I've been using Gemini to live chat in Afrikaans and do impromptu Afrikaans grammar lessons during my solo drives around town. It is phenomenal at speaking the language - like, it really shocks my family members when they hear it.
This is probably the most joy I get from any of my usages of LLMs/AIs. It's been really, really nice getting to speak my language regularly again. =)
So, I'm excited about this release and live chat getting better. I also hope the other frontier labs pick up niche languages like this as well so that I have more options.
I once asked it to summarize The Hobbit in Catalan to explain it to my daughter before sleep. I was expecting a lot of mistakes as I see regularly if I ask anything in my native language when using GPT or Claude, but it was surprisingly good. I was going just to kind of skim ahead and retell it my own way, but ended up almost saying it verbatim because it was good already.
She loves Zelda so I asked it to explain the story of Breath of The Wild keeping the original names, and to make it fun, etc.. I was surprised again. I did retell some bits in my own style and taste but it is very convincing.
I haven't tried Catalan on newer models like GTP-6 Astra or Fable tho. We have all these benchmarks based on software development, and AGI, etc.. but it would be cool to have some language benchmarks for different communities.
As I work in english and use them in english, I wonder if using LLMs in a different language to code renders a different result as well. Like, if some of these benchmarks were made in other languages, would the result be similar.
You don't. They'll tell you.
Most of the time I don't need what the bench tests and I'm not really giving them completely ambiguous tasks without any refinement.
I only find marginal differences between models at this point and it almost feels like personality quirks in each model than anything.
Anecdotally I'd rate Gemini behind Claude and OpenAI models at fiction and I can't find any benchmarks showing Gemini is the clear winner at this task.
I doubt they even intended it to be, but it seems like I kept going from resorting to 3.5-3.8 (over time) to realizing that Claude and GPT, while great at Python, will make rudimentary mistakes with R; even when they compose giant complicated R code.
It reminds me of a pedantic grad student.
I'm worried in their push to catch up on the SOTA front, it's going to lose that natural sounding touch it currently has.
My ChatGPT env only says "low", "medium", "high".
Is this a "pro" thing? I have totally no idea what I'm talking to, so actually I'm thinking of stopping my plan. Gemini and Claude are much more clear about it.
Anyway, I like the speed at which Gemini responds so indeed for simple things it is preferable.
I’ve been using Work for all my queries, since it seems to just be the same interface as Chat but with more features. I don’t understand why they’re two separate things.
Their AI leadership team has taken some hits recently too, in the form of departures. I believe when they get their bearings they will be competitive again. 3.8 Flash has been a great model for me.
Meanwhile Claude and Astra like to couch all their agreements with caveats and provisos.
Sometimes that's what being smart sounds like.
Someone confident but incorrect, can often sound more convincing than someone with actual expertise. The expert must add caveats/hedge, because those are the facts on the ground, whereas the person reciting google can be entirely confident.
Of course the people judging aren't experts, so they side with confidence and simplicity. Heck, just writing shorter replies on Reddit is rewarded. Nobody reads the articles, let alone a paragraph-long reply.
That all being said though, there are limits. Sometimes LLMs on high-thinking go off on full tangents based on little, and don't have the self-awareness to bring it back.
To an expert communicating with a layperson is a form of compression. You must turn some very complex idea into one that you suppose the other person can grasp given their limited frame of reference. It's always lossy, and you have to guess how much you can remove without sounding patronizing or being inaccurate. It's tough, and the more you know the tougher it gets.
Ever done that "explain what happens when I visit Google in my web browser" interview question?
A sales guy will answer in a sentence. An engineer might be able to talk about it for several days and still not be sure they didn't miss anything important. That much knowledge can actually be detrimental to communication.
When you're a ChatGPT Projects or Claude Projects user, those caveats and provisos are your worst enemy because they'll change caveats into hard rules (either for the session or committed to memories) and you end up in absolute hell having to make it investigate to figure out why it can no longer produce anything but read-only pre-check code that never actually does anything but keeps performing stupid safety checks.
