Posted by meetpateltech 6 days ago
Developer Blog: https://blog.google/technology/developers/build-with-gemini-...
Model Card [pdf]: https://deepmind.google/models/model-cards/gemini-3-flash/
Gemini 3 Flash in Search AI mode: https://blog.google/products/search/google-ai-mode-update-ge...
Deepmind Page: https://deepmind.google/models/gemini/flash/
I have been playing with it for the past few weeks, it’s genuinely my new favorite; it’s so fast and it has such a vast world knowledge that it’s more performant than Claude Opus 4.5 or GPT 5.2 extra high, for a fraction (basically order of magnitude less!!) of the inference time and price
After reading your comment I ran my product benchmark against 2.5 flash, 2.5 pro and 3.0 flash.
The results are better AND the response times have stayed the same. What an insane gain - especially considering the price compared to 2.5 Pro. I'm about to get much better results for 1/3rd of the price. Not sure what magic Google did here, but would love to hear a more technical deep dive comparing what they do different in Pro and Flash models to achieve such a performance.
Also wondering, how did you get early access? I'm using the Gemini API quite a lot and have a quite nice internal benchmark suite for it, so would love to toy with the new ones as they come out.
Examples from the wild are a great learning tool, anything you’re able to share is appreciated.
For my product, I run a video through a multimodal LLM with multiple steps, combine data and spit out the outputs + score for the video.
I have a dataset of videos that I manually marked for my usecase, so when a new model drops, I run it + the last few best benchmarked models through the process, and check multiple things:
- Diff between outputed score and the manual one - Processing time for each step - Input/Output tokens - Request time for each step - Price of request
And the classic stats of average score delta, average time, p50, p90 etc. + One fun thing which is finding the edge cases, since even if the average score delta is low (means its spot-on), there are usually some videos where the abs delta is higher, so these usually indicate niche edge cases the model might have.
Gemini 3 Flash nails it sometimes even better than the Pro version, with nearly the same times as 2.5 Pro does on that usecase. Actually, pushed it to prod yesterday and looking at the data, it seems it's 5 seconds faster than Pro on average, with my cost-per-user going down from 20 cents to 12 cents.
IMO it's pretty rudimentary, so let me know if there's anything else I can explain.
And it shouldn't be shared publicly so that the models won't learn about it accidentally :)
...yet. Crap, do I need to now? =)
But pretty rudimentary, nothing special. Also did not know about deepwalker, looks quite interesting - you building it?
I periodically ask them questions about topics that are subtle or tricky, and somewhat niche, that I know a lot about, and find that they frequently provide extremely bad answers. There have been improvements on some topics, but there's one benchmark question that I have that just about every model I've tried has completely gotten wrong.
Tried it on LMArena recently, got a comparison between Gemini 2.5 flash and a codenamed model that people believe was a preview of Gemini 3 flash. Gemini 2.5 flash got it completely wrong. Gemini 3 flash actually gave a reasonable answer; not quite up to the best human description, but it's the first model I've found that actually seems to mostly correctly answer the question.
So, it's just one data point, but at least for my one fairly niche benchmark problem, Gemini 3 Flash has successfully answered a question that none of the others I've tried have (I haven't actually tried Gemini 3 Pro, but I'd compared various Claude and ChatGPT models, and a few different open weights models).
So, guess I need to put together some more benchmark problems, to get a better sample than one, but it's at least now passing a "I can find the answer to this in the top 3 hits in a Google search for a niche topic" test better than any of the other models.
Still a lot of things I'm skeptical about in all the LLM hype, but at least they are making some progress in being able to accurately answer a wider range of questions.
So I want to have a general idea of how good it is at this.
I found something that was niche, but not super niche; I could easily find a good, human written answer in the top couple of results of a Google search.
But until now, all LLM answers I've gotten for it have been complete hallucinated gibberish.
Anyhow, this is a single data point, I need to expand my set of benchmark questions a bit now, but this is the first time that I've actually seen progress on this particular personal benchmark.
