11 ms·

Don’t let the “flash” name fool you, this is an amazing model. I have been playing with it for the past few weeks, it’s genuinely my new favorite; it’s so fast

• esafak 9mo ago
  What are you using it for and what were you using before?
• jauntywundrkind 9mo ago
  Just to point this out: many of these frontier models cost isn't that far away from two orders of magnitude more than what DeepSeek charges. It doesn't compare the same, no, but with coaxing I find it to be a pretty capable competent coding model & capable of answering a lot of general queries pretty satisfactorily (but if it's a short session, why economize?). $0.28/m in, $0.42/m out. Opus 4.5 is $5/$25 (17x/60x). 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.
  • happyopossum 9mo ago
    Two orders of magnitude would imply that these models cost $28/m in and $42/m out. Nothing is even close to that.
    • jauntywundrkind 9mo ago
      To me as an engineer, 60x for output (which is most of the cost I see, AFAICT) is not that significantly different from 100x. 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.
    • minraws 9mo ago
      Gpt 5.2 pro is well beyond that iirc
      • jauntywundrkind 9mo ago
        Whoa! I had no idea. $21/$168. That's 75x / 400x (1e1.875/1e2.6). https://platform.openai.com/docs/pricing https://platform.openai.com/docs/pricing
  • KoolKat23 9mo ago
    I struggle to see the incentive to do this, I have similar thoughts for locally run models. It's only use case I can imagine is small jobs at scale perhaps something like auto complete integrated into your deployed application, or for extreme privacy, honouring NDA's etc. 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.
    • lukan 9mo ago
      "or for extreme privacy" Or for any privacy/IP protection at all? There is zero privacy, when using cloud based LLM models.
      • Workaccount2 9mo ago
        Really only if you are paranoid. It's incredibly unlikely that the labs are lying about not training on your data for the API plans that offer it. Breaking trust with outright lies would be catastrophic to any lab right now. Enterprise demands privacy, and the labs will be happy to accommodate (for the extra cost, of course).
        • mistercheph 9mo ago
          No, it's incredibly unlikely that they aren't training on user data. It's billions of dollars worth of high quality tokens and preference that the frontier labs have access to, you think they would give that up for their reputation in the eyes of the enterprise market? LMAO. Every single frontier model is trained on torrented books, music, and movies.
          • user34283 9mo ago
            Considering that they will make a lot of money with enterprise, yes, that's exactly what I think. 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.
• Sincere6066 9mo ago
  [flagged]
• thecupisblue 9mo ago
  Oh wow - I recently tried 3 Pro preview and it was too slow for me. 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.
  • lancekey 9mo ago
    Curious to learn what a “product benchmark” looks like. Is it evals you use to test prompts/models? A third party tool? Examples from the wild are a great learning tool, anything you’re able to share is appreciated.
    • theshrike79 9mo ago
      Everyone should have their own "pelican riding a bicycle" benchmark they test new models on. And it shouldn't be shared publicly so that the models won't learn about it accidentally :)
      • ggsp 9mo ago
        Any suggestions for a simple tool to set up your own local evals?
        • theshrike79 9mo ago
          My "tool" is just prompts saved in a text file that I feed to new models by hand. I haven't built a bespoke framework on top of it. ...yet. Crap, do I need to now? =)
          • 9mo ago
• freedomben 9mo ago
  Cool! I've been using 2.5 flash and it is pretty bad. 1 out of 5 answers it gives will be a lie. Hopefully 3 is better
  • samyok 9mo ago
    Did you try with the grounding tool? Turning it on solved this problem for me.
    • Davidzheng 9mo ago
      what if the lie is a logical deduction error not a fact retrieval error
      • rat9988 9mo ago
        The error rate would still be improved overall and might make it a viable tool for the price depending on the usecase.
• epolanski 9mo ago
  Gemini 2.0 flash was good already for some tasks of mine long time ago..
• mmaunder 9mo ago
  Thanks, having it walk a hardcore SDR signal chain right now --- oh damn it just finished. The blog post makes it clear this isn't just some 'lite' model - you get low latency and cognitive performance. really appreciate you amplifying that.
• unsupp0rted 9mo ago
  How good is it for coding, relative to recent frontier models like GPT 5.x, Sonnet 4.x, etc?
  • bovermyer 9mo ago
    In my own, very anecdotal, experience, Gemini 3 Pro and Flash are both more reliably accurate than GPT 5.x. I have not worked with Sonnet enough to give an opinion there.
