix-stripping in fetch_ollama_cloud_models() handles the
    # model-picker UX; this handles the context-length lookup path.
    for suffix in (":cloud", "-cloud"):
        suffixed_key = model + suffix
        entry = models.get(suffixed_key)
        if entry:
            ctx = _extract_context(entry)
            if ctx:
                return ctx
        # Also try case-insensitive
        suffixed_lower = model_lower + suffix
        for mid, mdata in models.items():
            if mid.lower() == suffixed_lower:
                ctx = _extract_context(mdata)
                if ctx:
                    return ctx

    return None


def _extract_context(entry: Dict[str, Any]) -> Optional[int]:
    """Extract context_length from a models.dev model entry.



    Returns None for invalid/zero values (some audio/image models have context=0).

    """
    if not isinstance(entry, dict):
        return None
    limit = entry.get("limit")
    if not isinstance(limit, dict):
        return None
    ctx = limit.get("context")
    if isinstance(ctx, (int, float)) and ctx > 0:
        return int(ctx)
    return None


# ---------------------------------------------------------------------------
# Model capability metadata
# ---------------------------------------------------------------------------


@dataclass
class ModelCapabilities:
    """Structured capability metadata for a model from models.dev."""

    supports_tools: bool = True
    supports_vision: bool = False
    supports_reasoning: bool = False
    context_window: int = 200000
    max_output_tokens: int = 8192
    model_family: str = ""


def _get_provider_models(provider: str) -> Optional[Dict[str, Any]]:
    """Resolve a Hermes provider ID to its models dict from models.dev.



    Returns the models dict or None if the provider is unknown or has no data.

    """
    mdev_provider_id = PROVIDER_TO_MODELS_DEV.get(provider)
    if not mdev_provider_id:
        return None

    data = fetch_models_dev()
    provider_data = data.get(mdev_provider_id)
    if not isinstance(provider_data, dict):
        return None

    models = provider_data.get("models", {})
    if not isinstance(models, dict):
        return None

    return models


def _find_model_entry(models: Dict[str, Any], model: str) -> Optional[Dict[str, Any]]:
    """Find a model entry by exact match, then case-insensitive fallback."""
    # Exact match
    entry = models.get(model)
    if isinstance(entry, dict):
        return entry

    # Case-insensitive match
    model_lower = model.lower()
    for mid, mdata in models.items():
        if mid.lower() == model_lower and isinstance(mdata, dict):
            return mdata

    return None


def get_model_capabilities(provider: str, model: str) -> Optional[ModelCapabilities]:
    """Look up full capability metadata from models.dev cache.



    Uses the existing fetch_models_dev() and PROVIDER_TO_MODELS_DEV mapping.

    Returns None if model not found.



    Extracts from model entry fields:

      - reasoning  (bool)  → supports_reasoning

      - tool_call  (bool)  → supports_tools

      - attachment (bool)  → supports_vision

      - limit.context (int) → context_window

      - limit.output  (int) → max_output_tokens

      - family     (str)   → model_family

    """
    models = _get_provider_models(provider)
    if models is None:
        return None

    entry = _find_model_entry(models, model)
    if entry is None:
        return None

    # Extract capability flags (default to False if missing)
    supports_tools = bool(entry.get("tool_call", False))
    # Vision: prefer explicit `modalities.input` when models.dev provides it.
    # The older `attachment` flag can be stale or too broad for image routing;
    # fall back to it only when the input modalities are absent/invalid.
    input_mods = entry.get("modalities", {})
    if isinstance(input_mods, dict):
        input_mods = input_mods.get("input")
    else:
        input_mods = None
    if isinstance(input_mods, list):
        supports_vision = "image" in input_mods
    else:
        supports_vision = bool(entry.get("attachment", False))
    supports_reasoning = bool(entry.get("reasoning", False))

    # Extract limits
    limit = entry.get("limit", {})
    if not isinstance(limit, dict):
        limit = {}

    ctx = limit.get("context")
    context_window = int(ctx) if isinstance(ctx, (int, float)) and ctx > 0 else 200000

    out = limit.get("output")
    max_output_tokens = int(out) if isinstance(out, (int, float)) and out > 0 else 8192

    model_family = entry.get("family", "") or ""

    return ModelCapabilities(
        supports_tools=supports_tools,
        supports_vision=supports_vision,
        supports_reasoning=supports_reasoning,
        context_window=context_window,
        max_output_tokens=max_output_tokens,
        model_family=model_family,
    )


def list_provider_models(provider: str) -> List[str]:
    """Return all model IDs for a provider from models.dev.



