Gemini 3.0 Pro + Claude Opus 4.5 = The Ultimate AI Coding Workflow! Incredible Coding Results!

Looks like things are finally settling in and we're getting a much clearer picture of the new models that was just recently released like Google's Gemini

3.0 Pro and Enthropic Slade 4.5 Opus.

Both are insanely powerful across a huge range of benchmarks. The Gemini 3.0 Pro by Google is their most intelligent model yet, and it's designed for complex

reasoning, advanced multimodal tasks, as well as bringing creative concepts to life. It delivers state-of-the-art

life. It delivers state-of-the-art agentic coding performances as well as incredible results on terminal bench live codebench, and various other coding benchmarks. It is paired with the 1

benchmarks. It is paired with the 1 million context window, and you've got a model that can handle a massive code base with ease with deep understanding long context, reasoning, as well as

strong coding capabilities all in one.

But right after the Gemini 3.0 Pro release, Entropic fired back with a curveball with the launch of Cloud 4.5 Opus, arguably the best coding model in the world right now. This thing is a

monster for coding agents and rural computer use. It's also significantly

computer use. It's also significantly better at everyday tasks like deep research, document analysis, working with spreadsheets, and creating polished slide decks thanks to its agenda

capabilities. And the numbers speak for

capabilities. And the numbers speak for themselves. It hit a state-of-the-art

themselves. It hit a state-of-the-art 80.9 percentage on the Swaybench verified, which is just insane. But

here's the interesting thing. Even

though both Gemini 3.0 Pro and Cloud 4.5 Opus are incredible, they lack something that the other model nails. To

illustrate this, just take a look at this comparison test. A benchmark across three different coding challenges where it tested Gemini 3.0 versus the Cloud

4.5 Opus inside Kilo Code. This is a Python rate limiter prompt which is a strict prompt adurance. It's a test that had 10 rigged requirements. Exact class

names, exact error messages, exact method signatures, zero creativity allowed. And the results showed that the

allowed. And the results showed that the Gemini 3.0 Pro followed the prompt literally. It was clean, minimal

literally. It was clean, minimal correct with its implementation. There

was no extra features, no supplements no assumptions, and it delivered exactly what was asked for. nothing more and nothing less. It scored the highest for

nothing less. It scored the highest for the strict prompt adherance. Now, if you were to compare the results of the Opus 4.5, it stayed close to the spec with

clean code and better documentation slightly more verbose than Gemini, but it lost the point due to a tiny naming mismatch with the tokens as well as the current tokens. It came second place

current tokens. It came second place very close behind Gemini in this particular case, and it costed a bit more. The takeaway with this clear

more. The takeaway with this clear comparison was that if you want just to get the exact instructions, Gemini is the most obedient model you can use. If

you want to follow the instructions but write it nicely, Opus gives a more polished code that was generated in this case. The second comparison test was a

case. The second comparison test was a TypeScript API refrarator. It was

provided a 365 line messy legacy API with vulnerabilities, inconsistent naming, missing validation, and unsafe queries. The task was to refractor

queries. The task was to refractor completely, fix everything, and implement 10 architectural requirements. And in this case, the

requirements. And in this case, the cloud 4.5 opus was perfect where it scored a 10 out of 10. The only model to actually catch all the required fixes.

It was the only one to actually implement the rate limiting which was explicitly required. It used environment

explicitly required. It used environment variables for secrets, added prompt and proper error hierarchies. It also

included every architectural component that was asked for and it was the most complete refrator that was seen in this particular case. But if you were to

particular case. But if you were to compare it with the Gemini 3.0 Pro, it was solid but missed deeper issues and it scored an 8 out of 10 with its overall score. It was a clean output but

overall score. It was a clean output but minimal interpretation. Missed some

minimal interpretation. Missed some deeper vulnerabilities and architectural flaws and it understood the transactions that were needed but didn't actually implement them which was surprising and

it didn't implement rate limiting at all which was a cure requirement that was set. But overall it is good at surface

set. But overall it is good at surface level refratoring but weaker on full system corrections. Gemini is great for

system corrections. Gemini is great for fast, clean rewrites and Claude is in this case far better at deeper architecture, security and complete implementation. And the last test is