Get an API and try to use it for classification of text or classification of images. Having an excel file with somewhat random looking 10k entries you want to classify or filter down to 10 important for you, use LLM.
Get it to make audio transcription. You can now just talk and it will make note for you on level that was not possible earlier without training on someone voice it can do anyone’s voice.
Fixing up text is of course also big.
Data classification is easy for LLM. Data transformation is a bit harder but still great. Creating new data is hard so like answering questions where it has to generate stuff from thin air it will hallucinate like a mad man.
The ones that LLMs are good in are used in background by people creating actual useful software on top of LLMs but those problems are not seen by general public who sees chat box.
Maybe the scale is different with genAI and there are some painful learnings ahead of us.
After all it's the same search engine team that didn't care about its search results - it's main draw - activey going shit for over a decade.
They probably use old Flash Lite model, something super small, and just summarize the search...
I know without the ability to search it's very unlikely the model actually has accurate "memories" about these things, I just hope one day they will acutally know that their "memory" is bad or non-existing and they will tell me so instead of hallucinating something.
I don't need an LLM to have a trillion parameters if i just need it to be a great user interface.
Someone is probably working on this somewere or will but lets see.
Basically making sense of unstructured data is super cool. I can get 20 people to write an answer the way they feel like it and model can convert it to structured data - something I would have to spend time on, or I would have to make form with mandatory fields that annoy audience.
I am already building useful tools with the help of models. Asking tricky or trivia questions is fun and games. There are much more interesting ways to use AI.
So I think LLMs can be good for finding niche info.
Which also implies that (for most tasks), most of the weights in a LLM are unnecessary, since they are spent on memorizing the long tail of Common Crawl... but maybe memorizing infinite trivia is not a bug but actually required for the generalization to work? (Humans don't have far transfer though... do transformers have it?)
Kinda sounds like you're testing two things at the same time then, right? The knowledge of the thing (was it in the training data and was it memorized?) and the understanding of the thing (can they explain it properly even if you give them the answer in context).
Today I had to resolve performance problems for some sql server statement. Been doing it years, know the regular pitfalls, sometimes have to find "right" words to explain to customer why X is bad and such.
I described the issue to GPT5.2, gave the query, the execution plan and asked for help.
It was spot on, high quality responses and actionable items and explanations on why this or that is bad, how to improve it and why particularly sql may have generated such a query plan. I could instantly validate the response given my experience in the field. I even answered with some parts of chatgpt on how well it explained. However I did mention that to customer and I did tell them I approve the answer.
Asked high quality question and receive a high quality answer. And I am happy that I found out about an sql server flag where I can influence particular decision. But the suggestion was not limited to that, there were multiple points given that would help.
Obviously, the fact that I've done Google searches and tested the models on these means that their systems may have picked up on them; I'm sure that Google uses its huge dataset of Google searches and search index as inputs to its training, so Google has an advantage here. But, well, that might be why Googles new models are so much better, they're actually taking advantage of some of this massive dataset they've had for years.
What's the value of a secret benchmark to anyone but the secret holder? Does your niche benchmark even influence which model you use for unrelated queries? If LLM authors care enough about your niche (they don't) and fake the response somehow, you will learn on the very next query that something is amiss. Now that query is your secret benchmark.
Even for niche topics it's rare that I need to provide more than 1 correction or knowledge update.
The reason I don't disclose isn't generally that I think an individual person is going to read my post and update the model to include it. Instead it is because if I write "I ask the question X and expect Y" then that data ends up in the train corpus of new LLMs.
However, one set of my benchmarks is a more generalized type of test (think a parlor-game type thing) that actually works quite well. That set is the kind of thing that could be learnt via reinforcement learning very well, and just mentioning it could be enough for a training company or data provider company to try it. You can generate thousands of verifiable tests - potentially with verifiable reasoning traces - quite easily.
