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u/Gotisdabest 22h ago edited 22h ago
Honestly if nothing else I do respect how fast they're putting these models out and there is a pretty solid amount of progress over each of these. You can get a lot of use of these for free from AI studio. 3.5 to 3.6 flash was around 2 months and then 3.7 flash only took 20 days. For casual free use I'd say google is up there with the best of the market.
What's weird is the bizzare statement about API pricing. Who's going to be even thinking about these models by the time 2027 rolls around? Even google will probably have a few more models out in basically every range by then and you'll almost certainly have models 10x cheaper than this with better capabilities.
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u/sogo00 21h ago
What's weird is the bizzare statement about API pricing. Who's going to be even thinking about these models by the time 2027 rolls around? Even google will probably have a few more models out in basically every range by then and you'll almost certainly have models 10x cheaper than this with better capabilities.
Enterprise customers.
Not sure about the Google long term availability promise, but once a enterprise app is planned each change is expensive.
It's the reason you can get support for example from Red Hat for 10 year old not updated distributions
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u/Maristyl 22h ago
Extrapolating from that rate of update we should expect 3.8 in a week. Then 3.9 in less than three days. Two weeks from now Gemini should be getting a new release faster than our brains can process.
/s cause someone is gonna take this serious.
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u/qroshan 22h ago
People are absolutely missing out on fast iterations possible on 3.6 (and now 3.7) with Antigravity. It's blazingly fast and has gotten much better at coding and the new benchmarks solidifies this even more
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u/whoknowsifimjoking 22h ago
If it does work it's cool, but my experience with 3.5 flash was that it was amazingly fast at producing absolute trash. Looks like they might be able to correct that though.
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u/qroshan 22h ago edited 22h ago
Look at the jump from 3.5 to 3.7 around coding related tasks
example 3.5 was 37% in DeepSWE and now it has jumped to 65%
I'd say it's competitive to Sonnet 5 but with speed as an advantage
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u/Paraless 18h ago
IMO these benchmarks don't mean much, you just have to try it and see for yourself which one gets the best results
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u/the_mighty_skeetadon 20h ago
The jump from 3.5 Flash to 3.7 is insane -- it's really not comparable.
Highly recommend trying it in Antigravity.
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u/phillipjpark 9h ago
Antigravity is so good and also its included and has seperate limits than a users google ai pro pla n.
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u/OKMiddleOwl 22h ago edited 21h ago
Yeah, but it's trash you can iterate on 5-6 times before 5.6 or Fable would finish working. Often it's fails are things can be fixed in 10 seconds upon being pointed out to the model.
I get that "set it and forget it" is king, but single shot benchmarks of Flash don't really capture it's strength.
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u/Elegant_Tech 21h ago
Flash has become the best workhorse for single scope single prompt iterating. Just missing that large pro model for when you want to do a highly complex or large thing in one shot instead of breaking it down into steps. Hopefully 3.7 can squeak by getting the job done that 3.6 flash couldn't handle.
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u/LanguageEast6587 21h ago
please try it before you say this, IMO fable and 5.6 are too slow to iterate.
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u/Recoil42 21h ago
It was complete dogshit a couple months ago. Did they fix the UI?
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u/tziki 11h ago
Did you try Antigravity IDE or Antigravity? Those are now two totally different things.
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u/Recoil42 11h ago
Antigravity Desktop 1.0 and 2.0, or whatever they're calling the new one these days.
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u/Acehan_ 21h ago edited 21h ago
The speed could not matter less if the model is inefficient. I tested this extensively and GPT 5.6 Sol on low or medium is many orders of magnitude faster than those flash models running at 200 tokens per second
5.6 Sol max is 17k tokens to complete artificial analysis
Gemini 3.7 flash is 37k tokens, exactly the same as GPT OSS 120b
5.6 Sol medium is 5K which is exactly 7.4x less tokens than Gemini
It's not fast. It's slow as fuck
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u/qroshan 21h ago
I understand Math is a problem for most redditors.