    Returns an empty list if the provider is unknown or has no data.

    """
    from hermes_cli.models import normalize_provider
    provider = normalize_provider(provider) or provider
    
    models = _get_provider_models(provider)
    if models is None:
        return []
    return [
        mid for mid in models.keys()
        if not _should_hide_from_provider_catalog(provider, mid)
    ]


# Patterns that indicate non-agentic or noise models (TTS, embedding,
# dated preview snapshots, live/streaming-only, image-only).
import re
_NOISE_PATTERNS: re.Pattern = re.compile(
    r"-tts\b|embedding|live-|-(preview|exp)-\d{2,4}[-_]|"
    r"-image\b|-image-preview\b|-customtools\b",
    re.IGNORECASE,
)

# Google's live Gemini catalogs currently include a mix of stale slugs and
# Gemma models whose TPM quotas are too small for normal Hermes agent traffic.
# Keep capability metadata available for direct/manual use, but hide these from
# the Gemini model catalogs we surface in setup and model selection.
_GOOGLE_HIDDEN_MODELS = frozenset({
    # Low-TPM Gemma models that trip Google input-token quota walls under
    # agent-style traffic despite advertising large context windows.
    "gemma-4-31b-it",
    "gemma-4-26b-it",
    "gemma-4-26b-a4b-it",
    "gemma-3-1b",
    "gemma-3-1b-it",
    "gemma-3-2b",
    "gemma-3-2b-it",
    "gemma-3-4b",
    "gemma-3-4b-it",
    "gemma-3-12b",
    "gemma-3-12b-it",
    "gemma-3-27b",
    "gemma-3-27b-it",
    # Stale/retired Google slugs that still surface through models.dev-backed
    # Gemini selection but 404 on the current Google endpoints.
    "gemini-1.5-flash",
    "gemini-1.5-pro",
    "gemini-1.5-flash-8b",
    "gemini-2.0-flash",
    "gemini-2.0-flash-lite",
})


def _should_hide_from_provider_catalog(provider: str, model_id: str) -> bool:
    provider_lower = (provider or "").strip().lower()
    model_lower = (model_id or "").strip().lower()
    if provider_lower in {"gemini", "google"} and model_lower in _GOOGLE_HIDDEN_MODELS:
        return True
    return False


def list_agentic_models(provider: str) -> List[str]:
    """Return model IDs suitable for agentic use from models.dev.



    Filters for tool_call=True and excludes noise (TTS, embedding,

    dated preview snapshots, live/streaming, image-only models).

    Returns an empty list on any failure.

    """
    models = _get_provider_models(provider)
    if models is None:
        return []

    result = []
    for mid, entry in models.items():
        if not isinstance(entry, dict):
            continue
        if _should_hide_from_provider_catalog(provider, mid):
            continue
        if not entry.get("tool_call", False):
            continue
        if _NOISE_PATTERNS.search(mid):
            continue
        result.append(mid)
    return result



# ---------------------------------------------------------------------------
# Rich dataclass constructors — parse raw models.dev JSON into dataclasses
# ---------------------------------------------------------------------------

def _parse_model_info(model_id: str, raw: Dict[str, Any], provider_id: str) -> ModelInfo:
    """Convert a raw models.dev model entry dict into a ModelInfo dataclass."""
    limit = raw.get("limit") or {}
    if not isinstance(limit, dict):
        limit = {}

    cost = raw.get("cost") or {}
    if not isinstance(cost, dict):
        cost = {}

    modalities = raw.get("modalities") or {}
    if not isinstance(modalities, dict):
        modalities = {}

