I’m not going to comment on internal studio issues because they’re minor, but I will discuss the real failures we’re seeing. With the release of Gemini 3.0 Pro, the marketing makes it sound like the most powerful model ever created, as if the debate is settled by description alone. In practice, however, there is almost no meaningful difference between it and 2.5 Pro, aside from an increase in “reasoning.” But reasoning itself is not inherently valuable, despite what experiments or benchmarks might claim.
In real-world usage, what truly matters comes down to three factors:
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Response length
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Attention-mechanism effectiveness (not context size—attention strength)
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Training quality
Additionally, Gemini’s only major advantage is its extremely large context window of up to 1 million tokens, which still remains unmatched.
- Response Length
Technically, Gemini can generate very long answers—somewhere around 65,000 tokens or more. But this capability does not translate into consistently rich content. In practice, the responses often feel as short and limited as a long text message.
Gemini 2.5 used to produce the longest and most comprehensive answers automatically, without requiring extremely detailed prompting. Today, achieving that same richness requires precise instructions.
Gemini 3.0 Pro produces more consistent and accurate descriptions—especially in visually oriented tasks—but the substance of the answer often feels weak. The angles, structure, and order might look correct, but the core content lacks strength.
- Attention-Mechanism Strength
This is where the difference between versions becomes extremely clear.
Real Example: Feeding the Model a Dense Document
I tested both models using a highly information-rich file, where a critical piece of information appeared at the very end in a subtle, side-note form. Here’s what happened:
Gemini 3.0:
Most of the time, it completely ignores the critical detail.
And when it does notice it, it often discards everything else and responds only to that final detail—losing all broader context.
Gemini 2.5:
Most of the time, it detects the final detail and builds on it intelligently.
Only about 1 or 2 times out of 10 does it hyper-focus on that detail alone.
This clearly shows that 2.5 has a much stronger and more stable attention mechanism, while 3.0’s attention is inconsistent—either overly selective or completely blind to critical signals.
This is why I consider “reasoning” far less important than solid attention. If the model cannot reliably detect the key signal in a context, no amount of chain-of-thought will save it.
- Training Quality
This issue applies across all Gemini models.
When you compare Gemini to its primary competitor, the difference is fundamental:
Gemini behaves like someone who has memorized the material very well.
The competitor behaves like someone who memorized the material and learned how to connect it to other fields, extract insights, and develop new ideas.
The competitor can:
generate concepts for large, complex projects
offer extremely rare, expert-level knowledge
provide information only a top 1% engineer would know
operate like a specialist with 10+ years of experience
Meanwhile, Gemini often requires you to introduce the rare information first, and even then, the answer tends to feel improvised and shallow.
Expert-Level Response Quality: A Clear Example
If you ask a complex question—say about chaotic maps—and request a new, unconventional pattern, the difference becomes dramatic.
Gemini models:
They respond with variations of the most well-known chaotic maps, offering simple parameter tweaks or generic modifications. The suggestions feel like standard textbook material.
The competitor:
It may list the well-known systems initially,
but when you refine the request, it produces entirely new categories—ideas even YouTube channels haven’t discussed.
For example:
If you ask:
“Give me a chaotic-like map that has chaotic behavior but doesn’t exhibit explosive divergence.”
Gemini will typically reply with something like:
“Try adjusting parameters of known chaotic maps,”
or suggest minor modifications to standard systems such as the logistic or Hénon map.
But the competitor will respond with something like:
“Have you heard of the SNA — the Strange Nonchaotic Attractor?”
And then explain its properties, use-cases, and even how to construct such a system.
This is the level of expertise that Gemini currently lacks.
Final Thoughts
Gemini still has one standout advantage:
its massive 1-million-token context window.
That alone keeps it competitive.
But if Google manages to fix these three areas—response depth, attention strength, and training quality—then Gemini could genuinely become the best model in the world.
Until then, I still prefer 2.5 Flash, because its stability, attention behavior, and practicality outperform the newer versions in real usage.