I asked a simple follow-up question recently, and the model responded with the word "producing" repeated 2,368 times. Not a typo. Just 4,700 tokens of pure nonsense filling my screen before I had to manually kill the run.

This token looping is a known issue, but imagine this happening in a production API.

When building agentic workflows, we obviously implement guardrails—repetition detection, token budget limits, validation layers. But when the model breaks at the inference level, we can't fix it. All we can do is detect the garbage, kill the request, and retry.

Meaning you pay for the failure, and then you pay again for the retry.

AI hallucinations get all the hype, but these "boring" repetition loops are the real expensive nightmares in production.
What's the weirdest or most expensive API failure you've caught in the wild? Has anyone seen something worse than 2,368 "producing"s?

PS: Image in comments

Good evening,
I am a French-language writer and lyricist. Gemini is currently helping me prepare English versions of my latest songs – a challenging task, as I need to preserve the rhythm, meaning, and rhyme scheme. My English isn't perfect, which is why I need the help. (I’m very tired right now, so I’m using Google Translate to write this message.)
All day long, Gemini was a delightful, sensitive, funny, and skilled translation partner, just as it has been since February. Then, bracketed tags containing information about me – pulled from its memory – started cluttering its responses. The chat became increasingly sluggish, even though the conversation wasn't particularly long and it has handled much larger ones before. I took a break just in case, but the problem persisted when I returned.
I started a new conversation but encountered a "1097" error, and the only interface I got was the standard Gemini, which spoke to me in a cold, impersonal tone.
I’ve noticed that these kinds of issues tend to happen during updates. Is that the case right now? Is there a traffic overload preventing my usual Gemini from loading?
I’ve cleared my Chrome browser cache multiple times, refreshed the Gemini page, checked for Windows updates, and restarted my computer, but nothing has worked.
Aside from crossing my fingers and waiting for a quick fix, is there anything else I can do? I should mention that I have a paid Google AI Pro subscription (5To).

Reposting as I used wrong profile before but as per title. Our company got 100k credits last year then had a tough period and forgot about it and now only have 2-3 months to spend. What would you build with it?

Company with infinite compute and data who made transformers architecture and gvisor like many many techniques can't be what all are saying.

Google definitely playing 4d chess.

I've made a few posts about this already, so this combines my main thoughts into one post.

A lot of people are talking about Gemini 3.5 Pro, but I think many are misunderstanding what Google is actually building. People compare Claude Fable 5 to Gemini 3.5 Flash, which is a heavily throttled, low-latency model built for speed, coding, and agentic workflows. It's designed to be fast and inexpensive enough to act as a sub-agent, not to represent Google's highest capability. Assuming Flash is Google's ceiling just doesn't make sense.

I'm also seeing people compare the raw intelligence of the models without considering architecture. Models like Fable 5 appear to rely heavily on sub-agent swarms that brute-force solutions through repeated trial-and-error. That's not a bad thing, but if a model takes 20 minutes to build something like a Minecraft clone, it's probably because it's repeatedly encountering compiler errors and trying again until it works.

Google seems to be taking a different approach. Nearly every Gemini model is natively multimodal, and 3.5 Pro appears to be designed as an orchestrator sitting above specialized sub-agents. That means its job isn't simply to generate text—it's coordinating multiple systems together. I don't think 3.5 Pro is some magical AGI that can suddenly absorb every DeepMind breakthrough, but Google has decades of AI research that they're slowly integrating into one ecosystem. They just need a model powerful enough to coordinate it. While being a model that can oneshot without agents/multiple iterations

That brings me to why I think 3.5 Pro is delayed.

The leak mentioned Google wanted to incorporate learnings from the Gemini 3.5 Flash rollout regarding token consumption. A lot of people took that to mean Flash itself was delayed, but I think Flash is actually the bottleneck that's delaying Pro.

If you've watched 3.5 Flash think, it burns through an enormous number of intermediate tokens. When it reaches a difficult problem, it often stops, writes something out, thinks again, writes more, and keeps looping while fighting for a solution. It consumes a huge number of reasoning tokens.

Now imagine 3.5 Pro sitting above several Flash instances as an orchestrator. It has to ingest everything those sub-agents produce. If Flash is excessively token-hungry, Pro ends up wasting premium compute simply reading all of that intermediate reasoning. You can't really release an orchestrator until the token economy of the sub-agents is efficient enough. That's why I think the delay makes sense. So they are either releasing 3.6 flash or 4 flash to improve the model as they did with 3 pro (3.1 pro to improve it) alongside with 3.5 pro

I also think Google is following the same pattern they used before.

When Gemini 3 Pro launched, it was incredibly capable, but its hallucination rate was very high. Google later released 3.1 Pro, which significantly reduced hallucinations while improving the model overall. I wouldn't be surprised if they're doing something similar here: improve Flash's efficiency, reduce token consumption, make it cheaper to run, then launch 3.5 Pro on top of that.

I've also noticed 3.1 Pro feels noticeably worse than it used to. I do think it's throttled, but I don't think that's because Google suddenly made the model worse. I think it's a compute allocation problem. As we get closer to 3.5 Pro, they're likely reallocating hardware and preparing deployment. If that's true, 3.1 Pro feeling worse could actually be a sign that 3.5 Pro is close.

Google is simultaneously serving an unusually broad AI ecosystem not just Gemini itself, but multiple Flash variants, Pro variants, AI Studio, Search, Workspace, NotebookLM, Flow, Veo, Imagen, Astra, Jules, Gemma and numerous specialized models behind those products. So compute is low.

As for the hallucinations, I think people confuse two different problems.

One issue is general AI hallucination, which every frontier model still struggles with. Google already reduced hallucinations substantially going from 3 Pro to 3.1 Pro, and I'd expect 3.5 Pro to improve further.