For fun: https://chatgpt.com/s/t_694361c12cec819185e9850d0cf0c629
1. What is the purpose of the benchmark?
2. What is the purpose of publicly discussing a benchmark's results but keeping the methodology secret?
To me it's in the same spirit as claiming to have defeated alpha zero but refusing to share the game.
2. I discussed that up-thread, but https://github.com/microsoft/private-benchmarking and https://arxiv.org/abs/2403.00393 discuss some further motivation for this if you are interested.
> To me it's in the same spirit as claiming to have defeated alpha zero but refusing to share the game.
This is an odd way of looking at it. There is no "winning" at benchmarks, it's simply that it is a better and more repeatable evaluation than the old "vibe test" that people did in 2024.
I don't understand the value of a public post discussing their results beyond maybe entertainment. We have to trust you implicitly and have no way to validate your claims.
> There is no "winning" at benchmarks, it's simply that it is a better and more repeatable evaluation than the old "vibe test" that people did in 2024.
Then you must not be working in an environment where a better benchmark yields a competitive advantage.
In principle, we have ways: if nl's reports consistently predict how public benchmarks will turn out later, they can build up a reputation. Of course, that requires that we follow nl around for a while.
> A secret benchmark is: Useful for internal model selection
That's what I'm doing.
The root of this whole discussion was a post about how Gemini 3 outperformed other models on some presumably informal question benchmark (a"vibe test"?). When asked for the benchmark, the response from the op and and someone else was that secrecy was needed to protect the benchmark from contamination. I'm skeptical of the need in the op's cases and I'm skeptical of the effectiveness of the secrecy in general. In a case where secrecy has actual value, why even discuss the benchmark publicly at all?
Example: You are probably already aware that almost any metric that you try to use to measure code quality can be easily gamed. One possible strategy is to choose a weighted mixture of metrics and conceal the weights. The weights can even change over time. Is it perfect? No. But it's at least correlated with code quality -- and it's not trivially gameable, which puts it above most individual public metrics.
Will someone (or some system) see my query and think "we ought to improve this"? I have no idea since I don't work on these systems. In some instances involving random sampling... probably yes!
This is the second reason I find the idea of publicly discussing secret benchmarks silly.
I guess they get such a large input of queries that they can only realistically check and therefore use a small fraction? Though maybe they've come up with some clever trick to make use of it anyway?
you dont train on your test data because you need to have that to compare if training is improving or not.
I'll need to find a new one, or actually put together a set of questions to use instead of just a single benchmark.
"When was the last time England beat Scotland at rugby union"
new variant "Without using search when was the last time England beat Scotland at rugby union"
It is amazing how bad ChatGPT is at this question and has been for years now across multiple models. It's not that it gets it wrong - no shade, I've told it not to search the web so this is _hard_ for it - but how badly it reports the answer. Starting from the small stuff - it almost always reports the wrong year, wrong location and wrong score - that's the boring facts stuff that I would expect it to stumble on. It often creates details of matches that didn't exist, cool standard hallucinations. But even within the text it generates itself it cannot keep it consistent with how reality works. It often reports draws as wins for England. It frequently states the team that it just said scored most points lost the match, etc.
It is my ur example for when people challenge my assertion LLMs are stochastic parrots or fancy Markov chains on steroids.
The only non-TPU fast models I'm aware of are things running on Cerebras can be much faster because of their CPUs, and Grok has a super fast mode, but they have a cheat code of ignoring guardrails and making up their own world knowledge.
Where are you getting that? All the citations I've seen say the opposite, eg:
> Inference Workloads: NVIDIA GPUs typically offer lower latency for real-time inference tasks, particularly when leveraging features like NVIDIA's TensorRT for optimized model deployment. TPUs may introduce higher latency in dynamic or low-batch-size inference due to their batch-oriented design.
https://massedcompute.com/faq-answers/
> The only non-TPU fast models I'm aware of are things running on Cerebras can be much faster because of their CPUs, and Grok has a super fast mode, but they have a cheat code of ignoring guardrails and making up their own world knowledge.