    input_mods = modalities.get("input") or []
    output_mods = modalities.get("output") or []

    ctx = limit.get("context")
    ctx_int = int(ctx) if isinstance(ctx, (int, float)) and ctx > 0 else 0
    out = limit.get("output")
    out_int = int(out) if isinstance(out, (int, float)) and out > 0 else 0
    inp = limit.get("input")
    inp_int = int(inp) if isinstance(inp, (int, float)) and inp > 0 else None

    return ModelInfo(
        id=model_id,
        name=raw.get("name", "") or model_id,
        family=raw.get("family", "") or "",
        provider_id=provider_id,
        reasoning=bool(raw.get("reasoning", False)),
        tool_call=bool(raw.get("tool_call", False)),
        attachment=bool(raw.get("attachment", False)),
        temperature=bool(raw.get("temperature", False)),
        structured_output=bool(raw.get("structured_output", False)),
        open_weights=bool(raw.get("open_weights", False)),
        input_modalities=tuple(input_mods) if isinstance(input_mods, list) else (),
        output_modalities=tuple(output_mods) if isinstance(output_mods, list) else (),
        context_window=ctx_int,
        max_output=out_int,
        max_input=inp_int,
        cost_input=float(cost.get("input", 0) or 0),
        cost_output=float(cost.get("output", 0) or 0),
        cost_cache_read=float(cost["cache_read"]) if "cache_read" in cost and cost["cache_read"] is not None else None,
        cost_cache_write=float(cost["cache_write"]) if "cache_write" in cost and cost["cache_write"] is not None else None,
        knowledge_cutoff=raw.get("knowledge", "") or "",
        release_date=raw.get("release_date", "") or "",
        status=raw.get("status", "") or "",
        interleaved=raw.get("interleaved", False),
    )


def _parse_provider_info(provider_id: str, raw: Dict[str, Any]) -> ProviderInfo:
    """Convert a raw models.dev provider entry dict into a ProviderInfo."""
    env = raw.get("env") or []
    models = raw.get("models") or {}
    return ProviderInfo(
        id=provider_id,
        name=raw.get("name", "") or provider_id,
        env=tuple(env) if isinstance(env, list) else (),
        api=raw.get("api", "") or "",
        doc=raw.get("doc", "") or "",
        model_count=len(models) if isinstance(models, dict) else 0,
    )


# ---------------------------------------------------------------------------
# Provider-level queries
# ---------------------------------------------------------------------------

def get_provider_info(provider_id: str) -> Optional[ProviderInfo]:
    """Get full provider metadata from models.dev.



    Accepts either a Hermes provider ID (e.g. "kilocode") or a models.dev

    ID (e.g. "kilo").  Returns None if the provider is not in the catalog.

    """
    # Resolve Hermes ID → models.dev ID
    mdev_id = PROVIDER_TO_MODELS_DEV.get(provider_id, provider_id)

    data = fetch_models_dev()
    raw = data.get(mdev_id)
    if not isinstance(raw, dict):
        return None

    return _parse_provider_info(mdev_id, raw)


# ---------------------------------------------------------------------------
# Model-level queries (rich ModelInfo)
# ---------------------------------------------------------------------------

def get_model_info(

    provider_id: str, model_id: str

) -> Optional[ModelInfo]:
    """Get full model metadata from models.dev.



    Accepts Hermes or models.dev provider ID.  Tries exact match then

    case-insensitive fallback.  Returns None if not found.

    """
    mdev_id = PROVIDER_TO_MODELS_DEV.get(provider_id, provider_id)

    data = fetch_models_dev()
    pdata = data.get(mdev_id)
    if not isinstance(pdata, dict):
        return None

    models = pdata.get("models", {})
    if not isinstance(models, dict):
        return None

    # Exact match
    raw = models.get(model_id)
    if isinstance(raw, dict):
        return _parse_model_info(model_id, raw, mdev_id)

    # Case-insensitive fallback
    model_lower = model_id.lower()
    for mid, mdata in models.items():
        if mid.lower() == model_lower and isinstance(mdata, dict):
            return _parse_model_info(mid, mdata, mdev_id)

    return None