implementation. And the last test is focused on notification systems and understanding the actual system feature buildout. This is where it was provided

buildout. This is where it was provided 400 lines of code with web hooks and SMS support and it asked the models to first explain the existing architecture with

the ask mode and then the second requirement was to add a full email handler with the code mode. This tests

the system comprehension plus the ability to extend the existing architecture and the results were pretty expected where the cloud 4.5 opus focused on the fastest and most complete

output where it finished it in one minute with the most thorough implementation and it added templates for all seven notification events. It

delivered the runtime template management error hierarchies as well as fully aligning architecture. It was

extremely high with its system awareness and with the comparison to the Gemini 3.0 Pro, it was minimal but it was functional and it performed the task a

bit uh cheaper. It was able to actually add a working email handler which was simpler which is better than the output that we saw from the cloud opus 4.5. No

attachments, no CC or BCC. It assumed

the payload always contains the email and the only implementation template for few lines of code. Now overall it did seem like Gemini produced the minimal

workable version but Claude was able to produce a complete production ready fully featured system. In short this is just a small test that will extensively test these two models on different

domains. And yes, Gemini also does

domains. And yes, Gemini also does better at front-end tasks, especially when the goal is clean UI, but you were able to see that Claude 4.5 does really

good for egentic workflows inside real coding environments. It's best for full

coding environments. It's best for full system reasoning and end-to-end feature builds. It doesn't have more verbose or

builds. It doesn't have more verbose or it doesn't add any extra abstractions like Gemini does, but it is something that's strong in different domains like refractoring as well as security

awareness. Now, the Gemini 3.0 Pro, on

awareness. Now, the Gemini 3.0 Pro, on the other hand, is extremely fast. It's

minimal and it's precise. It follows

instructions word to word. It's cheaper

than the Cloud Opus 4.5. It's great for front-end and clean implementation, but it misses deeper architecture. It often

produces just enough solutions, and it doesn't go over and beyond. But what if you were to combine these two together?

Inside Kilo Code, you can actually combine the Gemini 3.0 Pro as well as Cloud 4.5 Opus into a single workflow letting each model handle the task it's

best at. Because when you pair Gemini's

best at. Because when you pair Gemini's fast, clean front-end generation with Claude's deep back-end reasoning and architecture, you're essentially building a dual engine coding system

that produces higher quality apps higher quality code than either model could do alone. Now let's break down exactly what we can actually do by combining these two within Kilo code and

how both of them be can become a specialized engineer that you can work with in one single environment. Now to

get started you can simply go ahead and open up VS Code. I particularly like using Kilo code cuz it is an open-source better alternative than Klein and it functions a lot better and you can

easily get started completely for free cuz they also offer free credits. So you

can install it for whatever ID you want and you can just simply install it from the extension store by simply going ahead and searching up Kilo and then you can go ahead and install this and then

you will be able to access it on the left hand panel. What you can do next is select the model of your choice. Now, in

this case, what I'm going to be doing to showcase the best optimal coding experience is having Cloud 4.5 Opus for all planning and architecture needs. So

what you want to do first is head over to settings. And the reason why is cuz

to settings. And the reason why is cuz we're going to be setting up the ultimate workflow where you can use these two models in different cases when you're working with Kilo Code, the AI

agent. What you want to do first is

agent. What you want to do first is configure your first profile. You want

to click on add profile. You can give it a name. I'm going to be naming this

a name. I'm going to be naming this opus. We're going to create that

opus. We're going to create that profile. And what we want to do is

profile. And what we want to do is obviously select the provider that you want to use. I'm using kilo. And then I want to select the opus 4.5. And now

what you want to do is also enable reasoning and change the verbosity to high. Then in the same manner you want

high. Then in the same manner you want to add another profile for the Gemini model. You want to create this profile.

model. You want to create this profile.

Then provide your API provider and then select the Gemini 3 Pro preview. Then

you want to change the reasoning effort to high and then you are basically set and ready to go. You can then click save. And now we can work with the kilo

save. And now we can work with the kilo code agent. In essence, you want to

code agent. In essence, you want to first select the architect mode, the planning mode. This is a specific mode

planning mode. This is a specific mode built within kilo code which is going to help you plan and design better with the cloud 4.5 opus. So you want to select the opus profile that we have already

set where we're going to be using it for all planning and architecture needs because it's going to break down tasks designs for systems catching errors issues and thinking long term cuz it

does that better than the Gemini model.

