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

8/14/2025

Why Is the Gemini 2.5 Pro API So Unreliable & Slow? A Deep Dive

Alright, let's talk about something that’s been on a lot of developers' minds lately: the Gemini 2.5 Pro API. There's a TON of buzz around it, but not all of it is good. If you've been working with it & felt like you're going crazy, you're not alone. One minute it's brilliant, the next it's a frustrating mess. So, what's REALLY going on? Is it just growing pains, or something more?

I’ve been digging through forums, Reddit threads, & community discussions to get to the bottom of this. Honestly, the picture that’s emerging is pretty complex. It seems like the "unreliable & slow" label isn't just a feeling; it's a shared experience backed by a lot of frustrated developers.

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.

This has HUGE implications. If you're running a business that depends on a consistent & reliable AI, these sudden slowdowns & failures are completely unacceptable. Imagine you’ve built a customer service system using the Gemini API. Your users expect instant answers, but because of some backend model rollout you weren't even aware of, your service is now lagging or failing. That directly impacts your customer satisfaction & trust.

This is where having a more robust & specialized solution becomes critical. For instance, businesses that need 24/7, dependable customer support often turn to platforms like Arsturn. The idea is to build a custom AI chatbot trained specifically on your own business data. This creates a more controlled environment. The chatbot isn't subject to the whims of a general API's update cycle; it's a dedicated system designed for one thing: providing instant, accurate support to your website visitors. It's a different approach that prioritizes stability for business-critical functions.

What’s in a Name? The "2.5 Pro" Confusion

Here's a fun fact that turns out to be a major source of frustration: the term "2.5 Pro" might not even be an official, stable model name. A lot of the issues developers are facing seem to stem from using incorrect or outdated model identifiers. Google has "Stable," "Preview," & "Experimental" versions of their models, & it seems many developers were unknowingly using a preview or experimental version they were calling "2.5 Pro".

These preview versions are, by nature, unstable. They are for testing new features, they come with no service level agreements (SLAs), & they can be changed or deprecated with little to no warning. One user reported their "gemini-2.5-pro-preview-05-06" model worked perfectly one day & then completely stopped working the next, because it was being discontinued in favor of a new preview version.

This constant churn is a headache. You might build your app around a model that performs amazingly, only to have it pulled from under you. This leads to a frantic scramble to update your code, only to find the new recommended version isn't even fully live yet, as one user frustratingly pointed out. This naming confusion & the fleeting nature of preview models contribute heavily to the perception of unreliability.

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.
Worse Coding Performance: The very thing that got many developers excited—its coding ability—seems to have taken a nosedive. It makes more mistakes, provides nonsense solutions, & is generally less helpful for development tasks.
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.

So, why the downgrade? The leading theory among users is cost. The hypothesis is that the amazing "03-25" preview version was computationally expensive to run. To make the "Pro" version more economical at scale, Google may have "distilled" or quantized the model, effectively making it faster & cheaper but also, well, dumber. As one user put it, they likely "forced onto us, because it can reduce the resource usage more effectivelly, making it cheaper but not better."

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.

This is a scenario where building a no-code AI chatbot with a platform like Arsturn offers a clear advantage for specific business goals like lead generation or customer engagement. When you're trying to optimize your website & boost conversions, you need a predictable & efficient system. Arsturn helps businesses build these specialized chatbots, trained on their own data, that can engage visitors, answer product questions, & capture leads. The focus isn't on creating a general-purpose agent that might overthink a problem; it's about creating a streamlined, conversational AI that reliably achieves a specific business outcome, like guiding a user through a sales funnel. It helps build meaningful connections with your audience through personalized, predictable interactions.

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.

For developers & businesses, the key takeaway is to be cautious. If you're building a mission-critical application, relying solely on a "preview" or "experimental" API endpoint is a recipe for disaster. Even the "stable" versions have shown a tendency to wobble during new releases.

It's a classic trade-off. Do you want the absolute cutting-edge, with all its power & unpredictability? Or do you need a stable, reliable solution for a specific business problem?

If you're in the latter camp, especially for things like customer service, lead generation, or website engagement, it might be worth looking at more specialized platforms. A tool like Arsturn lets you sidestep a lot of these issues. By allowing businesses to create their own no-code AI chatbots trained on their specific data, it provides a level of control & reliability that a general-purpose, constantly-in-flux API sometimes can't. You get an AI that's an expert in your business, providing instant support & personalized experiences 24/7 without you having to worry that a new model rollout is going to break your entire setup.

Ultimately, the Gemini API will likely mature & stabilize over time. Google is undoubtedly aware of these issues (the forums & Reddit threads are hard to ignore). But for now, the reality is a mix of incredible potential & frustrating unreliability.

Hope this deep dive was helpful & gives you a clearer picture of what's going on. It’s a bit of a wild west out there in the world of AI APIs right now. Let me know what you think & what your own experiences have been.