Originally published at https://blog.akshatuniyal.com.
The AI model race is moving ridiculously fast.
Every few months there’s a new release claiming to be the “most powerful model yet.” Sometimes it’s hard to keep track of what actually changed and what’s just marketing noise.
Right now two of the most interesting models are:
OpenAI → GPT-5.4
Google → Gemini 3.1 Pro
Both are extremely capable. No question about that.
But after spending some time using them side-by-side for actual work (not benchmark screenshots), one thing became clear pretty quickly:
They feel very different to use.
ChatGPT (GPT-5.4): The Workhorse
GPT-5.4 feels a bit like working with a very competent engineer sitting next to you.
Where it tends to shine the most:
• coding and debugging
• structured reasoning
• breaking down messy problems
• editing or refining technical writing
• building workflows or agents
One thing I’ve noticed is how it tends to structure the problem before answering.
If you give it a messy prompt (which honestly happens a lot in real work), it often pauses a bit, organizes the problem internally, and then responds with a fairly clean breakdown.
That behavior actually matters more than you might expect.
Instead of feeling like a chatbot producing text, it often feels more like a problem-solving assistant.
Not perfect of course — but surprisingly reliable.
Gemini 3.1 Pro: The Multimodal Knowledge Engine
Gemini feels a bit different.
Where ChatGPT behaves like a structured thinker, Gemini often feels like a massive knowledge engine.
It seems particularly strong when the task involves large amounts of information or mixed input types.
For example:
• long documents
• multimodal inputs (text + images + video)
• large context reasoning
• combining information from multiple sources
Another thing worth mentioning is how deeply it connects to the Google ecosystem.
Gemini is increasingly integrated across:
• Google Docs
• Gmail
• Search
• Android
• developer tooling
Because of that, it sometimes feels less like “a chatbot” and more like an AI layer sitting on top of Google’s products.
That’s a very different strategy compared to OpenAI.
Context Windows: This Part Is Honestly Wild
One of the biggest changes in modern AI models is context size.
Both GPT-5.4 and Gemini 3.1 Pro can now handle around 1 million tokens of context.
Which basically means you can throw things like:
• entire codebases
• long research papers
• full reports
• books
• multi-hour transcripts
into a single prompt.
A couple of years ago this would have sounded unrealistic.
Now it’s becoming fairly normal.
For things like research, engineering analysis, or enterprise knowledge work, this is actually a pretty big deal.
Quick Capability Comparison
Not scientific benchmarks — just practical impressions from using both.
Both models are strong. They just optimize for slightly different things.
My Honest Takeaway
If your work is heavily focused on:
• engineering
• coding
• technical reasoning
• structured problem solving
GPT-5.4 currently feels slightly stronger.
But if your work involves:
• large documents
• multimodal inputs
• research synthesis
• Google ecosystem workflows
then Gemini 3.1 Pro is extremely impressive.
The Real Story
The most interesting part of this comparison isn't which model wins.
The real story is how fast the lead keeps changing.
Six months ago this comparison looked different.
Six months from now it will probably look different again.
The pace of change in AI right now is honestly a bit crazy.
Which also makes it one of the most fascinating technology shifts to watch.
If you’ve been using both recently, I’m curious:
Which one actually made you more productive?
About the Author
Akshat Uniyal writes about Artificial Intelligence, engineering systems, and practical technology thinking.
Explore more articles at https://blog.akshatuniyal.com.
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