Both Cerebras and Grok have custom AI-processing hardware (not CPUs).
The knowledge grounding thing seems unrelated to the hardware, unless you mean something I'm missing.
The citation link you provided takes me to a sales form, not an FAQ, so I can't see any further detail there.
> Both Cerebras and Grok have custom AI-processing hardware (not CPUs).
I'm aware of Cerebras' custom hardware. I agree with the other commenter here that I haven't heard of Grok having any. My point about knowledge grounding was simply that Grok may be achieving its latency with guardrail/knowledge/safety trade-offs instead of custom hardware.
I don't see any latency comparisons in the link
https://jax-ml.github.io/scaling-book/gpus/#gpus-vs-tpus-at-...
Re: Groq, that's a good point, I had forgotten about them. You're right they too are doing a TPU-style systolic array processor for lower latency.
For each you can use it as “instant” supposedly without thinking (though these are all exclusively reasoning models) or specify a reasoning amount (low, medium, high, and now xhigh - though if you do g specify it defaults to none) OR you can use the -chat version which is also “no thinking” but in practice performs markedly differently from the regular version with thinking off (not more or less intelligent but has a different style and answering method).
Coming up with all that fluff would keep my brain busy, meaning there's actually no additional breathing room for thinking about an answer.
> Coming up with all that fluff would keep my brain busy, meaning there's actually no additional breathing room for thinking about an answer.
It gets a lot easier with practice: your brain caches a few of the typical fluff routines.
And now with RAM, GPU and boards being a PitA to get based on supply and pricing - double middle finger to all the big tech this holiday season!
It's a lost battle. It'll always be cheaper to use an open source model hosted by others like together/fireworks/deepinfra/etc.
I've been maining Mistral lately for low latency stuff and the price-quality is hard to beat.
They do have a priority tier at double the cost, but haven't seen any benchmarks on how much faster that actually is.
The flex tier was an underrated feature in GPT5, batch pricing with a regular API call. GPT5.1 using flex priority is an amazing price/intelligence tradeoff for non-latency sensitive applications, without needing to extra plumbing of most batch APIs
Turns out becoming a $4 trillion company first with ads (Google), then owning everybody on the AI-front could be the winning strategy.
https://github.com/Roblox/open-game-eval/blob/main/LLM_LEADE...
Markets seems to be in a: "Show me the OpenAI money" mood at the moment.
And even financial commentators who don't necessarily know a thing about AI can realize that Gemini 3 Pro and now Gemini 3 Flash are giving ChatGPT a run for its money.
Oracle and Microsoft have other source of revenues but for those really drinking the OpenAI koolaid, including OpenAI itself, I sure as heck don't know what the future holds.
My safe bet however is that Google ain't going anywhere and shall keep progressing on the AI front at an insane pace.
[0] At least the guys who publish where you or me can read them.
This story also shows the market corruption of Google's monopolies, but a judge recently gave them his stamp of approval so we're stuck with it for the foreseeable future.
I ask this question about Nazi Germany. They adopted the Blitkrieg strategy and expanded unsustainably, but it was only a matter of time until powers with infinite resources (US, USSR) put an end to it.
Most obvious decision points were betraying the USSR and declaring war on the US (no one really had been able to print the reason, but presumably it was to get Japan to attack the soviets from the other side, which then however didn't happen). Another could have been to consolidate after the surrender/supplication of France, rather than continue attacking further.
Not saying that the Nazi strategy was without flaws, of course. But your specific critique is a bit too blunt.
They always had the best talent, but with Brin at the helm, they also have someone with the organizational heft to drive them towards a single goal
/s
Abandoning our mose useful sense, vision, is a recipe for a flop.
The amount of money sloshing around in these acquisitions makes you wonder what they're really for
Waiting for Apple to say "sorry folks, bad year for iPhone"
All these announcements beat all the other models on most benchmarks and are then the best model yet. They can't see the future yet so they are not aware or care anyway that 2 weeks later someone says "hold my beer" and we get again better benchmark results from someone else.
