Deeper Critique of Gemini 2.5 Pro Based on Actual Use Cases
Case Study: Working with Files Up to 400K Tokens
One of the clearest weaknesses in Gemini 2.5 Pro (month 5 & 6 versions) is how it handles very large contexts, like a file with 400,000 tokens. When I tried to get the model to re-parse or re-process that content:
The Pro version kept leaning toward summarization, even when explicitly instructed not to.
Even when I split the content into smaller chunks, and asked for a full response per chunk, the Pro version still defaulted to summarized or compressed replies.
This behavior persisted across attempts, showing that the attention mechanism is not only weak over long contexts, but also not adaptive to user intent in such cases.
Meanwhile…
Flash Version: Richer, Fuller, More Aligned Responses
The Gemini Flash model, when tested on exactly the same input, was far better at:
Expanding on the content instead of collapsing it.
Producing long, detailed, and coherent responses.
Respecting the contextual space given in each chunk, without falling back to lazy summarization.
Flash clearly exhibits more emergent behavior, where the model builds on the context rather than compressing it. It flows with the user’s goal, rather than imposing its own optimization shortcuts.
“Thinking” ≠ Intelligence
The whole idea that “thinking” improves LLM responses needs to be questioned.
What people call “thinking” is actually just:
A set of linear or tree-structured steps.
Reorganization of tasks.
Breaking problems into parts, then reassembling the answer.
This might work for logic puzzles, but when dealing with huge, multi-threaded contexts, that method actually slows down and weakens the output.
In practice, I’ve seen that:
Well-trained models don’t need to ‘think’ — they just respond intelligently.
And ironically, sometimes, when you turn off the “thinking” pattern, the responses become cleaner, sharper, and more insightful.
Cost-Cutting Reflected in Response Quality?
It honestly feels like Gemini 2.5 Pro has been tuned to save computation cost, even if that means:
Shorter, less detailed answers.
Overuse of summarization.
Less willingness to maintain semantic density in long replies.
This is especially noticeable when you compare it to older Pro versions, which were:
More expressive.
More semantic-heavy.
More generous with token usage when the context demanded it.
It’s as if 2.5 Pro’s default behavior is:
“Why expand, when I can just compress and give you a surface-level answer?”
That might save server time, but it breaks the value of LLMs in serious use cases.
Connection Across Conversation Segments
Another limitation in Gemini 2.5 Pro is its inability to weave together ideas across different parts of a conversation.
Even if it has access to all prior content:
It doesn’t initiate connections unless forced.
It lacks emergent linking between themes unless the structure is spoon-fed.
It’s more reactive than constructive.
This makes it feel like the model is waiting for commands, instead of actively collaborating with the user in a flowing dialogue.
Suggestion: “Disable Thinking” Option in Pro
We need an option in Gemini Pro to disable forced “thinking-style” steps.
Some tasks don’t need breakdowns, and in fact, suffer when they’re broken down.
This is especially true when:
You’re working with long narratives.
You want raw expansion, not deduction.
You need density of information, not hierarchy.
Final Thought
If Flash can do it better, richer, and faster, then what’s the point of calling the Pro “Pro”?
The potential is clearly there—but the defaults are sabotaging it. Let Pro be pro by:
Giving full responses when needed.
Respecting the user’s intent, not just the optimization logic.
Making “less thinking, more responding” a real option, not a workaround.
##To clarify, the Pro version can link the context and events, but not with the quality of Flash, which, if it had more limits, would be better than Pro.
