Preface: Leveraging its prowess in model scale and cost-efficiency, Google has ascended to the forefront of the large-scale AI model competition, igniting a fresh wave of rivalry in the AI coding market.

Author | Fang Wensan | Image Source | Network

Google's DeepMind research division has recently released the Gemini 2.5 Pro Preview (I/O version), the latest iteration of the Gemini 2.5 Pro multimodal large language model introduced earlier this year.

Since the generative AI boom ignited by ChatGPT in late 2022, Google has taken the lead in key code generation evaluation metrics, surpassing all competitors for the first time.

Currently, the model reigns supreme on the LMArena coding leaderboard and also tops the WebDev Arena leaderboard, particularly excelling in the development of interactive web applications.

The new version, labeled [Gemini-2.5-pro-preview-05-06], replaces the previous 03-25 iteration.

A notable aspect of Google's update is its ability to empower users to create complete, interactive web applications or simulations with a single prompt, aligning with DeepMind's ambition to streamline the prototype design and development process.

Google asserts that users can input visual patterns or thematic prompts, which are instantly converted into executable code, significantly lowering the entry barrier for design-oriented developers or innovative teams.

While Google has yet to disclose the underlying architecture and technical specifics of the new Gemini 2.5 Pro, its practical application effects indicate a steadfast commitment to providing a more efficient and intuitive development experience.

With its prowess in code generation and multimodal input, Gemini 2.5 Pro has transcended its status as a mere [research model] within a technology lab, evolving into a practical tool for addressing real-world development challenges.

This early release underscores DeepMind's intention to preemptively respond to market demands and maintain its technological lead ahead of the I/O conference.

Google has introduced a novel application method for the model's new version in visual AI code generation, enabling the creation of complete, interactive web applications or simulations with a single prompt.

For instance, in the VideoMME video benchmark test, Gemini 2.5 Pro scored an impressive 84.8%. This amalgamation of abilities and coding technology facilitates a new workflow, where manual sketches can be seamlessly translated into corresponding program functions, a feat unattainable by previous versions.

Gemini 2.5 Pro has undergone significant optimizations tailored for front-end web development.

Historically, developers were required to manually review design files and inspect individual components to match style attributes such as colors, fonts, padding, margins, and borders, before painstakingly writing CSS code to replicate these visual attributes.

Now, utilizing Gemini 2.5 Pro within an Integrated Development Environment (IDE) simplifies the process of generating new functional programs, such as adding a video player function akin to the Gemini 95 entry-level application.

One of the most eye-catching new features is the [Video Learning Application], showcased in Google AI Studio, enabling the creation of interactive learning applications from a single YouTube video.

The ability to comprehend video content and generate learning applications with a comprehensive UI stands to revolutionize the toolkit of educational content creators.

By amplifying its code generation and multimodal input strengths, Gemini 2.5 Pro is transitioning from a research innovation to a productivity tool that addresses real-world programming challenges.

Remarkable Performance Enhancements Across Multiple Platforms

On the WebDev Arena leaderboard, a third-party platform, Gemini 2.5 Pro Preview (05-06) received the highest human-reviewed score for crafting aesthetically pleasing and practical web applications, surpassing Anthropic's Claude 3.7 Sonnet to claim the top spot.

Google's new model scored 1499.95, markedly higher than Sonnet 3.7's score of 1377.10.

The previous version of Gemini 2.5 Pro (03-25) ranked third with a score of 1278.96, indicating a notable 221-point improvement in the I/O version.

The cornerstone of this upgrade lies in its programming prowess, ranking first on the LMArena programming leaderboard while also outpacing the former leader, Claude 3.7 Sonnet, by a substantial margin on the WebDev Arena leaderboard.

Particularly in the WebDev Arena rankings, it is the first model to surpass 1400 points, marking a 147-point increase compared to the previous version of Gemini 2.5 Pro.

Demis Hassabis, CEO of DeepMind, heralded it as the [strongest programming model in history] and announced that Gemini 2.5 Pro (I/O) is now accessible via Gemini APP, Vertex AI, and Google AI Studio, particularly excelling in the development of interactive web applications.