> This mirrors how Apple has always segmented Pro vs. non-Pro iPhones: base models got LTPS panels while Pro models got LTPO, and only with the mainline iPhone 17/17 Plus did that gap close the standard versions previously lacked the smoother 120Hz ProMotion technology and the always-on display feature, unlike the Pro models — the 17e is the one model line still using the older, cheaper panel.
(emphasis mine)
I mean, I can guess what it is trying to say, but who RL'd this nonsense?
Only worked in a 1:1 in a quiet place. Still, can't complain for free.
Lately it became load-bearingly-reality-difficult to not only read, but to comprehend the Claude output
And like, it does this despite it speaking in extremely dense math, which both makes it sound correct and requires a lot more effort to prove when it is wrong... yet, it isn't actually correct more often, and so that time sink just isn't worth the benefit. I then think many people--including people who can speak math (as can I)--just stop bothering, as if you come across a human who speaks like this it probably does correlate with slow and careful thought that helps prevent errors.
Instead, Claude has the mistake rate of an advanced beginner impossibly combined with the language of an expert professor; and we as humans just aren't good at that combination: it becomes very dangerous and makes it take longer to spot its egregious mistakes and trained-in biases. If you have to use Claude, I thereby claim you really need to have a team of not-Claudes to help insulate you from this, and Gemini (while being a bit senile) is a lot more collaborative and approaches problems in ways that makes it harder to get tricked.
(To translate this into more of an engineering analogy: Claude always feels to me like the engineer who put more effort into learning how to program in functional languages than into how to actually develop working code, and then confidently presents you answers in Haskell or Lisp that never quite work. To find their errors is then very costly. In contrast, Gemini feels more like a Java or Go developer who knows they are a cog... that's helpful!)
I've set my documentation sub agent to Gemini and my code agent to Luna
On my TODO is try and run all of the analysis pipeline in dense "machine speak" to save on tokens and just let Gemini sort it out at the end.
Copes well with thick accent, voices are pleasant and latency seems low.
Oh and I can actually use it on a workspace account - which for most of the recent releases was an account stuck in limbo. Not personal enough for personal offering, not enterprise enough for enterprise.
Well done G - will definitely be using this
And looks like one can trigger live mode via siri
I can't think of a better general purpose model than 3.8 flash right now. It also writes more naturally than the other big models too.
However, if I want it to DO something then Gemini is in absolute last place. I don't trust it for anything more than renaming files that I don't care about very much or extracting data (though it's too expensive for data extraction at scale).
It’s for this exact kind of scenario where a random question pops in to my head.
Plus, it’s the most grounded by real live data of all the chatbots.
Silicon Valley people are majorly sleeping on Google Search AI Mode.
People use text with LLMs but it's great to have a high fidelity "analyze this image"
I think they’ve made a shrewd move in focusing on search integration and everyday users (Gemini app) vs software power users. They have their corner and nobody is really competing with them, plus it feeds directly into their existing revenue stream.
Google in a sense won (me over) like that. I also expected them to brute force their way into everything and dominate. This is how it played out though. Image generation is great as well, but ChatGPT one is more lenient on copyright and nannying - for example when my kid asks me to "take a photo of him and Sonic". Gemini cops out either because of the kid or Sonic, disappointing us both, but ChatGPT can be.. persuaded.
Ask yourself, how does Google -- a company that famously does everything -- benefit from SWEs outside of Google having access to powerful coding models? They would just be competition.
Famously, Google just eventually discards almost all businesses that don't have the same fire hose of revenue that ads does. Selling coding plans isn't something they are going to want to do.
Google is clearly motivated to make better search and information finding tools and stuff that will ultimately drive users through their existing search/ads/youtube ecosystem. That's really why they're in Android, that's why they do Chrome. Everything else with them is a sideshow.
Google is also full of beancounters obsessed with data centre quota and resourcing. Even massively profitable ads projects have to justify and fight for it. (Source: used to work there).
I can't think of anything less resource & revenue sensible than providing outside parties access to your TPUs for the purpose of letting them write stuff which could just end up competing with you.
Yes, maybe as part of their cloud business, selling token access could be useful money. But I doubt they'd tune it for coding.