Exhausting and exciting
I think it's bad naming on google's part. "flash" implies low quality, fast but not good enough. I get less negative feeling looking at "mini" models.
Mini - small, incomplete, not good enough
Flash - good, not great, fast, might miss something.
I've been playing around with other models recently (Kimi, GPT Codex, Qwen, others) to try to better appreciate the difference. I knew there was a big price difference, but watching myself feeding dollars into the machine rather than nickles has also founded in me quite the reverse appreciation too.
I only assume "if you're not getting charged, you are the product" has to be somewhat in play here. But when working on open source code, I don't mind.
I tried to be quite clear with showing my work here. I agree that 17x is much closer to a single order of magnitude than two. But 60x is, to me, a bulk enough of the way to 100x that yeah I don't feel bad saying it's nearly two orders (it's 1.78 orders of magnitude). To me, your complaint feels rigid & ungenerous.
My post is showing to me as -1, but I standby it right now. Arguing over the technicalities here (is 1.78 close enough to 2 orders to count) feels besides the point to me: DeepSeek is vastly more affordable than nearly everything else, putting even Gemini 3 Flash here to shame. And I don't think people are aware of that.
I guess for my own reference, since I didn't do it the first time: at $0.50/$3.00 / M-i/o, Gemini 3 Flash here is 1.8x & 7.1x (1e1.86) more expensive than DeepSeek.
Otherwise, if it's a short prompt or answer, SOTA (state of the art) model will be cheap anyway and id it's a long prompt/answer, it's way more likely to be wrong and a lot more time/human cost is spent on "checking/debugging" any issue or hallucination, so again SOTA is better.
Or for any privacy/IP protection at all? There is zero privacy, when using cloud based LLM models.
What I don't think is that I can take seriously someone's opinion on enterprise service's privacy after they write "LMAO" in capslock in their post.
Second thing to consider is the whole geopolitical situation. I know companies in europe are really reluctant to give US companies access to their internal data.
Its different if they proclaimed outright they won't use it and then do.
Not that any of this is right, it wouldn't be a true betrayal.
On a related note, these terms to me are a great example of success for EU GDPR regulations, and regulations on corporates in general. It's clear as day, additional protections are afforded to EU residents in these terms purely due to the law.
claude is coding model from the start but GPT is in more and more becoming coding model
I hope open source AI models catch up to gemini 3 / gemini 3 flash. Or google open sources it but lets be honest that google isnt open sourcing gemini 3 flash and I guess the best bet mostly nowadays in open source is probably glm or deepseek terminus or maybe qwen/kimi too.
For me the bigger concern which I have mentioned on other AI related topics is that AI is eating all the production of computer hardware so we should be worrying about hardware prices getting out of hand and making it harder for general public to run open source models. Hence I am rooting for China to reach parity on node size and crash the PC hardware prices.
So I don't think we are on any sigmoid curve or so. Though if you plot the performance of the best model available at any point in time against time on the x-axis, you might see a sigmoid curve, but that's a combination of the logarithm and the amount of effort people are willing to spend on making new models.
(I'm not sure about it specifically being the logarithm. Just any curve that has rapidly diminishing marginal returns that nevertheless never go to zero, ie the curve never saturates.)
And now I am saying the same for gemini 3 flash.
I still feel the same way tho, sure there is an increase but I somewhat believe that gemini 3 is good enough and the returns on training from now on might not be worth thaat much imo but I am not sure too and i can be wrong, I usually am.
If Google released their weights today, it would technically be open weight; but I doubt you'd have an easy time running the whole Gemini system outside of Google's datacentres.
Claude Code just caught up to cursor (no 2) in revenue and based on trajectories is about to pass GitHub copilot (number 1) in a few more months. They just locked down Deloitte with 350k seats of Claude Enterprise.
In my fortune 100 financial company they just finished crushing open ai in a broad enterprise wide evaluation. Google Gemini was never in the mix, never on the table and still isn’t. Every one of our engineers has 1k a month allocated in Claude tokens for Claude enterprise and Claude code.
