Gemini 2.5 Pro API: Why It's Unreliable & Slow - Arsturn

## Why Is the Gemini 2.5 Pro API So Unreliable & Slow? ... Alright, let's talk about something that’s been on a lot of developers' minds lately: the Gemini 2.5 Pro API. ... ### The Core of the Problem: Instability is the New Normal One of the biggest complaints I've seen over & over again is the sheer instability of the Gemini API, especially when Google rolls out new models. It’s like clockwork: a new model is announced, & suddenly, older, supposedly stable models like Gemini 1.5 Pro or Gemini 2.0 Flash start to get wonky. We're talking about massive latency spikes, with response times jumping from milliseconds to over 15 seconds for the exact same input. One developer in a Google Developer forum put it perfectly: "The function-calling feature in Gemini 2.0 Flash began failing intermittently for approximately three days" right after the Gemini 2.5 Pro release. And the weirdest part? The issues often just... resolve themselves after a couple of days. This kind of unpredictable behavior is a nightmare for anyone trying to build a production-ready application. You can't have your customer-facing features just randomly breaking with no explanation. … ### The "Lobotomized" Model: A Serious Downgrade in Quality This is probably the most passionate & widespread complaint. A huge number of users who were early adopters of a preview version, often referred to as "03-25," feel that the official "stable" release of Gemini 2.5 Pro is a massive step backward. The sentiment is so strong that I saw the phrase "lobotomized" pop up more than once. The complaints are shockingly consistent: - **Increased Hallucinations:** The newer model is accused of making things up with complete confidence, proposing fake solutions, & introducing bugs into code. One user on Reddit lamented, "When Gemini 2.5 Pro don't know how to do something, instead of research, its start to liying and introducing bugs." - **Ignoring Instructions:** Developers report that the model has become terrible at following direct instructions & rules. It ignores prompts, changes variable names for no reason, & fails to stick to the requested format. - **Painful Verbosity:** Even when explicitly told to be concise, the model has a new tendency to be overly verbose, wrapping simple answers in unnecessary fluff. … - **Gaslighting & Sycophancy:** This one is more of a personality quirk, but it's infuriating for users. The model will confidently state incorrect information & then apologize profusely when corrected, only to repeat the same mistake. It’s also developed a sycophantic tone, starting every response with "what an excellent question," which many find annoying & a departure from the more direct & useful earlier versions. … ### The Perils of Tool Calling & Runaway Costs Another major pain point has been the unreliability of tool calls, or function calling. This is a crucial feature for creating more complex applications & agents. There have been numerous reports of tool calls freezing up, failing, or the model simply printing the underlying tool call command into the code it's writing. While some community managers have acknowledged that these issues were "on Google's end" & are improving, the inconsistency has been a huge problem. What’s worse, this unreliability can hit your wallet. One user on the Cursor forum posted a screenshot of their bill, exclaiming, "CURSOR IS A LEGIT FRAUD TODAY 18 CALLS TO GEMINI TO FIX API ROUTE!!! IT OVERTHINKS AND BURNS THE REQUESTS AT INSANE SPEEDS 1$ PER MINUTE IS ■■■■■■■ INSANSE". This "overthinking" is a real concern. The model might get stuck in a loop, making numerous unnecessary tool calls to perform a simple task, racking up API charges without delivering a useful result. This is another area where a general-purpose API can be a double-edged sword. The flexibility is great, but the lack of fine-tuned control can lead to unpredictable behavior & costs. … ### So, Where Do We Go From Here? Look, here’s the thing. The Gemini 2.5 Pro API is an incredibly powerful piece of technology. But it's clear from the widespread user feedback that it's going through some serious growing pains. The combination of instability during model updates, confusion around model naming, a perceived drop in quality for the sake of efficiency, & unreliable tool-calling has created a perfect storm of frustration.