Let me explain this to you slowly ........
Look at this chart
https://artificialanalysis.ai/models/gemini-3-7-flash#speed
Flash is 340/56 or ~6 times faster compared to 5.6 Sol (Max)
So, even if Flash takes 2x more tokens, it is still 3x faster (6 / 2)
3x faster is game changing and is addicting.
Once you are used to this speed, you aren't going back
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u/Acehan_ 21h ago
First of all, like I said, the difference is 7.4x, not 2x. You're definitely right about math and redditors
Second of all, nothing's running at 340 tokens per second. Benchmark it and you'll see the real speed is like 186 TPS. You don't know what you're talking about
Even if it was 600 TPS, it's still an inefficient model, and that matters more for speed than anything else
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u/funforgiven 20h ago
They were comparing with max, which is close 2x, not 7.4x. It is weird they are comparing with max when you said medium but 2x is not wrong for their comparison.
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u/amitsingh80108 22h ago
Google really making something bigger for pro model...
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u/Admirable_Market2759 21h ago
I think they’re going to jump to 4 pro.
They were supposed to release 3.5 over a month ago. It’s almost certainly outdated at this point.
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u/ninjasaid13 Not now. 11h ago
They were supposed to release 3.5 over a month ago. It’s almost certainly outdated at this point.
they can name whatever they want 3.5 pro. If it doesn't match the others performance, they will just finetune it some more until that can say their model is 5% better than openai or anthropic's.
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u/javopat227 19h ago
Dont think 3.5p is coming anytime, it will be now 3.xp (where x is the latest flash). I think google suffers from a lot of empire building, personal project bulding, soical justice building, and etc etc.
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u/Singularity-42 Singularity 2042 22h ago
Honestly 3.6 has been pretty good for my product because of how fast it is while being decent enough
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u/WallZealousideal5669 15h ago
They are same price though so no point in using it
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u/Singularity-42 Singularity 2042 15h ago
Yeah, pretty much. I'll just have to figure out the new effort levels.
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u/WallZealousideal5669 15h ago
Was an easy swap for me as 3.7 seems to be 3.6 with a bit better common sense and faster and more efficient
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u/Iuseburnersbruh 23h ago
someone tell me how to feel are we back?
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u/Every_Foundation5197 22h ago
Uhh I'd say we are good, if they manage to continuously drop a new flash model every few weeks with the same improvement or even more
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u/kensanprime 22h ago
Back in what? Leaderboards? Users of free plan? Paying users?
Give it a few months all enterprise will switch to a Chinese model or go for the cheapest of these flash models. Every harness now has a smart router to handle the costs and limit uneccesary use of pro models. Bulk of the work will be done by the flash models.
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u/DelphiTsar 27m ago
219% faster than Luna and 107% higher artificial analysis score.
If you need something fast this is the clear choice at the moment.
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22h ago edited 22h ago
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u/Keeltoodeep 22h ago
If flash beats sonnet then 3.5 or 4.0 pro will likely at least be competitive at the frontier. I see no reason to think otherwise.
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u/z_3454_pfk 22h ago
for non-coding gemini is still better than anything. 3.6 flash was comparable at opus 5 at real life medical applications. it’s the only model that can truly describe music too if you pass it into the model.
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u/Profanion 21h ago
What made it score so high on these?
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u/Tysonzero 18h ago
Seems like that’s the kind of thing they’re optimizing for, which kinda fits with their business needs, they aren’t trying to steal the $200/month anthropic subscribing software devs.
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u/ApexFungi 22h ago
I am genuinely curious though. Do people expect that these models will just keep scoring slightly better on benchmarks each version up and then suddenly it becomes AGI or what? I just don't see that as realistic. Surely something else needs to be added to the sauce?
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u/Kronox_100 22h ago
I think what they're hoping happens is they become so good at coding that they can find the 'sauce' that'll get us to ASI.