There is 1 leader with enterprise. There is one leader with developers. And google has nothing to make a dent. Not Gemini 3, not Gemini cli, not anti gravity, not Gemini. There is no Code Red for Anthropic. They have clear target markets and nothing from google threatens those.
> Google Gemini was never in the mix, never on the table and still isn’t. Every one of our engineers has 1k a month allocated in Claude tokens for Claude enterprise and Claude code.
Does that mean y'all never evaluated Gemini at all or just that it couldn't compete? I'd be worried that prior performance of the models prejudiced stats away from Gemini, but I am a Claude Code and heavy Anthropic user myself so shrug.
Enterprise will follow.
I don't see any distinction in target markets - it's the same market.
Also I do not really use agentic tasks but I am not sure that gemini 3/3 flash have mcp support/skills support for agentic tasks
if not, I feel like they are very low hanging fruits and something that google can try to do too to win the market of agentic tasks over claude too perhaps.
So far they seem faster with Flash, and with less corruption of files using the Edit tool - or at least it recovered faster.
Pretty much every person in the first (and second) world is using AI now, and only small fraction of those people are writing software. This is also reflected in OAI's report from a few months ago that found programming to only be 4% of tokens.
This sounds like you live in a huge echo chamber. :-(
Apart from my very old grandmothers, I don't know anyone not using AI.
A lot of public religious imagery is very clearly AI generated, and you can find a lot of it on social media too. "I asked ChatGPT" is a common refrain at family gatherings. A lot of regular non-techie folks (local shopkeepers, the clerk at the gas station, the guy at the vegetable stand) have been editing their WhatsApp profile pictures using generative AI tools.
Some of my lawyer and journalist friends are using ChatGPT heavily, which is concerning. College students too. Bangalore is plastered with ChatGPT ads.
There's even a low-cost ChatGPT plan called ChatGPT Go you can get if you're in India (not sure if this is available in the rest of the world). It costs ₹399/mo or $4.41/mo, but it's completely free for the first year of use.
So yes, I'd say many people outside of tech circles are using AI tools. Even outside of wealthy first-world countries.
Just googling means you use AI nowdays.
Remember, really back in the day the A* search algorithm was part of AI.
If you had asked anyone in the 1970s whether a box that given a query pinpoints the right document that answers that question (aka Google search in the early 2000s), they'd definitely would have called it AI.
[1]: https://entropicthoughts.com/haiku-4-5-playing-text-adventur...
Anyone tried something similar already?
BTW: I have the same impression, Claude was working better for me for coding tasks.
I have not worked with Sonnet enough to give an opinion there.
...and all of that done without any GPUs as far as i know! [1]
[1] - https://www.uncoveralpha.com/p/the-chip-made-for-the-ai-infe...
(tldr: afaik Google trained Gemini 3 entirely on tensor processing units - TPUs)
They are pushing the prices higher with each release though: API pricing is up to $0.5/M for input and $3/M for output
For comparison:
Gemini 3.0 Flash: $0.50/M for input and $3.00/M for output
Gemini 2.5 Flash: $0.30/M for input and $2.50/M for output
Gemini 2.0 Flash: $0.15/M for input and $0.60/M for output
Gemini 1.5 Flash: $0.075/M for input and $0.30/M for output (after price drop)
Gemini 3.0 Pro: $2.00/M for input and $12/M for output
Gemini 2.5 Pro: $1.25/M for input and $10/M for output
Gemini 1.5 Pro: $1.25/M for input and $5/M for output
I think image input pricing went up even more.
Correction: It is a preview model...
Google has been discontinuing older models after several months of transition period so I would expect the same for the 2.5 models. But that process only starts when the release version of 3 models is out (pro and flash are in preview right now).
You really need to look at the cost per task. artificialanalysis.ai has a good composite score, measures the cost of running all the benchmarks, and has 2d a intelligence vs. cost graph.