Then when it comes to coding we can then switch over to the coding mode and then we can choose the Gemini profile. This

is where the Gemini model will be uh essentially the coding executor because it follows instructions perfectly. It

writes minimal and clean code, handles front end and UI task extremely well and we can have it so that both of these two different profiles can work together and it can work on building out the best

outcome for the code that you're looking for. So I can start off by giving the

for. So I can start off by giving the system prompt for the plan that I want the cloud model to actually generate for this app that I'm trying to create

which is a task manager with a smart prioritization feature where you can add tasks, upload documents, and use an AI to extract key tasks and priorities. We

wanted to work on building out the back-end system and then have Gemini work on building the code. So now what I can do is select the architect mode and then select the opus profile and then

have it work on generating the actual plan. So you can see that it is going to

plan. So you can see that it is going to rapidly work on thoroughly building out the plan structure. And this is something that Claude does best at.

Looks like it has finished developing the plan. And what we can also do is we

the plan. And what we can also do is we can even use the opus model in certain cases to code functions for our app.

It's just a bit more expensive, but it does a better job in using tools better than previous models. And now you can switch over to the code mode. And then

you can select the Gemini profile. And

you can give it the prompt to now implement the overall plan that Claude had generated. And it is going to work

had generated. And it is going to work systematically to work on implementing that plan using the code generation capabilities of the Gemini model. So you

can send in this prompt and it's going to work on building out that overall component of the AI task manager with the plan that was built. Now what you can also do is switch between different

profiles. You can actually set this up

profiles. You can actually set this up within the settings so that it could actually switch profiles so that it could use the capabilities of both profiles in certain cases based off the

prompt that you sent in cuz both models have their own benefits and we want Gemini to be used for implementation and cloud to be used for reviewing as well

as debugging. So you can have it so that

as debugging. So you can have it so that it could work on the debugging process and use its code generation capabilities for that particular use case. And there

we go. Just take a look at this. It was

able to implement that full plan that the Opus architect mode was able to generate and the Gemini model was able to code out all the components that was necessary for this app to be functional.

And it took approximately $2 to do this.

So you can see it is drastically cheaper than having Gemini just work on the whole process on its own. And it

definitely looks a lot better than having Opus generate all the components cuz this is the UI of the task management app. You can see that it is

management app. You can see that it is something that it built and it is a way for you to prioritize smarter where you can add, edit and delete different

tasks. You can add in any of your tasks

tasks. You can add in any of your tasks like adding a homepage and then you can add a priority, add a tag. So we can say something like coding here and then add

a description for the task and you can create it. And this is the overall task

create it. And this is the overall task that has been generated. Here is the functional uh canban board that has been generated. You can edit these

generated. You can edit these components. You have different board

components. You have different board views as well. You have your profile.

But one thing you can also do is you can start smart extraction which is an AI feature I had told it to generate. So

I'm going to go ahead and upload a file.

So I've gone ahead. I have uploaded a large file and I can have it extract all the tasks. So it is analyzing it and it

the tasks. So it is analyzing it and it is something that's powered by the Gemini AI model and it is going to then analyze all those tasks and display it

on our task board. So you can see that it was able to review the data scraping reliability task, the investigating the missing company analysis as well as

fixing the summary report generation input. It also gave it a tag and added

input. It also gave it a tag and added the description. This is a simple app

the description. This is a simple app that it was able to generate, but it is something that has zero flaws, zero bugs, cuz it was able to thoroughly debug it and code out all the components

that we had the Opus model implement.

With this combination, we were able to get one of the most cheapest and best generations with this combination. If

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capability of Claude Opus 4.5 being combined with Gemini 3 Pro and you're going to be able to get a dual model workflow that's perfect for coding purposes. I'll leave all these links in

purposes. I'll leave all these links in the description below so that you get a better understanding of how they benchmark these two models in different cases as well as how you can easily get

started with Kilo Code with these two amazing models. This is something that I

amazing models. This is something that I highly recommend and it's something that you should even try out just to see if it fits your needs and it's something if could get you uh definitely elevating

your workflow if you were to implement.

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