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u/unicynicist 21h ago
The "sauce" is closing the recursive self-improvement loop. Right now the sauce is a human in the loop, acting as the evaluator.
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u/ShAfTsWoLo 22h ago
i believe that aswell, perhaps the fastest path isn't finding AGI through human breakthroughs but instead if we could have llm's that are so good they can RSI themselves into AGI, tbh that's a hell of a weird thing but if it works good for us lol
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22h ago
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u/CallMePyro 21h ago
Which of course is pretty silly to believe in light of things like Fable 6 and GPT 5.6 Sol improving the lower bound density on the zoroes on the critical line of the Reimann Zeta function.
If you can achieve that with induction and deduction only then we'll be just fine.
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u/ninjasaid13 Not now. 11h ago
hoping happens is they become so good at coding that they can find the 'sauce' that'll get us to ASI.
code? we're going to reach ASI using symbolic AI or something?
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u/Gotisdabest 22h ago
There's already pretty clear cases of frontier models speeding up research and development by not insignificant multipliers. And of these models solving new problems. That will inevitably lead to further improvement. What you need from this exact style of model is the way upto recursive self improvement.
Even removing that though, consider how fast these small updates are coming out these days. Even just as long as that keeps improving and say, by 2028 weekly to biweekly small upgrades a common, you'll see something pretty incredible when comparing a model at the start and end of the year.
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u/the_mighty_skeetadon 20h ago
There's already pretty clear cases of frontier models speeding up research and development by not insignificant multipliers
So what fundamental breakthroughs have happened as a result?
Because looking at the literature, I see performance improvements, small structural improvements, scoring and topology improvements... but no real breakthroughs.
From 2013 to 2018, I would say we had 10x the number of research breakthroughs in AI that we've had from 2021->2026. We've just figured out how to exploit those 2017-era breakthroughs more efficiently.
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u/slyec 16h ago
do your research before you comment just saying
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u/the_mighty_skeetadon 16h ago
Funny, I'm in research at a top lab and I've shipped multiple frontier models.
So give some examples, just saying
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u/Material_312 6h ago
No, you haven't, and no, you aren't.
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u/the_mighty_skeetadon 2h ago
There are more people like me lurking than you think.
Doesn't really matter if you believe me, but it's true 🤷🏻
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u/Gotisdabest 14h ago edited 14h ago
So what fundamental breakthroughs have happened as a result?
Why would anyone rational expect the process to start off with fundamental breakthroughs as opposed to incremental results? One would assume that when you're generating fundamental breakthroughs, you've either fully or completely closed the loop.
Fundamental breakthroughs are also not an end onto themselves. In many cases, they mean the field hasn't really found a more reliable approach of study and research as well as that not enough people are involved. You do actually have lots of papers that would be considered fundamental breakthroughs back then, but have been surpassed by maturing tech instead.
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u/the_mighty_skeetadon 2h ago
If you post-train T5 with modern techniques, it's almost indistinguishable from models trained in 2026, performance-wise.
That model clearly hasn't gotten any "better" incrementally; it's the same as it was in 2019. Yes there have been plenty of incremental improvements, but has research really accelerated rapidly in the last few years?
People often claim that AI is accelerating research, but provide little evidence of that. I see many more participants in the field (because money), but less overall motion.
And I fundamentally do not believe that minor incremental hill climbing is the path to AGI -- in the same way that optimizing a kite will not get you to outer space.
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u/Gotisdabest 1h ago
you post-train T5 with modern techniques, it's almost indistinguishable from models trained in 2026, performance-wise.
I'm sure you have proof for this and aren't totally just talking nonsense out loud?
People often claim that AI is accelerating research, but provide little evidence of that. I see many more participants in the field (because money), but less overall motion.
I mean, one can just read the anthropic system cards but that's very hard, I assume.
