Gemini 2.5 and the future of AI reasoning for frontend devs

Google’s new AI model, Gemini 2.5 Pro, is designed for building rich web applications. Its capabilities have helped vault Google to the top of the AI leaderboard for many frontend developers.

Gemini 2.5 Pro is Google’s “thinking model,” and it promises strong math and code capabilities. The new update puts it in contention with GPT 4.0 in terms of usefulness.

In this post, we will cover Google’s latest breakthrough with the Gemini 2.5 model, focusing on its “thinking” capabilities and what they mean for the future of frontend artificial intelligence tools.

What makes Gemini 2.5 a “thinking model”?

Gemini 2.5 Pro distinguishes itself through deep reasoning capabilities integrated into its architecture, which is a significant advancement over its predecessors. Unlike the earlier models, where step-by-step thinking might have been achieved through patient prompting, Gemini 2.5 Pro’s design inherently supports this cognitive process.

This native integration allows Gemini 2.5 Pro to effectively break down and handle more complex problems through its multi-step reasoning:

These steps can be observed in interfaces like Google AI Studio. The model appears to “think out loud,” which leads to solutions across challenging tasks such as complex coding, mathematical problems, and scientific reasoning.

While Google doesn’t explicitly publish the way Gemini 2.5 Pro achieves its reasoning, I did a little research, and tried my best to wrap my head around this.

Here’s a quick diagram to explain:

Gemini 2.5 Pro processes its information through a three-part system:

1. Ingesting various data types – Text, images, code, and video/audio through a wide input channel
2. Connecting information patterns – The core processing unit connects patterns and applies step-by-step reasoning, enhanced by extensive training
3. Outputting – It produces coherent, reasoned outputs

Now that we understand a bit more about how it works, let’s explore why you should use Gemini 2.5 Pro.

Why should you use Gemini 2.5 Pro?

Rapid adoption

The model has been able to show strong reasoning and coding capabilities across a wide range of tasks. It presently leads the WeDev Arena Leaderboard with a significant gap:

Large context window

Gemini 2.5 Pro handles vast amounts of information effectively, thanks to its large context window, tested up to around 71,000 tokens. It officially supports up to one million input tokens:

This, in turn, allows it to process an entire codebase, long documents, or even video and audio inputs 👏.

Native multimodal capabilities

Gemini Pro 2.5’s native multimodal capabilities mean it can understand and process text, images, audio, video, and PDFs, including parsing out graphs and charts, all within a single prompt.

Connection with Google’s ecosystem

Other significant features include a grounding capability that connects responses to Google Search for more up-to-date and factual answers, complete with source links. While Gemini 2.5 Pro itself focuses on text output, it integrates within Google’s ecosystem, which includes models for image (Imagen 3) and video (Veo 2) generation.

How Google is winning

So, how does Google win? Some of its advantages will be due to its access to large amounts of data, advancements in science and machine learning, and the use of powerful hardware, including custom chips.

Unlike many competitors who might specialize in model development (like OpenAI or Anthropic), data collection (like Scale AI), or hardware (like Groq or Samba Nova), Google is the only company that integrates all three:

This integration, particularly between the science and hardware teams, provides a significant strategic advantage. Google’s AI researchers can build models optimized to run efficiently on Google’s own custom chips (Tensor Processing Units, or TPUs).

This whole collaboration allows optimizations that may not be possible when targeting general-purpose hardware like NVIDIA GPUs. These GPUs have historically dominated AI training and inference due to their parallel processing capabilities. Google isn’t reliant on external chip manufacturers like Nvidia, allowing for more competitive pricing.

Google utilizes its own specialized hardware (like TPUs) to make Gemini models run faster, but way cheaper than its competitors. We have seen their Gemini Flash model demonstrate this with impressive speed, at reportedly 25x lower token costs.

This hardware advantage, combined with Google’s large data resources and self-funded research, allows them to offer competitive AI primarily through cloud services and their improved AI Studio interface.

How to use Gemini 2.5 Pro effectively

Getting started

Gemini 2.5 Pro’s advanced reasoning and large context window (1M tokens) could significantly impact various fields. These capabilities can be accessed through multiple platforms (Google AI Studio, Vertex AI, Gemini app/web, or integrated Google products):

Google’s AI Studio provides a web-based platform for experimenting with Google’s AI models.

The interface above is divided into a navigation panel on the left for selecting tools like Chat, Stream, or Video Gen, and accessing history or starter apps.

