AI Wave

AI model releases, price changes and deprecations in one feed, merged hourly from vendor release notes, Hugging Face, the OpenRouter catalog, GitHub and the LiteLLM price map. Same model found by two sources becomes one entry, and each is marked official (from the vendor) or pending review (inferred from a catalog). Not only chat models: embedding, rerank, transcription, speech and video are included, each priced in its own unit (per second, per character) rather than assumed to be tokens. Use it to ask what changed since you last checked, what a model costs per month, how two models compare, or what to switch to when one disappears. Collection is hourly, not realtime. Deprecation is inferred from a model staying absent from the catalog for 24 hours, not from a vendor announcement. Free tier: 1,000 calls/day.

该来源不提供完整文件导出（国内平台多为平台内托管），仅存元数据与原链

接入信息

传输形态: http
鉴权方式: 鉴权未知
端点: https://ai-wave--ghej0607.run.tools

鉴权方式未标注，请核对官方文档后再接入——不要直接使用以下片段

{
  "mcpServers": {
    "AI Wave": {
      "url": "https://ai-wave--ghej0607.run.tools"
    }
  }
}

能力清单

工具	说明
list_model_changes	Changes across AI models — new releases, price changes, API changes and deprecations. Normalized from vendor release notes, Hugging Face, the OpenRouter catalog and GitHub, deduplicated per model, each marked official or pending review. Poll with `since` (unix seconds) and feed the returned `latest` back next time.
get_model	Pricing, context length and catalog status for one model. Accepts an OpenRouter id (anthropic/claude-opus-5) or a Hugging Face repo id.
get_price_history	Price history for one model — when the price changed and to what. The public catalog only exposes the current price, so this answers 'was this cheaper last month?'. Each entry holds until the next one. History starts when AI Wave began recording, not when the model launched.
search_models	Shortlist models by budget, context and capability, ranked by measured performance. Use this to answer 'which model should I use for X' — it returns benchmark scores alongside price so the trade-off is visible in one call. Sort by a subject (math, coding, science, reading) to find a model good at one thing.
compare_models	Put two or more models side by side — price, context, benchmark scores per subject, and what each costs per month at a given volume. Use this instead of calling get_model repeatedly: it aligns the fields and marks which subjects a model has not been tested on, so a missing score is not read as a low one.
estimate_cost	What one model costs per month at a given token volume, in USD and KRW. Token prices are per million and hard to reason about directly; this turns them into a monthly bill. Input and output are mixed 75/25 unless you pass your own split.
get_today	Today in one call: the top 5 models, which ones climbed, whose pricing changed in the last 24h, and what was newly listed in the last 7 days. Use this instead of paging list_model_changes and re-deriving the summary — price changes are already collapsed to one per model, with the raw count kept.
find_replacement	What to switch to when a model is gone or you need a fallback. Ranked by closeness in measured performance, not by vendor or price — what you usually need to preserve first is the quality of the output. Candidates whose context window is less than half the original are excluded. Returns the score, price and context deltas so you can judge; we do not pick for you.

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