  • jasonjmcghee 9mo ago
    My experience so far- much less reliable. Though it’s been in chat not opencode or antigravity etc. you give it a program and say change it in this way, and it just throws stuff away, changes unrelated stuff etc. completely different quality than pro (or sonnet 4.5 / GPT-5.2)
    • piokoch 9mo ago
      So why Flash is so high in LiveCodeBench Pro? BTW: I have the same impression, Claude was working better for me for coding tasks.
    • PrayagS 9mo ago
      Been thinking of having Opus generate plans and then having Gemini 3 Flash execute. Might be better than using Haiku for the same. Anyone tried something similar already?
• encroach 9mo ago
  How did you get early access?
• scrollop 9mo ago
  Alright so we have more benchmarks including hallucinations and flash doesn't do well with that, though generally it beats gemini 3 pro and GPT 5.1 thinking and gpt 5.2 thinking xhigh (but then, sonnet, grok, opus, gemini and 5.1 beat 5.2 xhigh) - everything. Crazy. https://artificialanalysis.ai/evaluations/omniscience https://artificialanalysis.ai/evaluations/omniscience
  • tallclair 9mo ago
    On your Omniscience-Index vs. Cost graph, I think your Gemini 3 pro & flash models might be swapped.
• tonymet 9mo ago
  Can you be more specific on the tasks you’ve found exceptional ?
• giancarlostoro 9mo ago
  I wonder at what point will everyone who over-invested in OpenAI will regret their decision (expect maybe Nvidia?). Maybe Microsoft doesn't need to care, they get to sell their models via Azure.
  • toomuchtodo 9mo ago
    Amazon Set to Waste $10 Billion on OpenAI - https://finance.yahoo.com/news/amazon-set-waste-10-billion-151541306.html https://finance.yahoo.com/news/amazon-set-waste-10-billion-1... - December 17th, 2025
  • outside1234 9mo ago
    Very soon, because clearly OpenAI is in very serious trouble. They are scaled and have no business model and a competitor that is much better than them at almost everything (ads, hardware, cloud, consumer, scaling).
  • jack_riminton 9mo ago
    But you’re forgetting the Jonny Ive hardware device that totally isn’t like that laughable pin badge thing from Humane /s
    • user34283 9mo ago
      I agree completely. Altman was at some point talking about a screen less device and getting people away from the screen. Abandoning our mose useful sense, vision, is a recipe for a flop.
      • jack_riminton 9mo ago
        I'm not entirely sure it will ever see the light of day tbh The amount of money sloshing around in these acquisitions makes you wonder what they're really for
  • guelo 9mo ago
    OpenAI's doom was written when Altman (and Nadella) got greedy, threw away the nonprofit mission, and caused the exodus of talent and funding that created Anthropic. If they had stayed nonprofit the rest of the industry could have consolidated their efforts against Google's juggernaut. I don't understand how they expected to sustain the advantage against Google's infinite money machine. With Waymo Google showed that they're willing to burn money for decades until they succeed. 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.
• tonyhart7 9mo ago
  I think google is the only one that still produce general knowledge LLM right now claude is coding model from the start but GPT is in more and more becoming coding model
  • Imustaskforhelp 9mo ago
    I agree with this observation. Gemini does feel like code-red for basically every AI company like chatgpt,claude etc. too in my opinion if the underlying model is both fast and cheap and good enough 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.
    • Uehreka 9mo ago
      I would expect open weights models to always lag behind; training is resource-intensive and it’s much easier to finance if you can make money directly from the result. So in a year we may have a ~700B open weights model that competes with Gemini 3, but by then we’ll have Gemini 4, and other things we can’t predict now.
      • xbmcuser 9mo ago
        There will be diminishing returns though as the future models won't be thah much better we will reach a point where the open source model will be good enough for most things. And the need for being on the latest model no longer so important. 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.
        • FuckButtons 9mo ago
          I had a similar opinion, that we were somewhere near the top of the sigmoid curve of model improvement that we could achieve in the near term. But given continued advancements, I’m less sure that prediction holds.
• poopiokaka 9mo ago
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• lambda 9mo ago
  I'm a significant genAI skeptic. 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.
  • TeodorDyakov 9mo ago
    Hi. I am curious what was the benchmark question? Cheers!
    • Turskarama 9mo ago
      The problem with publicly disclosing these is that if lots of people adopt them they will become targeted to be in the model and will no longer be a good benchmark.
