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正面對決基準比較
DeepSeek vs TypeSafe AI - Jev
DeepSeek 每月有 372,761,591 次造訪，月增率 +4.3%，定價模式為免費增值。TypeSafe AI - Jev 每月有 6,148,688 次造訪，成長率 -36.4%，定價模式為付費。
由 DeepSeek-V4 與 R1 驅動的高效開源權重與託管推理 AI，token 成本極低。
每月造訪次數
3.73億
月成長速度
+4.3%
主要市場
🇨🇳 中國
DeepSeek 每月有 372,761,591 次造訪，月增率 +4.3%，定價模式為免費增值。TypeSafe AI - Jev 每月有 6,148,688 次造訪，成長率 -36.4%，定價模式為付費。
由 DeepSeek-V4 與 R1 驅動的高效開源權重與託管推理 AI，token 成本極低。
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 | deepseek.com | typesafe.ai |
| 定價模式 | 免費增值 | 付費 |
| 每月造訪次數 | 3.73億 (372,761,591) | 614.87萬 (6,148,688) |
| 月流量變化量 | +1536.22萬 (+4.3%) | -351.3萬 (-36.4%) |
| 主要市場與市佔 | 🇨🇳 中國 (50.5%) | 🇺🇸 美國 (26.4%) |
| 每月直接造訪 | 2.92億 | 314.11萬 |
| 使用者評分 | 4.8 | 4.2 |
| 支援平台 | 網站 | 網站 |
由 DeepSeek-V4 與 R1 驅動的高效開源權重與託管推理 AI，token 成本極低。
依託稀疏混合專家架構（V4-Pro 為 1.6T 中的 49B 啟用參數），以極低延遲與記憶體開銷處理最多 100 萬 token。
由 DeepSeek-R1 開創並融入 V3.2 與 V4 思考模式，對複雜數學、邏輯與程式碼除錯展示逐步自驗證過程。
快取命中時，輸入 token 成本降至 Flash 的每百萬 0.003 美元與 V4-Pro 的每百萬 0.022 美元，離峰 UTC 時段費率再低 50%。
在 chat.deepseek.com 及 iOS/Android 上免費提供文件上傳、推理模式與網頁搜尋，核心模型無須付費牆。
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.
提供巨大的上下文視窗與深度的 Google 生態系整合，但對於純推理任務感覺較不夠專精。
提供巨大的上下文視窗與深度的 Google 生態系整合，但對於純推理任務感覺較不夠專精。
Google 的快速原型開發 IDE 與開發者 API 工作台，用於建構以 Gemini 模型為基礎的多模態應用程式。
免費 / 開源提供同等級的開源權重基礎模型與多語言效能，但需要自行架構主機或第三方推論設定。
免費 / 開源Enables running open-source models locally on consumer hardware, but lacks DeepSeek's cloud-scale hosted reasoning power.
免費 / 開源Enables running open-source models locally on consumer hardware, but lacks DeepSeek's cloud-scale hosted reasoning power.