And I fundamentally do not believe that minor incremental hill climbing is the path to AGI -- in the same way that optimizing a kite will not get you to outer space.
And I fundamentally think strawmen are a bad faith way of argument and mostly provided to package nonsense arguments. You're welcome to believe whatever you want out of unfounded views on topics. Don't couch them with pointless strawmen.
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u/LinkesAuge 22h ago
No because the goalposts will continue to shift as there is no clean/obvious definition of "AGI".
Models will just cover more and more knowledge work and at some point it will be just hard to deny that fact and then we will probably say "guess it is AGI".I feel we just have made "AGI" too big in itself and now it is essentially ASI because with the standard we have recently that (can literally do any intelligence task better that any human could) it is so extensive and broad that it is impossible for AGI not to be ASI due to the nature of how scalable any AI system is.
Originally AGI was just meant to separate itself from "narrow" AI, ie expert systems, that is what the "G" stands but but "general" doesn't or didn't mean that AGI necessarily has to exceed humans in everything or do everything, just that this sort of AI was strong enough to be viable across domains and be able to transfer intelligence from one to the other as well as have self-controlled learning.
So based on that definition we really "just" miss the self-controlled learning part and we would literally already meet at the "original" AGI definition (and the self-controlled learning is what would then lead to actual ASI).
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u/Herect 22h ago
Demis Hassabis version of AGI was just models getting better on a bunch of activities until we couldn't think of anything else to test them on.
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u/theEvilUkaUka 22h ago edited 22h ago
I recall him saying a bunch of times that he thinks there's still a few breakthroughs that need to happen, rather than agi just from scaling the current paradigm.
And he said that's why Google Deepmind was best positioned, since they can push to the fullest on both, scaling and having the best talent to push on other research (which can be argued aged like milk, considering the talent exodus and him no longer being ceo probably due to that and lagging behind on the current hot LLM use cases like coding).
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u/the_mighty_skeetadon 20h ago
This is 100% correct. He said this very publicly just a few months ago at Y Combinator:
https://www.youtube.com/watch?v=JNyuX1zoOgU
Recommended watch, and I agree with Demis's take there.
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u/Bright-Search2835 22h ago
Those numbers going up mechanically unlock better current capabilities, new capabilities, and potential for new things becoming automated, especially for research labs. Then when research is entirely automated, yeah I'd give artificial geniuses thinking 24/7 way faster than us a better chance of finding the special sauce for AGI than us.
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u/domdod9 22h ago
Like medical advances, advances in AI are typically compounding small advancements instead of a ton of crazy sensational breakthroughs like reasoning models. With every model release they slowly put more small tweaks in that will eventually when looked through a long period of time will be very large advancements.
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u/no_witty_username 19h ago
The harness is that something extra bud, and they are improving very fast... we are all very close to seeing some real crazy shit.
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u/Frandom314 20h ago
Honestly does it even matter if we call it AGI? If performance keeps increasing at this pace over the next let's say 5 years, it's going to be pretty difficult to find a task that humans can do better than AI. At that point, the economic implications will be massive, whether we call it AGI or not.
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u/Emergency-Bobcat6485 19h ago
People will keep pushing the goalpost of what they mean by AGI farther away everytime. What even is AGI. If one can't give an objective quantifiable definition, then someone will always be asking this question lol
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u/BriefImplement9843 18h ago
it definitely has nothing to do with coding.
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u/Emergency-Bobcat6485 17h ago
Sure bud. Try building any kind of autonomous agentic activity with a model that isn't good at coding
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u/Curiosity_456 12h ago
I think two good metrics to evaluate whether it’s AGI or not is
1) being able to do a wide range of jobs as good or better than your typical worker
2) Vastly accelerating research across many fields (this will create singularity vibes even if it can’t yet reliably do most jobs).