Bragging rights to say they have a SOTA model. I guess that was more like Google of 10 years ago with moonshot projects. Nowadays, yeah, perhaps if it's not helping sell ads, it doesn't make sense.
Catch is, even Googlers internally do not have access to top-tier models. (or did not until recently, when apparently Claude was made accessible to the SWEs internally).
Funny enough, after trying it, I went back to G3.8
As opposed to what, them not having it and burning money that isn't their instead like openai and anthropic? At least Google is feeding itself instead of having to create a bubble to stay alive
Big rich companies take on debt for reasons that are sometimes inscrutable from the outside. Recently, they have been borrowing for ~5%, about a half point above what the US government gets for 10-year Treasuries.
Apple has been financing operations with debt for a number of years as part of a complex optimization plan.
No, Google is not broke.
Some expert wall street analysts discussing what they found and how they dissect things, have a healthy skepticism of Big Ai
If my quick search is correct, Google is sitting on a quarter trillion dollars in cash and marketable securities. They could keep doing the negative cash flow thing at this scale for another decade.
Excited to try this out! Shame on Google for not releasing Gemini 3.8 for Google AI Plus users yet, though.
Nothing but constant errors with cryptic messages.
Is this a pure TPU infra? Really high performance solid intelligence.
Looking forward to where this can go.
There's one or two I find more understated but I would love a 2026 SOTA V2V model that speaks clearly but without the artificial personality layered on.
Human interaction/theory of mind relies so much on non-verbal clues for interpreting emotion/intent and so for me having those neurons firing constantly while talking to an LLM just for an emotional no-op is exhausting to put up with for more than a couple minutes.
There's one male voice that would make me assume someone was sarcastically mocking me if I was talking to an actual person because it's just so over the top.
I don’t want to be aroused by my turn-by-turn street directions, thanks.
However, the "Extended Thinking" should be renamed to "Slightly Extended Thinking". Considering that it's the maximum thinking option for Gemini Flash in the chat UI, it doesn't actually think a whole lot, leading to an uncomfortably high number of incorrect/poor replies.
Let me tell you unlike every other mentioned model Gemini 3.8 Flash trial had to be reverted the same day. Instead of simply delegating tasks it would invent additional requirements and implementation details it knew nothing about and no amount of convincing not to do it would work. That's the first time a model failed on me so spectacularly despite having practically same Artificial Analysis Intelligence Index as another model that just worked (and higher than working DS Flash).
The reason I think it is relevant is: Live is likely even stupider model in every way possible (except hearing better than separate STT). So beware using it for agentic scenarios.
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As PrimeTime said; these are the guys that invented the 'T' in 'GPT', that deployed their first TPU in 2015, that is using billions on AI - and they are beaten by 300 people startup named Moonshot AI even. People are going to write books about this complete fumble.
My advice is to listen less to brainrot 'influencers' that optimise for engagement through sensationalism.
They have "unlimited" resources and has researched AI since the very beginning - PageRank is a form of AI even. And still, Gemini is behind Claude, GPT, Grok, Muse, GLM, Kimi and is maybe on par with DeepSeek?
As I said, it is embarrassing.
No one is behind grok. It literally has "be funny and irreverent when appropriate" (whatever the hell "when appropriate" means for them) baked into the system prompt. To me, that is all you need to know about how useful it is.
No serious people use it and the numbers bear it out tbh. It has the smallest market share of the "big companies" for a reason - and it's by a very, very large margin (~2.5% last I checked).
And Gemini is kinda good enough at everything. Never the top, but it is decent at every task, and it is much faster than Kimi K3 and significantly cheaper. Kimi is very focussed on coding, Gemini isn't.
More importantly, it natively understands text, audio and video. If/when we are able to make the jump to robotics, this becomes essential. As you say, Google has a lot of deep background and deep pockets, they are able to make more of a long play. No idea if it will pay off, but it is way to early in the game to count them out.
And they're making money doing it.
Perhaps they don't have the best coding model right now (although 3.8 flash is arguably SOTA at some benchmarks), but is that the be-all and end-all of AI? Only coding matters?
Given the very high margins on inference, once volume is large enough the other can also start printing enough money.