Tried a lot of them and settled on this one, they update instantly on model release and having all models on one page is the best UX.
Presumably a big motivation for them is to be first to get something good and cheap enough they can serve to every Android device, ahead of whatever the OpenAI/Jony Ive hardware project will be, and way ahead of Apple Intelligence. Speaking for myself, I would pay quite a lot for truly 'AI first' phone that actually worked.
From a business perspective it’s a smart move (inasmuch as “integrating AI” is the default which I fundamentally disagree with) since Apple won’t be left holding the bag on a bunch of AI datacenters when/if the AI bubble pops.
I don’t want to lose trust in Apple, but I literally moved away from Google/Android to try and retain control over my data and now they’re taking me… right back to Google. Guess I’ll retreat further into self-hosting.
As long as Apple doesn't take any crazy left turns with their privacy policy then it should be relatively harmless if they add in a google wrapper to iOS (and we won't need to take hard right turns with grapheneOS phones and framework laptops).
Did you forget all the Apple Intelligence stuff? They were never "ignoring" if anything they talked a big talk, and then failed so hard.
The whole iPhone 16 was marketed as AI first phone (including in billboards). They had full length ads running touting AI benefits.
Apple was never "ignoring" or "sitting AI out". They were very much in it. And they failed.
I almost switched out of the Apple ecosystem a few months ago, but I have an Apple Studio monitor and using it with non-Apple gear is problematic. Otherwise a Pixel phone and a Linux box with a commodity GPU would do it for me.
Stuff like:
"Open Chrome, new tab, search for xyz, scroll down, third result, copy the second paragraph, open whatsapp, hit back button, open group chat with friends, paste what we copied and send, send a follow-up laughing tears emoji, go back to chrome and close out that tab"
All while being able to just quickly glance at my phone. There is already a tool like this, but I want the parsing/understanding of an LLM and super fast response times.
On a related note, why would you want to break down your tasks to that level surely it should be smart enough to do some of that without you asking and you can just state your end goal.
Plus, if the above worked, the higher level interactions could trivially work too. "Go to event details", "add that to my calendar".
FWIW, I'm starting to embrace using Gemini as general-purpose UI for some scenarios just because it's faster. Most common one, "<paste whatever> add to my calendar please."
Is there an OSS model that's better than 2.0 flash with similar pricing, speed and a 1m context window?
Edit: this is not the typical flash model, it's actually an insane value if the benchmarks match real world usage.
> Gemini 3 Flash achieves a score of 78%, outperforming not only the 2.5 series, but also Gemini 3 Pro. It strikes an ideal balance for agentic coding, production-ready systems and responsive interactive applications.
The replacement for old flash models will be probably the 3.0 flash lite then.
So if 2.5 Pro was good for your usecase, you just got a better model for about 1/3rd of the price, but might hurt the wallet a bit more if you use 2.5 Flash currently and want an upgrade - which is fair tbh.
It's extremely fast on good hardware, quite smart, and can support up to 1m context with reasonable accuracy
Relevant to the linked Google blog: I feel like getting Nemotron 3 nano and Gemini 3 flash in one week is an early Christmas gift. I have lived with the exponential improvements in practical LLM tools over the last three years, but this week seems special.
I do pay special attention to what the most negative comments say (which in this case are unusually positive). And people discussing performance on their own personal benchmarks.
Gemini 3 pro got 20%, and everyone else has gotten 0%. I saw benchmarks showing 3 flash is almost trading blows with 3 pro, so I decided to try it.
Basically it is an image showing a dog with 5 legs, an extra one photoshopped onto it's torso. Every models counts 4, and gemini 3 pro, while also counting 4, said the dog had a "large male anatomy". However it failed a follow-up saying 4 again.
3 flash counted 5 legs on the same image, however I added distinct a "tattoo" to each leg as an assist. These tattoos didn't help 3 pro or other models.
So it is the first out of all the models I have tested to count 5 legs on the "tattooed legs" image. It still counted only 4 legs on the image without the tattoos. I'll give it 1/2 credit.