Programming Shifts from [Syntactic Correctness] to [Intentional Expressiveness]

With the proliferation of AI technology, many future job roles may rely heavily on AI tools, especially for developers, where an efficient AI programming tool can significantly amplify project efficiency.

In practical enterprise applications, the code generated by the model can be directly utilized in production environments, with a marked reduction in tool invocation failures.

This not only accelerates development speed but also lowers the trial-and-error costs for enterprises.

This paradigm shift not only signifies an increase in efficiency but also allows developers to focus more on validating ideas rather than delving into technical implementation details.

Historically, programming was perceived as a [craft] mastered by professionals, requiring a meticulous grasp of syntactic rules.

However, current models are now more attuned to users' genuine needs, enabling even non-programmers to articulate their desired applications using natural language.

For example, an ordinary user aiming to create an urban traffic simulator may have previously required a professional development team, taking months to complete.

Now, users can simply articulate their needs to the model in plain language, and the model can progressively build complex applications.

This shift transforms programming from a complex technical activity to one that allows more people to participate in application development, fostering greater creativity.

In the software development process, the design and optimization of backend routing systems are exceptionally complex tasks, necessitating developers to possess extensive experience and professional knowledge.

However, this model can now offer robust support in system architecture and decision-making, akin to seasoned developers.

It transcends mere code generation, collaborating with developers to analyze and solve problems, enabling more efficient collaboration.

The profound revelation from Gemini 2.5 Pro is that when AI addresses the [how] of problem-solving, human creativity can finally break free from the shackles of technical implementation.

Designers are no longer mired in pixel alignment, engineers are liberated from syntactic debugging, and everyone can concentrate on what truly matters: crafting an exceptional user experience.

When implementing ideas becomes this straightforward, the ability to [ask good questions] becomes particularly invaluable.

The core competitiveness of the future may hinge on who is better at defining problems rather than merely solving them.

Conclusion: AI Code Tools Become the Battlefield for Major Enterprises

According to market survey analysis by Verified Market Research, the global AI code tools market is projected to reach $4.91 billion by 2024;

This figure is anticipated to skyrocket to $30.1 billion by 2032, with a compound annual growth rate of approximately 27.1% from 2025 to 2032.

GitHub's report reveals that over 1.5 million developers have adopted GitHub Copilot, with 46% of the code it generates for supported languages;

Moreover, developers leveraging AI coding assistance complete pull requests 15% faster than those without AI assistance.

The AI code tools market is gradually diversifying into various domains, including web development, mobile app development, game development, enterprise applications, and data science and analysis.

Currently, North America dominates the global AI code tools market, benefiting from a large pool of software developers and numerous top AI experts in the region, coupled with its leading position in the field of large models.

The Asia-Pacific region is the fastest-growing area for AI code tool applications globally, accounting for 42.6% of the total number of developers worldwide, with approximately 12.7 million active developers, including about 7.6 million developers from China and India combined.

In the domestic market, numerous large enterprises and emerging unicorn companies are actively vying for market share.

Examples include Tencent Cloud's AI code assistant CodeBuddy, Alibaba's Tongyi Lingma, Baidu's Wenxin Kuaima Comate, Huawei's CodeArts Snap, ByteDance's Trae, iFlyTek's iFlyCode, and Zhipu AI's CodeGeeX, among others, engaging in fierce competition.

However, domestic AI code assistants, lacking the support of top-tier large models, still face significant hurdles in establishing competitiveness in the international market.

References: Head Technology: "Google's New Model Surpasses Claude 3.7 Sonnet, OpenAI Invests $3 Billion in Layout", AI Tool Navigation Station: "First-hand Test of New Gemini 2.5 Pro, Programming Ability Outshines Claude 3.7, Tops the List", Suanjia Cloud: "Google Upgrades Gemini 2.5 Pro, Dominates Programming Rankings, Far Exceeds Claude 3.7 Sonnet"