The central area is the main workspace, currently showing a Chat Prompt interface where users can input text, receive AI-generated responses, and use example prompts. The top bar provides access to API keys, documentation, and account settings.

On the right, a Run settings panel allows users to configure the AI’s behavior. This includes selecting the specific AI model (e.g., “Gemini 2.5 Pro Preview”), adjusting parameters like Temperature to control creativity, and managing Tools, such as structured output, code execution, function calling, and grounding with Google Search. This comprehensive setup enables developers and users to explore AI models directly in their browser.

With all these nice features, how do we utilize this in our codebase? Let’s check it out.

Analyzing large codebases

This can easily be done by using gitingest to accomplish everything if you wish. You can tell Gemini 2.5 Pro to extract a particular logic or rewrite the entire code base using a different framework. This will particularly come in handy for frontend developers as it bridges the gap of doing something repeatedly when it can be done in one shot.

Creating 3D games

Gemini offers real precision in making 3D games. These results are overwhelming. I did try one out using this prompt:

“Create a dreamy low-poly flight game scene. Cameras should follow behind with dynamic lighting and gentle animations. Add controls to make it faster. This flight game should be controlled by me, and it should be able to skip bricks and buildings, in a single HTML file.”

To be honest, the game didn’t work out in the first prompt. But with a little effort, I was able to fix it. Check out the game here:

See the Pen
Gemini 2.5 Flight Game by Emmanuel Odioko (@Emmanuel-Odioko)
on CodePen.

Building simple web apps

I also wanted to test Gemini’s performance in creating simple web apps. I gave it a one-sentence prompt:

“In one HTML file, recreate Facebook’s home page on desktop. Look up Facebook to see what it looks like recently.”

Here is the result:

See the Pen
Facebook Gemini 2.5 Examples by Emmanuel Odioko (@Emmanuel-Odioko)
on CodePen.

I did the same with X:

“In one HTML file, recreate the X home page on desktop. Look up X to see what it recently looks like, put in real images everywhere an image is needed, and add a toggle functionality for themes.”

It had a more difficult time doing this, but we arrived here at last:

See the Pen
X generated Gemini 2.5 by Emmanuel Odioko (@Emmanuel-Odioko)
on CodePen.

Dark theme looked like this:

And light theme:

Not bad for a free tool, right?

I went ahead and tried LinkedIn. Here is the result:

See the Pen
LinkedIn Generated By Gemini 2.5 by Emmanuel Odioko (@Emmanuel-Odioko)
on CodePen.

Best practices for Gemini 2.5 Pro

Something to note: To draw the very best from Gemini 2.5 Pro, be very distinct with your prompt. Explaining what you want in great detail will help you get to the end result quicker.

Comparing Gemini 2.5 Pro with other AI models

Gemini 2.5 Pro stands tall as of today as the best web development model out there. It’s going head-to-head with other leading companies like OpenAI, Microsoft, Anthropic, and others. Below are the comparison data according to artificialanalysis.ai :

Speed comparison

Provider	Model	Output Speed (Tokens/s)
Google	Gemini 2.5 pro	147
OpenAI	GPT-4o	142
xAI	Grok 3	95
DeepSeek	R1	23

Coding and math performance comparison

Provider	Model	Math (GSM8K / MATH)	Coding
Google	Gemini 2.0 Pro	67	55
OpenAI	GPT-4o	70	63
Anthropic	Claude 3.5 Sonnet	57	49
xAI	Grok 3	67	55
DeepSeek	R1	60	44

Pricing comparison

Provider	Model	Input Price ($/M) \$	Output Price (/M)
Google	Gemini 2.0 Flash	0.35	0.35
Google	Gemini 2.0 Pro	1.50	1.50
OpenAI	GPT-4o	5.00	15.00
Anthropic	Claude 3.5 Sonnet	3.00	15.00
xAI	Grok 3	2.00	2.00
DeepSeek	R1	0.30	0.30

Benchmarks can be deceiving, and you should only trust them to a point. When it comes to agentic coding, Claude 3.7 is up there. But we now have Gemini 2.5 as a strong competitor, and yes, it does have an edge as of today.

Its API’s are cheaper, and it has a much larger token context window. Claude will not be able to generate the 2D flight game above in one shot – not even in two, to be honest – because of its low token context.

Conclusion

One million tokens seems like enough, but the Google team has promised a two-million-token context window, which should be enough for many codebases. In this article, we were able to look at what makes Gemin 2.5 different, its use cases, and how to get the best when prompting. Lastly, we saw its ability to spin up different demo projects in seconds.

Hope you found this exploration helpful. Happy coding!