Claude 4 vs Gemini 2.5 Pro! What's Better?

Recently with the launch of Claude 4 Opus and Claude 4 Sonnet, there has been a rise of confusion as to which coding model is the best. Both of these models are exceptional at elite coding and structured reasoning. On the Swaybench verified test, you can see both of these models lead in this category versus many of these other models including Gemini 2. 5 Pro. And the same manner in terms of its benchmarks, it is excelling in various sorts of categories from engetic uh terminal coding to a gentic coding all the way to many of these other categories like agentic tool use versus the state-of-the-art models. Now, the confusion comes in when you factor in the Gemini 2. 5 Pro preview, the one model that has been designed for coding purposes. Now, you may be confused cuz you might want to use the Gemini 2. 5 Pro model due to its pricing as well as its context window which is up to 1 million tokens as well as its multimodal understanding. But in terms of having aic coding capabilities, it is a bit underwhelming in terms of the output that you get versus would get from the Cloud 4 models. This is why today we're going to be comparing the two models and showcasing which model would be best for a particular use case. So let's first start off with the first category which is coding and debugging. The cloud for models actually dominate in structured code generation, building from scratch and agent-like workflows. It's great for building intelligent assistants and agents. Now, in the same category of coding and debugging, the Gemini 2. 5 Pro is definitely something that shines in its capabilities of debugging due to its longer context, but having the same agility as the Cloud 4 models is something that it lacks its quality on. So, this is where I'm going to send in this exact same prompt to both of the models. We're testing the Gemini 2. 5 Pro preview, the 0506 one, which is the latest version. And in this case, this is a task of building a local guey based task manager with no backend, no API, and just a clean modular code cuz I'm wanted to demonstrate how well the model is in terms of generating a basic app structure. Before we get started, I just want to mention that you should definitely go ahead and subscribe to the world of AI newsletter. I'm constantly posting different newsletters on a weekly basis. So this is where you can easily get up-to-date knowledge about what is happening in the AI space. So definitely go ahead and subscribe as this is completely for free. There we go. Quite quickly we had the task manager app fully generated and in this case it is a functional Python app that it was capable of generating. And this is where it is something that showcases why the Gemini 2. 5 Pro might shine for you cuz it is something that will iteratively work on improving the code. This is just a simple guey that I had requested and it was able to do it quite quickly. Whereas the sonnet model might run into a couple of rate limits in terms of generating something basic which is a problem many models a part of the cloud series tend to face due to its rate limits. But in this case, you can see that if you're asking to generate something basic and rapidly, you can use the Gemini 2. 5 Pro as it's a cheap option that will get the job done as it is still a powerful model that's still pretty capable. Now, if I am to give the Cloud Opus the same prompt, it is likely going to perform better overall due to its strengths in structured reasoning as well as clean code generation and building applications from scratch. The claude is a model that excels at producing logically organized, maintainable code with clear separation of concepts and concerns. Ideal for guies as well as different sorts of code that require thoughtful architecture and flow. It's something that will handle all these steps that requested. You can see that there's multiple steps, which is something that it is capable of handling, but no doubt it is definitely way more expensive than using the Gemini 2. 5 Pro. So, it looks like the Claude model has finished generating this Python app. And one thing I can notice right away, it added a lot more extra things that I didn't really ask for, like a readme file, whereas the Gemini model just generated the Python file. So, let's now go ahead and open up our app to see how it looks. And there we go. This is our task manager app and it has functional buttons which is awesome. You have the ability to edit tasks if there was any sort of task delete and you have all the components that we had requested added which is nice. So this is a basic Python guey of a task management app and I would say both of these models did a great job. Now you can see that it spent approximately $3 to actually create that. Whereas the Gemini model in the other generation, it is something that I think spent

90s. Oh, it spent 15 cents. So you can see there's a huge difference in terms of the pricing when you're generating these basic tasks. Yeah, the cloud model might have generated this Python GUI a bit better in terms of the functionality, but the Gemini 2. 5 Pro did a great job in prototyping and generating the base structure of it while being super cheap in comparison to the Cloud model. Now, in terms of UI as well as UX designing, both of these models are exceptional at this. But what we're going to do is compare the Sonnet this time against the Gemini 2. 5 Pro. and we're trying to see how well these two models are in terms of generating the SAS landing page. So, it looks like the Gemini 2. 5 Pro has generated the landing page first, but let's see what the Sonnet is capable of generating. So, now let's compare the two. This is the SAS landing page that was generated by the Gemini 2. 5 Pro. It looks exceptional and it did a pretty great job in creating the base structure. We have a pricing plan and it was capable of adding animations. Now, this is the generation we've gotten from the Claude model. Now, it did a pretty good job as well. It added a landing animation, which we didn't see with the actual Gemini model. In the main landing page, there's no animation, but you can see it did a decent job in terms of generating the base structure. There's animations with the numbers and it did a decent job in generating overall structure of the SAS landing page. Both models did a great job and this is why I say both of these models do a pretty decent job in terms of coding out different I would say structured outputs of UX and UI designs. But if you are to compare the Opus, the Opus model does a way better job in terms of UI or UX designs. This is the landing page that the Opus model was actually capable of generating. You can see it is far superior than the generation that we saw from the Gemini 2. 5 Pro as well as the Claw for Sonnet. And this is why I say the Opus is a superior model in terms of any sort of coding task whether that's complex UI UX design or if it is something related to structured or complex coding task. Next up, we're asking the model to create a Tetris game. This is where we're testing the model's ability to handle and render 3D uh logic as well as real-time game mechanics and efficient in browser JavaScript coding. So, let's see which model does a better job in this task. And there we go. We finally have both the models outputting the Tetris game. And both of them did a pretty decent job. This is the generation I've gotten from the Gemini 2. 5 Pro. And clawed opus model. So you can see in this case it did a good job in creating the 3D blocks. And in this case I guess the functionality is a bit more stable and looks a little bit more appealing than the Opus model. But that's just my two cents. Both of the models did a great job in terms of executing this task. In conclusion, both of these models are exceptional. But in terms of advanced code generation, structured reasoning and agents as well as enterprise precision and reliability, I would choose the cloud for models over the Gemini 2. 5 Pro. But when you're working on debugging or refactoring large code bases, you would want to use Gemini 2. 5 Pro due to its large context as well as working with video and audio inputs. Due to its multimodal capabilities, it is something that is going to help you in that case. It is something that also does pretty good with general creativity and flexibility. So, both of these models are exceptional, but you would want to use them in different use cases as the ones that had mentioned. But that's basically it, guys. I hope you enjoyed today's video and got some sort of value out of it. I'll leave all these links in the description below, join the newsletter, join the Discord, follow me on Twitter, and lastly, subscribe, turn on notification bell, like this video, and please take a look at our previous videos so that you can stay up to date with the latest AI news. With that thought, guys, have an amazing day. Spread positivity and I'll see you guys fairly shortly.