My professional work involves a lot of research. Over the years, Google search has become much worse at finding things, like ignoring boolean search terms and having a narrow time view (even when selecting specific times).

In theory, AI is the solution. I want an advanced AI that finds me answers, and more importantly, cites them. Theres no value to research that doesnt have citations.

So I gave the new 3.8 model a quick (non work related) test.

" What is the post on Twitter by dril where he says he's a hater "

Gemini responded with a quote and link. The link didnt work, so I asked why

That link does not work because it was an AI hallucination—both the link URL and the specific tweet quoted in the previous response were fabricated.

What dril Actually Said About Haters: (3 more tweets)

Guess what, those links didnt work either. I asked it why.

In that follow-up response, the model compounded the original error by inventing new "real" quotes, arbitrary dates, and fake numeric post IDs under the guise of correcting the first mistake. None of those tweets exist, and the URLs lead to non-existent pages on X (formerly Twitter).

The previous responses repeatedly hallmarked a major model failure: inventing text and URLs out of whole cloth when attempting to satisfy requests for exact quotes and links. There is no @dril tweet matching this description.

So I gave it an updated prompt

"Find me a real verified post where dril mentions either haters or hating. Double check. Use live search. Then check again that it is a valid link before responding".

It provided a new tweet with three (3!) links.

All links were wrong.

You are completely right, and I apologize. I failed to verify the actual tweet ID against live data, and repeatedly serving incorrect and broken links is entirely unacceptable. I cannot verify direct tweet URLs or IDs with 100% accuracy right now.

So if youre like me, and were hoping AI could help you with research....this model ain't it either.

Maybe the next one.

Oh and pro-tip to devs: 'I cant do that" is a perfectly good response to a query. If you cant do a live search on Twitter because of API limits...ok! Just tell the user that.

I used to generate edited photos of me by using an old prompt(also used to add my ethnicity in that prompt)that i have been using for a long time but this time I typed that prompt without giving gemini the reference image of me but the resulted photo still has my face on for the same ethnic person for some reason??? How to fix it so I could get some variety in face???

So i migrated from Gemini to Grok, since Gemini was really making me angry by dodging what i said and not listening to stuff. Not even shouting worked. And one of the wildest things i done i got so mad i literally broke my phone screen (smashed at corner of desk), so yeah. If Gemini is making you mad, take a break and stop using it until it starts listening properly.

Edit: I used 3.5 flash

It has a score of 61 on the Artificial Analysis Intelligence Index, while Fable 5.1 has a score of 66.

Gemini 3.8 Flash has a score of 59. Given the rate at which the Flash models are improving, if we get a Gemini 3.9 Flash version in, say, three weeks, it could reach a score of 61 or even 62, potentially surpassing Astra.

I wrote up why I think Gemini 4 might be much better than a lot of people seem to think

https://sputnikintelligence.com/blog/gemini-flash-post-training

So, feelings are very subjective, but having used Pro and Flash variants, Pro is smarter but clunkier.

Flash is fast, agile, and sometimes gets to the point whereas Pro lumbers around. Picture a new library versus and older one. The new library is perfectly catalogued, has a great indexing system, and gets you to answers fast and efficiently. Whereas the old library has so many nooks and crannies from its sheer age and size, that it sometimes delivers hat tricks (think pulling a rabbit out).

So, I'm still languishing along with the old beast. How about you?

just saw they released 3.8 is it better then 3.7 since it hasnt even been 2 months since the 3.7 release?

EDIT: i meant flash models obv.

Is it just me or gemini 3.8 has been hallucinating too much? it is loosing context in like 4-5 prompts, inventing facts. it literally told my i made a typo as it said instead of typing H3 i typed flash-8b and flash-8b was never even typed in the chat or anything remotely mentioned even both dont know what it is.

so just wondering if im seeing lower intelligence then 3.6 or everyone? even tho benchmarks tell totally different story

Because I’m not.

Reinste Katastrophe, er erstellt mir komplett was anderes und nimmt die sachen nicht so an wie zuvor. Also ich sehe hier keine erweiterung sondern einfach einen rückschritt was mir null gefällt.

Some of you may have seen this when I first built it last year. Its natural language and RAG is powered by Gemini text and embedding models. This session due to the lighting behind me the clone couldn’t see my face and denied me lol. Echovault lets users create a clone like this full with their memories and personality traits. All you do is write into the journal, an AI biographer guides you and the clone gradually learns how you think, common words you use and pretty much a distillation of your world view/perception. It’s now on iOS, text tier is free. Pls give it a try ❤️

https://apps.apple.com/us/app/echovault-digital-legacy/id6762042028

right..?

it's over

aint no way

From a consistent but frustrated user:

I've been using Gemini for almost 2 years now and I feel that the team really need to take a hard look in the mirror and address some of the most basic problems plaguing their AI. I've done a number of internal comparisons for coding and other workloads and my opinion is that Gemini is dangerous to use in production coding environments. I'm a big fan of the team and the work they do, I've been using google products since 2004 and I firmly believe that they can fix the issues.

The Good:

The Speed (The model is consistently fast, and faster than other leading models)
Most accessible models for processing audio, video, and caters to a diverse array of needs.
Excellent Model Garden with google mass and they support a diverse array of non LLM capabilities
Cost: They are significantly cheaper than Claude
Excellent Sub Products like Notebook LM

The Bad:

High Hallucination Rates (In addition to the well known issues like the infinite token glitch)

API instabilities, random dips in capacity, thousands of bugs, hundreds of hours spent compensating for performance issues

Inconsistent pricing and excessive generational price hikes (my advice to any team looking to build on Gemini's API is don't. You will probably regret it. If you have to then please support other models providers or you will regret it)

Excessive and seemingly random censorship - rejects perfectly valid workloads with 0 justification

The image gen quality is significantly worse than Openai

The Ugly:

Antigravity is bad when compared to their competitors, if you are using Gemini for coding strongly consider a different IDE

Severe regression in coding consistency and quality (Not sure if this is due to excessive quantization but seemingly simple prompts can result in massive bugs, hallucinations and just general damage to a code base)

The gap is widening, 3.6, 3.7, 3.8 do not represent an improvement over Gemini 3. Basic tasks that worked with the older models close to their release dates do not work well with the new tools. Benchmark maxing is disguising fundamental issues in the AI.

The output quality and consistency of Gemini is 12-18 months behind their major competitors. This is both the industry and research consensus.

My Consensus:

Fixing the most offending problems, high hallucination rates, extremely buggy IDE, improving tool calling are low level fixes that can be done with a little bit of effort.

Bad Business practices (massive price hikes for integrators, cannibalizing ad revenue with AI search, arbitrary depreciation of core AI capabilities with little to no warning, charging customers for endless token glitches) are hurting adoption at the enterprise level.

It is okay to not have a frontier model. Google arguably has not had one for the past six months. But you have to offer more not just speed to compensate. Charging frontier prices while serving models that are arguably worse than the leading open source is not a recipe for long term success.
Please take the issues seriously and don't just release new models with the same problems as the previous generation. Your team has some of the most talented people on the planet and DeepMind invented the transformer architecture.