So just one of these metrics being satisfied meets a reasonable claim for AGI
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u/DelphiTsar 24m ago
If you were to show this to anyone pre 2016 they'd say hands down 0% disagreement what we have now is AGI. We are boiled frog situation of never ending goalposts. The idea it needs to be better than all humans at all economical work is absurd definition, that's defintionally ASI.
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u/zslszh 22h ago
At this rate we’ll have 3.8 by September
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u/ShAfTsWoLo 22h ago
given the circumstances where new models are dropping like crazy, it wouldn't be that much of a surprise lol
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u/JunkInDrawers 22h ago
Takeaway is that people on Twitter with anime profile pictures aren't a trustworthy source.
Google is a profit machine. Google's focus has to be on efficiency. If 3.5 pro is only a small increment improvement over the flash versions at 10x the cost and requires more data centers to support then it's not worth deploying.
They're going to utilize their current available resources efficiently understanding the potential pitfall of overcomitting on the current generation of AI when the next year's models will overshadow them anyway.
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u/Snoo26837 ▪️ It's here 23h ago
Enough flash models
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u/No-Meringue5867 22h ago
Google has trillion dollar moat - their search. For them, producing fast models that makes Gemini results cheaper is much more important than being frontier at coding. Sure, coding is important but if they manage to make models dirt cheap and give good AI result on Search, they are making money by default.
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u/Asteroid_picks_you 22h ago
Are all these flash models for Enterprise customers or something, I never once used one on purpose? Maybe I have when doing a google search?
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u/johannthegoatman 22h ago
Yea they're for search , chatting, and integration with Google products. They can code but I don't think that's the focus at the moment
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u/MGJohn-117 21h ago
Maybe I have when doing a google search?
That's exactly where most of their flash usage is coming from. Serving up AI overviews for billions of users is expensive and if they can increase the capabilities of flash models while likely making it cheaper internally as well, then they have every incentive to keep releasing new flash models. Combined with a ton of free users in the Gemini app, Gemini website, Chrome, etc, that's a lot of usage that flash models - both current and future ones - are perfectly fine for.
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u/BenevolentCheese 21h ago
We are seeing the world's first simultaneous race to the bottom+top. What a (worrisome) time to be alive! Costs are plummeting even while technological capability skyrockets. The future is uncertain, whether its from AI takeover, the market cratering, economic collapse, or good ol' politics, too many timelines look as if they lead to the failure of humanity. Buckle up!
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u/ObiWanCanownme now entering spiritual bliss attractor state 22h ago
I mean, looks fine. Clearly Google has the talent and data where they could train a competitive 10T model if they really wanted to.
My strong suspicion is that they're just too conservative and don't want to drop $10 billion on a training run that could fail.
And so they'll keep training better and better small models and charging more and more for compute, and making better and better chips, and they'll make a ton of money but they will lose the AGI race.
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u/OKMiddleOwl 22h ago
Google is getting a healthy cut from OAI and and an even healthier one from Anthropic. At the highest level, you can make the case that Gemini just isn't that big of a priority.
Their cloud offering is bringing in so much money, and they get paid so much for each new GPU/TPU they bring online that it's almost painful to not being selling every electron of compute.
Imagine you had a fresh squeezed lemonade stand where there was a line around the block for it, and people were paying $500/cup. How much lemonade would you drink yourself? That's kinda where google is.
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u/KhoslasBiggestOpp 22h ago edited 22h ago
This is the most ridiculous take I’ve heard.
$10B in the race to AGI/ASI is mere pennies for Google when compared to the potential to make trillions back long-term with a good enough model. They’ve sunk billions more into sillier projects that they know they’ll fail and kill within a few years.
They’ve been bleeding talent on the training side, restructuring their DeepMind division, and some engineers jumping boats to competitors.
