“Slower, Vaguely Smarter?”: Gemini 2.5 Pro Faces Backlash from Power Users Over Declining Precision and Performance
Once a Darling of Devs and Data Scientists, Google's Latest Flagship AI Sparks Frustration Across the Technical Community
In the high-stakes world of AI development, where milliseconds matter and precision isn’t optional, Google's May 6 release of Gemini 2.5 Pro—the much-anticipated upgrade to its flagship model—has landed with a resounding thud among its most discerning users: professional coders, data analysts, and technical researchers.
Just 24 hours after launch, forums and developer channels lit up with discontent. From “crippling lag” to “instructional amnesia,” early adopters have sounded alarms over what they view as a significant regression masked behind a veil of surface-level politeness and processing animations.
Gemini 2.5 Pro 05-06 Fact Sheet
| Feature | Details |
|---|---|
| Model Name | Gemini 2.5 Pro Preview 05-06 |
| Model ID | gemini-2.5-pro-preview-05-06 |
| Pricing (per 1M tokens) | |
| Input (≤200K tokens) | $1.25 |
| Input (>200K tokens) | $2.50 |
| Output (≤200K tokens) | $10.00 |
| Output (>200K tokens) | $15.00 |
| Best for | Coding, Reasoning, Multimodal understanding |
| Use Cases | - Reason over complex problems - Tackle difficult code, math, and STEM - Analyze large datasets/codebases/documents |
| Knowledge Cutoff | January 2025 |
| Rate Limits | - 150 RPM (Paid) - 5 RPM / 25 requests per day (Free) |
"It Thinks More, Says Less": A Frustrating Shift in Interaction Paradigms
One of the most consistent—and jarring—changes reported by users is a steep increase in latency. Multiple professionals shared that Gemini 2.5 Pro now “thinks” for extended periods, often 2–4 times longer than the previous build. These delays are compounded by a new pattern: the model intermittently displays messages like “thought for 13 seconds”, seemingly trying to justify its slower pace.
Yet what emerges after that wait is, paradoxically, less incisive output.
“It’s as if it’s buffering confidence,” said one technical lead at a financial modeling firm, requesting anonymity to speak candidly. “You wait longer, but get something shallower. There’s a disturbing drop in analytical depth, especially when tackling layered problems.”
This shift is particularly troubling for power users who rely on AI for nested logic flows, statistical modeling, or precision code review—areas where speed and rigor are inseparable.
Cognitive Drift: Instruction Following Takes a Hit
Another lightning rod for criticism is Gemini 2.5 Pro’s diminished ability to follow instructions across multi-turn conversations—a core capability for professional workflows.
Several users noted that the model forgets directives mid-thread, even failing to carry over simple parameters from one response to the next. Others observed that it would “fumble basic instructions”, or worse, ignore them entirely.
"At one point, I gave it five directives. It responded to two and lost the other three," one enterprise AI engineer recounted. "In the past, it used to weave those requirements together seamlessly. Now it’s like dealing with an intern on their first day."
And for developers, the frustration escalates further. Gemini reportedly omits key parts of code files, particularly in long-form outputs. This has led to broken builds and interrupted pipelines—outcomes that are not just inconvenient, but potentially costly in production environments.
“It Butchers Code Now”: The Anatomy of a Regression
Perhaps the most serious concern lies in code quality—an area where Gemini 2.5 Pro, by the numbers, underperforms its OpenAI counterparts.
According to LiveBench metrics, Gemini scored 72.87 in coding, compared to notably higher performance by OpenAI’s o3 Medium and High variants. While its math score and reasoning ability remain competitive, those strengths are proving insufficient compensation for the model’s erratic execution in technical domains.
One developer described how the model "mutilated" existing code rather than adjusting specific blocks, making sweeping and damaging edits rather than the precise, surgical modifications requested. Another noted that Gemini "satisfied maybe three out of eight sanity checks in a nested if-else test,” missing obvious logical paths that prior versions handled competently.
This isn't a minor degradation—this is, as one reviewer described, "at least 50% worse than the previous release in my honest opinion."
“Overly Polite, Dangerously Vague”: A Style Over Substance Problem?
Many have pointed to a conspicuous tonal shift in Gemini 2.5 Pro’s output. It is now, according to multiple reviewers, “more polite, more verbose, and more evasive.” The critique isn’t about tone for tone’s sake—but about what that tone masks.
"Earlier builds were curt but insightful. This one feels like it’s been run through a PR filter,” noted a software architect from Berlin. "You ask for a risk analysis and get a diplomatic essay. It’s vague, cautious—basically unusable when you need hard calls."
In an industry that prizes directness and diagnostic clarity, Gemini’s softened output style feels like an unwelcome editorial choice—one that comes at the expense of utility.
Hardware Strain and Upload Errors: Technical Limitations Rear Their Head
Beyond software performance, users also reported hardware inefficiencies, with Gemini’s local GPU usage plateauing around 30%, far below expected utilization. That bottleneck exacerbates already slow response times, especially during complex computations or multi-file tasks.
Several users further reported upload failures after prolonged usage—an issue that could point to memory leakage or unstable session handling in the new build.
The Numbers Don’t Lie—but They Don’t Tell the Whole Story Either
On paper, Gemini 2.5 Pro isn’t a failure. Its global LiveBench average score of 78.99 positions it as a strong general-purpose model, just behind OpenAI’s o3 class.
Its math and reasoning strengths make it viable for quantitative domains, and it performs **reasonably well in instruction following **—statistically speaking.
But in real-world, high-precision workflows—particularly in software engineering and data analysis, where the margin for vagueness is zero—those numbers are less reassuring.
"This model feels tuned for a user who never pushes past surface-level tasks," one data engineer remarked. "For people like me, that’s not just frustrating—it’s dangerous."
Nostalgia Meets Necessity: Will Users Revert?
Perhaps the most telling indicator of disillusionment is the sudden nostalgia for the previous Gemini iteration, with many calling for a rollback option.
“This is the first time I’ve had teammates say, ‘Can we go back?’ That should worry Google,” said one developer at a cloud infrastructure company.
And indeed, if Gemini 2.5 Pro continues on this trajectory, Google may face a stark decision: prioritize performance for professionals, or double down on accessibility for general users.
What's Next? A Crossroads for Gemini
The discontent around Gemini 2.5 Pro’s May release doesn’t just represent a technical misstep—it highlights a deeper tension in AI development: balancing broader user safety and tone refinement with the needs of power users who demand clarity, consistency, and control.
As competing labs iterate rapidly, and user expectations harden, Google may have little choice but to either recalibrate the model’s foundations—or risk ceding ground to nimbler, sharper challengers.
For now, those on the cutting edge of code and computation are watching closely—and waiting for a fix that doesn’t just think longer, but thinks better.