With this release the "good enough" and "cheap enough" intersect so hard that I wonder if this is an existential threat to those other companies.
In my experience, to get the best performance out of different models, they need slightly different prompting.
There's a plugin for everything that mimics anything the others are doing
I see all of these tools as IDEs. Whether someone locks into VS Code, JetBrains, Neovim, or Sublime Text comes down to personal preference. Everyone works differently, and that is completely fine.
Maybe someday future models will all behave similarly given the same prompt, but we're not quite there yet
Opus and Sonnet are slower than Haiku. For lots of less sophisticated tasks, you benefit from the speed.
All vendors do this. You need smaller models that you can rapid-fire for lots of other reasons than vibe coding.
Personally, I actually use more smaller models than the sophisticated ones. Lots of small automations.
Think beyond interfaces. I'm talking about rapid-firing hundreds of small agents and having zero human interaction with them. The feedback is deterministic (non agentic) and automated too.
Much cheaper price and much faster token generation.
At least, that's what I need. I stopped using Anthropic because for their $20 a month offering, I get rate limited constantly, but for Gemini $20/month I've never even once hit a limit.
You say good enough. Great, but what if I as a malicious person were to just make a bunch of internet pages containing things that are blatantly wrong, to trick LLMs?
So Reddit?
I’d imagine the AI companies have all the “pre AI internet” data they scraped very carefully catalogued.
Now, imagine for a moment they had also vertically integrated the hardware to do this.
The most terrifying thing would be Google expanding its free tiers.
Granted, this doesn't give api access, only what google calls their "consumer ai products", but it makes a huge difference when chatgpt only allows a handful of document uploads and deep research queries per day.
Then you realise you aren't imagining it.
Google is great on the data science alone, every thing else is an after thought
"And then imagine Google designing silicon that doesn’t trail the industry."
I'm def not a Google stan generally, but uh, have you even been paying attention?
TPUs on the other hand are ASICs, we are more than familiar with the limited application, high performance and high barriers to entry associated with them. TPUs will be worthless as the AI bubble keeps deflating and excess capacity is everywhere.
The people who don't have a rudimentary understanding are the wall street boosters that treat it like the primary threat to Nvidia or a moat for Google (hint: it is neither).
I'm speculating but Google might have figured out some training magic trick to balance out the information storage in model capacity. That or this flash model has huge number of parameters or something.
https://artificialanalysis.ai/evaluations/omniscience
Prepare to be amazed
Can someone explain how Gemini 3 pro/flash then do so well then in the overall Omniscience: Knowledge and Hallucination Benchmark?
One hypothesis is that gemini 3 flash refuses to answer when unsuure less often than other models, but when sure is also more likely to be correct. This is consistent with it having the best accuracy score.
> In the Hallucination Rate vs. AA-Omniscience Index chart, it’s not in the most desirable quadrant
This doesn't mean much. As long as Gemini 3 has a high hallucination rate (higher than at least 50% others), it's not going to be in the most desirable quadrant by definition.
For example, let's say a model answers 99 out of 100 questions correctly. The 1 wrong answer it produces is a hallucination (i.e. confidently wrong). This amazing model would have a 100% hallucination rate as defined here, and thus not be in the most desirable quadrant. But it should still have a very high Omniscience Index.
They have a similar chart that compares results across all their benchmarks vs. cost and 3 Flash is about half as expensive as 3 Pro there despite being four times cheaper per token.
That's what MoE is for. It might be that with their TPUs, they can afford lots of params, just so long as the activated subset for each token is small enough to maintain throughput.
More experts with a lower pertentage of active ones -> more sparsity.
It's 1/4 the price of Gemini 3 Pro ≤200k and 1/8 the price of Gemini 3 Pro >200k - notable that the new Flash model doesn’t have a price increase after that 200,000 token point.
It’s also twice the price of GPT-5 Mini for input, half the price of Claude 4.5 Haiku.