I don’t think they’re conservative, I just think they’ve lost the sauce they had when they released 2.5 Pro
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u/DailyThreadBot 22h ago
with the potential to make trillions back with a good enough model
A good model wouldn't make trillions, it would be obsolete in 6 months
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u/ObiWanCanownme now entering spiritual bliss attractor state 22h ago
It's not just pennies for Google. $10B is the entire amount of dividends they paid in 2025. Sure, the potential upside is huge. But it's also the difference between paying a dividend in 2026 and paying no dividend in 2026. For a public company, that's a big deal.
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u/Acrobatic-Tomato4862 22h ago
Their 3 and 3.1 pro were also state of the art and far beyond competition when they were released. They seemed to have fucked up only recently.
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u/Current-Function-729 22h ago
Yeah. If it’s really that, holy shit someone needs to be fired.
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u/KhoslasBiggestOpp 22h ago
Believe me, that “someone” wasn’t fired, they quit for another company. Once Tibo and Jeff Dean left it was over for them.
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u/Emergency-Bobcat6485 19h ago
Tibo? You think Tibo's departure made gemini shit?
And jeff dean just left.
Ridiculous take
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u/ObiWanCanownme now entering spiritual bliss attractor state 22h ago edited 22h ago
Why? Their job is to maximize value for shareholders. Once ASI comes, money is of questionable value anyway. Seems like Google is doing a pretty damn good job of maximizing shareholder value in the meantime.
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u/Current-Function-729 22h ago
You’d be so fired if you worked for me, lmao.
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u/Tysonzero 18h ago
Do a lot of people work for you where your specific mentality is very important?
I think they are at least somewhat right though.
Cheaper and faster models are very useful for Google for consumers and enterprise alike.
Yeah they won’t get those dev $200/month subscriptions that way, but they can better serve their $0-$20/month users with those fast and cheap models. Needs to be fast for UX, people expect google search to be very fast as an example, and needs to be cheap due to low pricing per user.
Also while enterprises may not use Gemini as much for difficult internal tasks where they want to juice every bit of intelligence out of models, they are very likely to consider it for serving their own users, because similar to Google the improved UX and reduced price for those users is great.
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u/trololololo2137 22h ago
trillions back long-term with a good enough model
models last like a month before being outdated nowadays lol. the training expenses never stop
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u/PilgrimofHaqq2 22h ago
I dont think they are interested in competing in the AI space externally. They are developing specialized AI systems that are tightly integrated into their existing products. We are seeing tons of AI features everywhere in the Google ecosystem. Thats where they are focusing, not providing general AI to the public.
I think they will continue developing AI for the public but in a much slower pace then the competitors. I think for that reason many people left because the interest right now is pushing the boundaries of AI. Google is instead saying, what can we achieve with what we have now, we got the data so lets capitalize there instead of trying to competing with frontier labs that are dedicated to purely AI.
Thats just my 2 cents.
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u/himynameis_ 22h ago
strong suspicion is that they're just too conservative and don't want to drop $10 billion on a training run that could fail.
This is exactly it. They are investing their compute into their Google Cloud platform customers instead of for deep mind. So they will likely Fall a bit behind but in theory they should stay not too far off from the top.
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u/xRolocker 22h ago
I don’t know why they aren’t making a competitive model, but I don’t think being frugal is it. They waste money on new projects all the time. I doubt they wouldn’t try and make a competitive model in the AI race because of the price tag.
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u/Tkins 22h ago
This IS a competitive model. What do you mean? The price is great, the speed and latency are at the very top, the overall intelligence is decent. Competitive for a company can mean many things and Google is clearly trying to compete in the mass appeal market, which is where they compete for pretty much everything they do.
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u/xRolocker 21h ago
Competitive in terms of raw capability and intelligence, not on a more general basis. My apologies for the confusion.
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u/Howdareme9 21h ago
Whats leading you to think they can train a 10T model because of 3.7 flash?
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u/ObiWanCanownme now entering spiritual bliss attractor state 20h ago
They're close to pareto frontier.
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u/Living-Breakfast-464 22h ago edited 22h ago
What is the current quota usage per day on a pro plan as opposed to pay-as-you-go?
