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

when i try to generate images in google flow nano banana pro, it says "We noticed some unusual activity. Please visit the Help Center for more information."

I know that this happens if we try to generate images too quickly this is happening even after only 1 image generation.
I have to wait 10 to 20 mins to generate images again but this happens again.

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!

TLDR: To save money, AI companies are secretly compressing, rationing, and swapping out models behind the scenes, making your everyday chatbot noticeably dumber and lazier.

I asked Gemini why individual users are noticing a degrading experience with AI chat bots. Here is what it said:

"​It isn't just a figment of your imagination; individual users are absolutely experiencing degraded performance, and it is a direct consequence of the aggressive cost-cutting and optimization happening behind the scenes.

Here is exactly how the push for financial efficiency ruins the individual, day-to-day user experience:

1. Stealth "Quantization" (Making the Brain Smaller) When an AI company trains a massive, brilliant model, running it at full capacity is astronomically expensive. To cut costs, companies use a process called quantization. Think of it like compressing a high-definition video into a lower resolution to save file space.

They compress the mathematical weights of the model so it requires less computer memory to run your prompt. While it makes the model incredibly fast and cheap for them to host, it strips away the subtle nuances. A quantized model is far more likely to miss complex instructions in your prompt, give generic answers, or lose its "creative edge."

2. Under-the-Hood Model Swapping The interface you look at might say the name of a premium model, but behind the scenes, providers frequently route your text through a mixture of models depending on demand and server load. If servers are busy or costs are spiking, your prompt might be dynamically handed off to a smaller, "distilled" version of the model.

This is why a tool might feel brilliant on a Tuesday morning but incredibly dense and repetitive on a Thursday afternoon.

3. Squeezing the "Thinking Process" For newer models that use an internal "chain of thought" (where the AI explicitly reasons through a problem step-by-step before answering), every hidden thought costs money. To keep individual subscription plans profitable, companies place strict limits on how long the model is allowed to "think" before it forced to spit out an answer. Cutting that reasoning time short leads directly to flatter logic and more frequent hallucinations.

4. Over-Aggressive Caching To avoid paying to process your entire prompt from scratch every single time you hit enter, systems rely heavily on prompt caching. The AI tries to match your request to previous patterns it has already processed. If it relies too heavily on cached data, the conversation loses its immediate context. The AI starts giving you answers that feel like they are recycling old parts of the conversation rather than truly listening to your latest instruction.

So, while the companies claim their core models are technically more powerful than ever on paper, the version you actually get to play with in a free or standard $20/month chat box is being constantly trimmed, compressed, and rationed to keep the provider's margins out of the red."

https://preview.redd.it/lbckzfghqebh1.png?width=1323&format=png&auto=webp&s=10872cc4c2ef3e9e9666fd866c81936e089ec015

bro what the actaul

How is Claude better for writing as compared to Gemini? I'm changing from ChatGPT to maybe Gemini Advanced also $20 a month as I'm writing a trilogy (soft sci-fi) but I found GPT slowing down having to copy/paste some from the last chat into a new one to keep continuity. I can only afford one AI... Also GPT would freeze. It also was praising my writing through two books of my trilogy. All writers like to think their writing is good but really I'm not that good. It happened enough I decided another reason to quit GPT because I needed a frank opinion.

I guess Gemini can cross reference chats where if it was a chat from long ago it was effectively in its memory and GPT doesn't do that. Also I struggled with GPT for weeks, giving up, going back... to get the margins right with my front, back, and spine covers of my book one paperback. Suddenly one day by pure chance GPT in seconds, and I could see "Python" flash as it computed and had all my margins fine! I asked GPT what it did and why didn't it do that weeks ago and GPT said it took a mathematical approach as that was what was needed to get the margins right. How could I know that? GPT did not utter a word about that and it cost me weeks of going in circles with GPT!!

Also Gemini is suppose to have one million "tokens" capacity I guess a lot more than GPT for storage where GPT would delete chats beyond a certain point. Gemini won't delete my old chats because it has a much larger storage capacity than GPT... I'm still doubting my judgement but need AI to help with my blood labs and medical, and with a big decision for an expedition ebike to use on my around the world journey. Does Claude do all of that too? I'm mainly a writer but I guess Gemini isn't a good choice for writers? With my case, which way would you go? Thanks!