feat: add new model configurations for various AI models

[teith]

#!/usr/bin/env python3.12
"""Build reviewed models.dev TOMLs for Runware from the model list.

No requests are sent to the models. Capabilities (reasoning, tool_call,
structured_output) default on; edit the exceptions. Missing required numbers
are left as TODO. Each new model is shown for approve/edit/skip.
"""
import argparse
import datetime as dt
import json
import os
import subprocess
import sys
import tempfile
from pathlib import Path

import httpx

PROVIDER = "runware"
PROVIDER_NAME = "Runware"
API_KEY_ENV = "RUNWARE_API_KEY"
DOC = "https://runware.ai/docs"
PROPRIETARY = {"openai", "anthropic", "google", "xai"}
MODALITY = {"text": "text", "image": "image", "audio": "audio", "video": "video", "pdf": "pdf", "file": "pdf"}
SCALARS = ("name", "attachment", "reasoning", "tool_call", "structured_output", "release_date", "last_updated", "open_weights")
DEFAULT_CAPS = {"reasoning": True, "tool_call": True, "structured_output": True}


def fetch_models(base: str) -> list[dict]:
    try:
        token = os.environ[API_KEY_ENV]
    except KeyError:
        sys.exit(f"{API_KEY_ENV} is not set")
    with httpx.Client(base_url=base, headers={"Authorization": f"Bearer {token}"}, timeout=30) as client:
        data = client.get("models").json()
    return data["data"] if isinstance(data, dict) and "data" in data else data


def per_million(value: str | None) -> float | None:
    try:
        rate = float(value)
    except (TypeError, ValueError):
        return None
    return round(rate * 1_000_000, 6) if rate else None


def listing_date(created: int | None) -> str | None:
    return dt.datetime.fromtimestamp(created, dt.UTC).strftime("%Y-%m-%d") if created else None


def build_spec(model: dict) -> dict:
    price = model.get("pricing") or {}
    mods = [MODALITY[m] for m in model.get("input_modalities", ["text"]) if m in MODALITY] or ["text"]
    cache = {k: per_million(price.get(src)) for k, src in (("cache_read", "input_cache_read"), ("cache_write", "input_cache_write"))}
    date = listing_date(model.get("created"))
    return {
        "name": model.get("name") or model["id"],
        "attachment": any(m in mods for m in ("image", "audio", "video", "pdf")),
        **DEFAULT_CAPS,
        "release_date": date,
        "last_updated": date,
        "open_weights": (model.get("owned_by") or "").lower() not in PROPRIETARY,
        "cost": {"input": per_million(price.get("prompt")), "output": per_million(price.get("completion")),
                 **{k: v for k, v in cache.items() if v is not None}},
        "limit": {"context": model.get("context_length"), "input": model.get("context_length"), "output": model.get("max_output_tokens")},
        "modalities": {"input": mods, "output": ["text"]},
    }


def field(key: str, value) -> str:
    if value is None:
        return f"{key} = 0  # TODO: /v1/models returned null"
    return f"{key} = {json.dumps(value, ensure_ascii=False)}"


def render(spec: dict) -> str:
    lines = [field(k, spec[k]) for k in SCALARS]
    for table in ("cost", "limit", "modalities"):
        lines.append(f"\n[{table}]")
        lines += [field(k, v) for k, v in spec[table].items()]
    return "\n".join(lines) + "\n"


def edit(text: str) -> str:
    with tempfile.NamedTemporaryFile("w+", suffix=".toml", delete=False) as fh:
        fh.write(text)
        path = Path(fh.name)
    subprocess.run([os.environ.get("EDITOR", "nano"), str(path)], check=False)
    edited = path.read_text()
    path.unlink()
    return edited


def review(model_id: str, toml: str) -> str | None:
    print(f"\n# providers/{PROVIDER}/models/{model_id}.toml\n{toml}")
    while True:
        match input("[a]pprove / [e]dit / [s]kip > ").strip().lower():
            case "a" | "y" | "":
                return toml
            case "s":
                return None
            case "e":
                toml = edit(toml)
                print(f"\n{toml}")


def main() -> None:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--repo", type=Path, default=Path("."), help="cloned models.dev repo root")
    parser.add_argument("--all", action="store_true", help="re-review models that already exist")
    args = parser.parse_args()

    base = os.environ.get("RUNWARE_BASE_URL", "https://api.runware.ai/v1").rstrip("/") + "/"
    models = fetch_models(base)

    models_dir = args.repo / "providers" / PROVIDER / "models"
    models_dir.mkdir(parents=True, exist_ok=True)
    provider_toml = models_dir.parent / "provider.toml"
    if not provider_toml.exists():
        provider_toml.write_text(
            f'name = "{PROVIDER_NAME}"\nnpm = "@ai-sdk/openai-compatible"\n'
            f'api = "{base.rstrip("/")}"\nenv = ["{API_KEY_ENV}"]\ndoc = "{DOC}"\n')

    todo = [m for m in models if args.all or not (models_dir / f"{m['id']}.toml").exists()]
    if not todo:
        sys.exit("nothing new to add")

    for model in todo:
        if (result := review(model["id"], render(build_spec(model)))) is not None:
            (models_dir / f"{model['id']}.toml").write_text(result)
            print(f"  wrote {model['id']}.toml", file=sys.stderr)


if __name__ == "__main__":
    main()

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[teith]

release_date = "2026-06-09"

[teith]

release_date = "2025-10-15"

[teith]

release_date = "2026-04-16"

[teith]

release_date = "2026-04-24"

[teith]

release_date = "2026-04-24"

[teith]

release_date = "2026-04-30"

[teith]

release_date = "2025-12-22"

[teith]

release_date = "2026-04-07"

[teith]

output = 128000

[teith]

release_date = "2026-06-01"

[jose-ares-runware]

Checked