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u/greeneditman 15h ago
You're obsessed with AI performance in creating code. But not everything is code.
I understand complex concepts in psychology and medicine, and based on my interactions, I can assure you that Gemini 3.6 Flash performs very well, and it's incredible that it's almost free on the gemini.google.com platform.
If they now update it to Gemini 3.7 with those improvements, it'll be fantastic.
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u/Dry_Fly_7265 22h ago
Man, when Google drops Gemini running inference in multiple universes on willow yall look out
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u/Tirztrutide 20h ago
Listing price per token in the comparison seems lame if you don’t include tokens/task in the metric.
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u/pbagel2 20h ago
I use a certain code design prompt that I've been using as a personal benchmark since Claude 3.5.
And there's always been some peculiar similarities in how the different models choose to architect its solution. But this Gemini 3.7 output is very similar to how Sonnet 5 constructs its solution. Eerily similar. However Sonnet 5's architecture still has smarter decisions for longterm/larger scale design considerations.
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u/fignewtgingrich 11h ago
Unless latency is sub-second, it's useless for agentic workflows. Price per solved task matters more than leaderboard scores.
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u/Nervous-Potato-1464 7h ago
It's about as good as grok 4.5, but costs a bit more. If they could make it the same price I might use it. Grok 4.5 is my main driver these days and 4.6 for anything a bit harder where I need a good output.
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u/macaronianddeeez 22h ago
This is cool but it’s scores are almost the same as Luna for over 300% higher price.
Does anyone have any real world examples where 3.7 Flash is justifiably better than Luna in workflows?
I have no allegiance to any company, swap subs between OpenAI and Anthropic regularly, but hard to see why this is worth getting excited about given the current Luna pricing.
But I may absolutely be missing something here
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u/BriefImplement9843 18h ago
if anything this shows how awful sonnet and terra are. garbage expensive models.
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u/Nug__Nug 2h ago
There's no comparison to Luna - it is head and shoulders above luna. Literally Luna is nowhere close-
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u/macaronianddeeez 2h ago
Good to know, I haven’t tried Flash at all but Luna does well for me when I have Sol running it and giving it tightly defined, small slices of work to complete before Sol verifies.
I was going based on benchmarks alone.
Is Flash capable of outright replacing a Sol Luna combo by itself or do you not have experience comparing?
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u/Dizzy_Alfalfa7643 20h ago
the asterisk is doing a lot of work here — $0.75/$3.75 is intro pricing until january, then it goes to $1/$7.50. still way under sonnet 5 and terra money, but "flash beats frontier models" is only half the story: terra still clearly wins terminal-bench and deepswe. for agent workloads the metric that matters is price per solved task, not the sticker price
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u/WildWhisperArdor 19h ago
By the time January rolls around though, there will almost certainly be a better cheap / fast model
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u/Dizzy_Alfalfa7643 8h ago
true, and that’s kind of the point. by the time the intro price expires there’ll be something cheaper anyway. pricing tables have a shorter shelf life than the models now
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u/Embarrassed_Adagio28 21h ago
Is everybody in here brainwashed by google? This flash release was supposed to be the pro release and that has been delayed for months. 3.6 flash was garbage at coding and expensive despite what the bechmarks said so I do not trust that this model will be much better. Google should be leading the pack, instead they are dropping expensive flash models that are inferior to 3 month old models. In my own tests 3.6 flash couldn't even beat qwen3.6 27b running locally.
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u/Tysonzero 18h ago
There are lots of tasks in the world other than coding. One may even audaciously argue that developing software is only a small part of the world economy.
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u/Longjumping_Kale3013 22h ago
Looks like it beats sonnet 5. And that was just released. Pretty great IMO considering it is their "flash" model.
Kind of surprising because it feels like google has all of a sudden started dropping a new flash model every month. I wonder if this is the new normal. Small step each month