The other issue is that 3.1 Pro seems to trust its internal knowledge far too much. Compared to Flash, which constantly searches the web even without prompting, 3.1 Pro often assumes its internal dataset is correct. That sometimes causes it to incorrectly conclude the user is mistaken or even "hallucinating," which ironically creates more hallucinations. I remember people saying the exact same thing before Gemini 3 Pro released, and then it ended up outperforming almost everything across a huge number of benchmarks.
Google doesn't seem to be panicking right now. If they were, we'd probably be seeing far more leaks and reactionary behavior. They barely seemed to respond to Fable 5 at all. My prediction is that 3.5 Pro either matches it across most areas while beating it in several key ones, or it surpasses it across the board.

...or Google completely fumbles the bag. 😭

I want to address rate limits as well. Google is really generous dare I say. You get ahem: Google A.I studio, Antigravity (Have not ran out of even messages. Not even 5 hour limit), consumer website, jules, and other things respectfully. You get about 45 messages in Google A.I studio with 3.1 pro (Yes low) but combine that with consumer and thats 90. Wanna know the best part? You can share with 5 of your accounts. So that's 45 times 10 and that's 450. And that's not even counting 3.5 flash, or any plethoras of models.

https://preview.redd.it/075y3i1fkebh1.png?width=734&format=png&auto=webp&s=9716f2e0f826477cba59c2ca648cc8824002b85c

Hi everyone,
I’m currently researching Gemini 3.1 Flash TTS Preview as the primary TTS engine for a long-form audiobook/storytelling application.
So far, the biggest challenge I’ve encountered is voice identity consistency.
My setup
Model: Gemini 3.1 Flash TTS Preview
Voice: Kore
Official Gemini API (Node.js)
Same API key
Same voice
Same prompt
Same text
Same parameters
The problem
When I generate exactly the same text multiple times, the voice does not change gender, but the voice identity changes noticeably.
I’m referring to subtle differences in:
Timbre
Tone
Speaking style
Delivery
Overall vocal character
It still sounds like “Kore,” but more like different recording sessions or different voice actors trying to imitate the same voice.
For long-form narration, this becomes very obvious after stitching multiple chunks together.
What I’ve already tested
I intentionally tested many different approaches:
Generating the exact same text multiple times
Different chunk sizes
Emotion tags
Synonym emotion tags
Natural-language performance directions
No emotion tags at all
Different prompting styles
None of these significantly improved voice consistency.
My question
Has anyone successfully built a long-form narration or audiobook pipeline using Gemini 3.1 Flash TTS?
Specifically:
Have you found a way to keep the voice identity consistent across multiple API calls?
Is there any hidden parameter, seed, or context mechanism that helps?
Does Vertex AI behave differently from the Gemini API?
Are there any prompting techniques that actually improve consistency?
Or is this simply a current limitation of Gemini 3.1 Flash TTS?
I’m not trying to clone a custom voice—I’m only trying to keep the built-in Kore voice sounding like the same narrator throughout an entire audiobook.
Any insights or real-world experience would be greatly appreciated.
Thanks!

I've seen many people disregarding any criticism of Gemini as if it were just "hatred" or lack of ability to write prompts. But honestly, this explanation doesn't make sense.

If a person has been using Gemini for months, developed a workflow, learned how the model responds, and suddenly, after an update, notices a drop in quality, he has every right to complain. This is even more true for those who pay for the service.

The argument that "the problem is your prompt" ignores a very simple fact: if the same prompt worked consistently before the update and now produces worse responses, there has been a change in the behavior of the model. It is not reasonable to automatically blame the user.

What makes these criticisms more relevant is that many users are not complaining about a single thing. They are describing similar patterns: loss of context in long conversations, the need to repeat information that had already been memorized, more superficial answers to complex questions, worse programming performance in certain cases, more refusals in legitimate requests, behavioral changes that made the results less predictable, and the feeling that the model began to "think less" before responding.

When many people independently point out the same types of regression, it deserves to be taken seriously.

Another point that generated a lot of frustration was the issue of usage limits. Many subscribers hired the service expecting a certain usability and, after the changes, began to find more restrictive or less predictable limits. For those who depend on the tool to work, study or program for hours, this represents an important change in the experience with the product.

No software is immune to regressions. This happens with operating systems, browsers, applications, games and also with AI models. Updates can fix problems and introduce others. This is part of software development.

I also see people saying "in my account it's perfect". Great. But this does not invalidate the experience of those who noticed a worsening. Users use Gemini in very different ways. An update can benefit certain use cases and harm others.

Another frequent argument is that "those who complain should learn to write prompts". But this reasoning ignores a fundamental point: if a user already mastered the tool and obtained excellent results before the update, it is natural to expect this level of performance to continue. If there was a regression, it is legitimate to question it.

Those who pay for a service are not just buying access; they are buying an experience. When this experience worsens, the customer has every right to demand improvements. This is not "crying". It's exactly the kind of feedback that companies need to receive to evolve their products.

In my opinion, these complaints will not disappear as long as many users continue to feel that Gemini no longer offers the quality they paid for and were used to. On the contrary: they will probably continue until the product returns to offer a performance that meets the expectations of those who use it daily.

In the end, reasoned criticism does not harm a product. They help identify regressions and direct improvements.

What doesn't help is to discard all criticism saying that "the prompt is wrong" or that "the problem is the user". If a significant number of people started reporting similar problems after an update, it's more worth investigating these complaints than simply assuming that everyone unlearned how to use the tool overnight.

It sucks for coding - yes. The reasoning is limited - sure. But it’s really good at retrieving specific information, so it encapsulates the idea of quality over quantity. No other model compares to it.