The deep integration of general large models and financial data has taken a key step forward.
On April 7th, the official WeChat account of Qianwen App announced that its &#34;Deep Research&#34; professional capability has been upgraded, adding modules such as financial analysis, and officially connecting to real-time quotes for 13,000 stocks and financial report data for approximately one million listed companies. Almost simultaneously, Kimi also announced its integration with the ROYALFLUSH INFO iFinD financial database.
<img src="https://imageproxy.pbkrs.com/http://img.zhitongcaijing.com/images/contentformat/25dfe22a4aa5a0b11570aca112240e5c.jpg?x-oss-process=image/auto-orient,1/interlace,1/resize,w_1440,h_1440/quality,q_95/format,jpg" alt="" original-src="https://imageproxy.pbkrs.com/http://img.zhitongcaijing.com/images/contentformat/25dfe22a4aa5a0b11570aca112240e5c.jpg"/>
Undeniably, the integration of market data into general large models marks another milestone in AI-driven &#34;equalization&#34; of investment research. From exclusive financial data terminals for institutions to the almost no-code OpenClaw intelligent body, and now to Qianwen and Kimi on everyone's mobile phones, the cost of accessing financial data is approaching zero, and the barriers to analysis are being lowered at an unprecedented speed.
In the face of the &#34;cross-border&#34; nature of general large models, broker AI and professional financial data platform AI are encountering new opportunities and challenges. Industry insiders believe that general large models will not replace broker AI; instead, the landscape will become clearer, with general large models responsible for breadth and convenience, while broker AI focuses more on depth and closed loops. This will also bring opportunities for brokers, allowing them to integrate their deeper and more compliant AI investment advisory capabilities into the general large model ecosystem in the form of plugins or intelligent bodies.
General Large Models Complete Financial Evolution
In the past, ordinary users wanting to analyze stocks with large models often had to write prompts themselves, call external data interfaces through plugins, or, like tech enthusiasts, deploy OpenClaw locally and configure financial skills. The entire process often required a certain level of technical knowledge.
This time, the general large models Qianwen and Kimi directly connect to stock market data. In the upgraded &#34;Deep Research&#34; mode of Qianwen, users can directly inquire about the real-time quotes and historical trends of a specific stock or request a financial health analysis based on the latest financial reports. The model will automatically call the backend stock data and financial report database to provide data-supported answers.
Similarly, after Kimi integrated with ROYALFLUSH INFO iFinD, its financial information retrieval and reasoning capabilities have been enhanced by the professional database, eliminating the need for users to switch between different software repeatedly.
The essence of this change is transforming financial data from &#34;external&#34; to &#34;embedded.&#34; General large models are no longer just text generators that can chat; they have become &#34;investment research assistants&#34; with real-time stock data perception capabilities. For billions of ordinary investors, this means they can obtain stock research and market analysis in the most everyday conversational manner, which in the past often required paying for terminal data from Wind, Choice, etc., before conducting analysis and research
The &#34;Second Collapse&#34; of Investment Research Thresholds
At the beginning of the year, the explosive popularity of open-source frameworks like OpenClaw allowed technically skilled investors to create their own &#34;handcrafted&#34; analysis assistants. Recently, the native integration of market data into general large models has brought AI investment research capabilities directly to the fingertips of every mobile user.
These two paths form an interesting complement. Personal AI assistants built using OpenClaw, for example, offer advantages in high customization, localized data, and the ability to integrate heterogeneous information sources like social media, making them suitable for advanced users with higher privacy and flexibility requirements. In contrast, general large models like Qianwen and Kimi take a &#34;ready-to-use&#34; approach for the masses, allowing users to ask about stocks simply by opening the app without any deployment or API key configuration.
&#34;It's like the difference between a professional camera and a smartphone,&#34; a respondent metaphorically explained to reporters. &#34;OpenClaw is like a DSLR, powerful but requiring a certain technical threshold; while Qianwen and Kimi, after integrating stock market data, are like smartphone computational photography, allowing ordinary people to get good results with just a snap. The common result is that more people are starting to 'take photos.'&#34;
This trend's acceleration can also be seen in the data. According to reports, after the Spring Festival, the domestic financial data platform Tushare saw a significant increase in new users and API call volumes, with a peak of nearly 4,000 new users in a single day. As general large models like Qianwen and Kimi directly embed market data, the reach of financial data will expand to hundreds of millions of ordinary retail investors. This magnitude of leap could lead to profound changes in the dissemination of information and decision-making patterns in the capital markets.
Are Brokerage AI and General Large Models Competing or Replacing Each Other?
What do brokerages that have already laid out AI investment advisory services think about the &#34;cross-industry&#34; emergence of general large models?
The reporter found that applications from brokerages such as Huatai Securities' AI Zhangle, Guotai Junan's Lingxi APP, and GF Securities' Yitaojin have completed AI native transformations in recent years, providing not only intelligent stock diagnosis and financial report interpretation but also closing the trading loop. The core barriers for these brokerage AIs lie in licensed compliance, vertical large models, and professionally researched content that has undergone compliance review.
The advantage of general large models is their generalization capability and user base. Qianwen and Kimi themselves have tens of millions or even hundreds of millions of active users, and after integrating market data, they can provide basic investment research services to a massive user base at a very low marginal cost.
For brokerages, this presents both a challenge and an opportunity. Some paid AI advisory functions may be diverted by general large models, but brokerages can also integrate their deeper and more compliant AI advisory capabilities into the general large model ecosystem in the form of plugins or intelligent agents.
&#34;I don't believe general large models will replace brokerage AIs,&#34; said a brokerage analyst. &#34;In the future, general large models will handle breadth and convenience, while brokerage AIs will focus more on depth and closure.&#34;
In fact, financial data providers are also actively embracing this trend. Wind Information has launched WindClaw, Tonghuashun's iFinD has introduced the MCP financial data service, and Choice has launched Miaoxiang Claw
These professional tools not only serve institutions but are also beginning to penetrate individual users through more user-friendly interaction methods. The boundaries between general large models and professional financial data platforms are becoming blurred

300033

The integration of the General Large Model Qianwen and Kimi with stock market data and financial databases marks the democratization process of AI in the investment research field. After the upgrade of Qianwen's "Deep Research" model, users can directly query real-time stock prices and financial report analyses, lowering the analysis threshold. Kimi has also enhanced its financial information retrieval capabilities through ROYALFLUSH INFO iFinD. This change brings opportunities for brokerage AI, and in the future, a collaborative development between general large models and brokerage AI will emerge

General large models directly access stock market data, a new competitive signal is here?