OpenAI 平台每月有 234,678,639 次造訪，月增率 -9.6%，定價模式為付費。TypeSafe AI - Jev 每月有 6,148,688 次造訪，成長率 -36.4%，定價模式為付費。
用於部署 GPT-6 Astra、Sol、Luna 以及即時多模態代理的企業級開發者平台與 API。
TypeSafe AI is an AI lab building machine-native intelligence infrastructure, and Jev is its first System One Model — a model designed to make decisions inside software rather than reply to humans. Instead of returning strings, Jev accepts structured questions and returns typed decisions with calibrated confidence scores, so applications can act autonomously when confidence is high and escalate for human review when it is not. TypeSafe reports roughly 193x faster and 444x cheaper performance on System One workloads compared with general-purpose LLMs.
| 面向／指標 | | |
|---|---|---|
| 官方網域 | openai.com | typesafe.ai |
| 定價模式 | 付費 | 付費 |
| 每月造訪次數 | 2.35億 (234,678,639) | 614.87萬 (6,148,688) |
| 月流量變化量 | -2478.28萬 (-9.6%) | -351.3萬 (-36.4%) |
| 主要市場與市佔 | 🇺🇸 美國 (25.4%) | 🇺🇸 美國 (26.4%) |
| 每月直接造訪 | 1.05億 | 314.11萬 |
| 使用者評分 | 4.8 | 4.2 |
| 支援平台 | 網站 | 網站 |
用於部署 GPT-6 Astra、Sol、Luna 以及即時多模態代理的企業級開發者平台與 API。
涵蓋 GPT-6 Astra（每百萬 token 10/50 美元）、GPT-6.1 Sol（2/10 美元）、GPT-6.1 Terra（1/5 美元）與 GPT-6 Luna（0.10/0.50 美元），全部具備 1,050,000 token 上下文視窗與最高 128K 輸出。
依每次 API 請求在六個細緻層級（`none`、`low`、`medium`、`high`、`xhigh`、`max`）調節思考 token 預算，以平衡延遲、準確性與成本。
在單次有狀態 API 呼叫中，把模型推理與代管網頁搜尋、檔案搜尋、函式呼叫以及自主的圖形介面電腦操作結合起來。
建置超低延遲的多模態語音代理，按秒計費（每分鐘 0.05 美元），無需向上取整到整分鐘。
TypeSafe AI is an AI lab building machine-native intelligence infrastructure, and Jev is its first System One Model — a model designed to make decisions inside software rather than reply to humans. Instead of returning strings, Jev accepts structured questions and returns typed decisions with calibrated confidence scores, so applications can act autonomously when confidence is high and escalate for human review when it is not. TypeSafe reports roughly 193x faster and 444x cheaper performance on System One workloads compared with general-purpose LLMs.
Jev outputs typed decisions that application code can act on directly, rather than prose that must be parsed and interpreted.
Every decision carries an estimate of how confident the model is, so you can set thresholds for autonomous action versus human escalation.
TypeSafe reports Jev at roughly 193x faster and 444x cheaper than LLM-based workflows on comparable System One tasks, with a price of $42 per billion input tokens.
Because confidence estimates expose uncertainty, Jev is designed to avoid hallucination-style failures in automation, enabling zero-hallucination decision paths where confidence is high.
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