- 每月造訪次數
- 6.8K
- 下降40.6%較上月2026 年 9 月
- 公開營收
- 未公開
- 暫無公開營收證據
- 入門價
- 免費
- 沒有月付訂閱價
- 首次發布
- 16 個月前
- 2025-07-15 · 估算
訪問
行銷動作
發布、搜尋與內容、引薦、促銷X 帳號@brucewok88
第三方評測與案例拆解
客戶來源管道拆解、工程師評測與開源檔案Fazier
2025-07-15查看原文
- 核心亮點評測：
- And now, it is within your reach. Today, we are open-sourcing: Kimi-K2-Base: The foundation model, a strong start for researchers and builders who want full control for fine-tuning and custom solutions. Kimi-K2-Instruct: The post-trained model best for drop-in, general-purpose chat and agentic experiences. It is a reflex-grade model without long thinking. With Kimi K2, advanced agentic intelligence is more open and accessible than ever. We can't wait to see what you build.
- 適用人群：
- Give Kimi K2 your tools and describe your task. It automatically understands how to use the tools and gets the job done. You don't have to write any complex workflow for agentic applications. 1.Salary Data Analysis: Imagine using Kimi K2 to explore remote-work salaries with the Salary Data Analysis example, where 16 IPython calls generate stats, visualizations, and an interactive webpage of insights. Dive into the Stanford NLP Genealogy, and get Kimi K2 to generate an interactive site built through 5 web searches, 4 browsings, 3 clicks, 5 scrolls, 6 edits, and 2 deployments. Or plan your dream Coldplay Tour 2025 in London with Kimi K2, it crafted the plan for you through 17 seamless tool calls spanning search, calendar, Gmail, flights, Airbnb, and restaurant bookings. 2.JavaScript Minecraft: For clarity, the terminal displays only a summary of each trajectory. In every example, Kimi K2 orchestrates multiple tools and commands behind the scenes to accomplish complex objectives. For instance, Kimi K2 can automate Minecraft development in JavaScript: it manages rendering, runs and debugs test cases, captures logs on failure, and iteratively improves the code until all tests succeed. For pre/post norm analysis, Kimi K2 uses the Weights & Biases (wandb) data reader to extract insights from language model experiments and generates a polished analysis report. When converting a Flask project to Rust, Kimi K2 systematically refactors the codebase and runs performance benchmarks to ensure robust results.
開源倉庫與自部署
GitHub 開源與智慧體接入
流量來源
2026 年 9 月
訪問構成
管道
- 直接訪問40%
- 自然搜尋33%
- 外部引薦10%
- 社交媒體6%
- 其他11%
國家/地區
- 美國28%
- 俄羅斯28%
- 墨西哥25%
- 印度18%
搜尋
站點權重 —網域名稱註冊 2026-09-26
| 關鍵字 | 月搜尋量 |
|---|---|
| kimi k2 | 18.1K |
| kimi chat | 6,050 |
| kimi login | 4,500 |
| kimik | 1,920 |
| kimik2 | 1,850 |
流量資料：Vaneform
定價
公開價格：Free來源資料原文，方案結構在官網快照後識別。
頁面與變化
開始記錄於 2026-09-14
關鍵頁面
每週快照還沒有頁面快照。有人追蹤後，首頁與定價頁開始每週快照。
變化
開始記錄以來，定價、方案與定位都沒有變化。
問題與證據
- Kimi K2 的網站造訪次數是多少？
- Vaneform 估算 2026-09 網站造訪次數為 6.8K。造訪次數包含重複訪問，付費客戶數與收入需分別查看對應證據。來源
同分類研究對象
AI 模型與智慧體 / 模型 API 與網關
這些產品屬於同一目錄分類。選型時請進一步核對客戶任務、功能與部署要求。