      • lambda 9mo ago
        Yeah, that's part of why I don't disclose. 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.
• moffkalast 9mo ago
  Should I not let the "Gemini" name fool me either?
• yunohn 9mo ago
  I love how every single LLM model release is accompanied by pre-release insiders proclaiming how it’s the best model yet…
  • hexasquid 9mo ago
    Make me think of how every iPhone is the best iPhone yet. Waiting for Apple to say "sorry folks, bad year for iPhone"
    • eru 9mo ago
      Wouldn't you expect that every new iPhone is genuinely the best iPhone? I mean, technology marches on.
      • OrangeMusic 9mo ago
        It was sarcasm.
  • Europas 9mo ago
    Thats true though. 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
    • yunohn 9mo ago
      My criticism is more about the fake-sounding pre-release insider hype aspect than the inevitable nature of forward progress.
• mips_avatar 9mo ago
  OpenAI made a huge mistake neglecting fast inferencing models. Their strategy was gpt 5 for everything, which hasn't worked out at all. I'm really not sure what model OpenAI wants me to use for my applications that require lower latency. If I follow their advice in their API docs about which models I should use for faster responses I get told either use GPT 5 low thinking, or replace gpt 5 with gpt 4.1, or switch to the mini model. Now as a developer I'm doing evals on all three of these combinations. I'm running my evals on gemini 3 flash right now, and it's outperforming gpt5 thinking without thinking. OpenAI should stop trying to come up with ads and make models that are useful.
  • simonw 9mo ago
    Yeah, I'm surprised that they've been through GT-5.1 and GPT-5.1-Codex and GPT-5.1-Codex-Max and now GPT-5.2 but their most recent mini model is still GPT-5-mini.
    • mips_avatar 9mo ago
      I cannot comprehend how they do not care about this segment of the market.
      • yakbarber 9mo ago
        it's easy to comprehend actually. they're putting everything on "having the best model". It doesn't look like they're going to win, but that's still their bet/
        • mips_avatar 9mo ago
          I mean they’re trying to outdo google. So they need to do that.
          • eru 9mo ago
            Until recently, Google was the underdog in the LLM race and OpenAI was the reigning champion. How quickly perceptions shift!
• dfsegoat 9mo ago
  > it’s more performant than Claude Opus 4.5 or GPT 5.2 extra high ...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-inference https://www.uncoveralpha.com/p/the-chip-made-for-the-ai-infe... (tldr: afaik Google trained Gemini 3 entirely on tensor processing units - TPUs)
• behnamoh 9mo ago
  > Don’t let the “flash” name fool you 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.
  • nemonemo 9mo ago
    Fair point. Asked Gemini to suggest alternatives, and it suggested Gemini Velocity, Gemini Atom, Gemini Axiom (and more). I would have liked `Gemini Velocity`.
    • behnamoh 9mo ago
      I like Anthropic's approach: Haiku, Sonnet, Opus. Haiku is pretty capable still and the name doesn't make me not wanna use it. But Flash is like "Flash Sale". It might still be a great model but my monkey brain associates it with "cheap" stuff.
  • pietz 9mo ago
    Interesting. Flash suggests more power to me than Mini. I never use gpt-5-mini in the UI whereas Flash appears to be just as good as Pro just a lot faster.
    • taytus 9mo ago
      Im in between :) Mini - small, incomplete, not good enough Flash - good, not great, fast, might miss something.
• kartayyar 9mo ago
  Can confirm. We at Roblox open sourced a new frontier game eval today, and it's beating even Gemini 3 Pro! ( Previous best model ). https://github.com/Roblox/open-game-eval/blob/main/LLM_LEADERBOARD.md https://github.com/Roblox/open-game-eval/blob/main/LLM_LEADE...
  • seany62 9mo ago
    Unbelievable
• ZuoCen_Liu 9mo ago
  What type of question is your one about testing AI inference time?
• kqr 9mo ago
  Yes, 2.5 Flash is extremely cost efficient in my favourite private benchmark: playing text adventures[1]. I'm looking forward to testing 3.0 Flash later today. [1]: https://entropicthoughts.com/haiku-4-5-playing-text-adventures https://entropicthoughts.com/haiku-4-5-playing-text-adventur...
• pplonski86 9mo ago
  Lately I was trying ask LLMs to generate SVG pictures, do you have famous pelican on bike created by flash model?