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

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...

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.

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.

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.

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.

Now, imagine for a moment they had also vertically integrated the hardware to do this.

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.

I assume that these are just different reasoning levels for Gemini 3, but I can't even find mention of there being 2 versions anywhere, and the API doesn't even mention the Thinking-Pro dichotomy.

Just avoiding/fixing that would probably speed up a good chunk of my own queries.

For comparison, from 2.5 Pro ($1.25 / $10) to 3 Pro ($2 / $12), there was 60% increase in input tokens and 20% increase in output tokens pricing.

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...

Trying to use Gemini cli is such a pain. I bought GDP Premium and configured GCP, setup environment variables, enabled preview features in cli and did all the dance around it and it won't let me use gemini 3. Why the hell I am even trying so hard?

Flash is meant to be a model for lower cost, latency-sensitive tasks. Long thinking times will both make TTFT >> 10s (often unacceptable) and also won't really be that cheap?

After Gemini 3.0 the OpenAI damage control crews all drowned.

Not only is it vastly better, it's also free.

I find this particular benchmark to be in agreement with my experiences: https://simple-bench.com

Turns out Gemini 3 Flash is pretty close. The Gemini CLI is not as good but the model more than makes up for it.

The weird part is Gemini 3 Pro is nowhere as good an experience. Maybe because its just so slow.

Image model they have released is much worse than nano banana pro, ghibli moment did not happen

Their GPT 5.2 is obviously overfit on benchmarks as a consensus of many developers and friends I know. So Opus 4.5 is staying on top when it comes to coding

The weight of the ads money from google and general direction + founder sense of Brin brought the google massive giant back to life. None of my companies workflow run on OAI GPT right now. Even though we love their agent SDK, after claude agent SDK it feels like peanuts.

Pipe dream right now, but 50 years later? Maybe

Google keeps their models very "fresh" and I tend to get more correct answers when asking about Azure or O365 issues, ironically copilot will talk about now deleted or deprecated features more often.

For now the venders I pay for are 90% Google, and 10% combination of Chinese models and from the French company Mistral.

I love the new Gemini 3 Flash model - it hits so many sweet-spots for me. The API is inexpensive enough for my use cases that I don’t even think about the cost.

My preference is using local open models with Ollama and LM Studio, but commercial models are also a large part of my use cases.

Its great that they have these new fast models, but the release hype has made Gemini Pro pretty much unusable for hours.

"Sorry, something went wrong"

random sign-outs

random garbage replies, etc

https://github.com/vectara/hallucination-leaderboard

Hoping that the local ones keep progressively up (gemma-line)

Skatval is a small local area I live in, so I know when it's bullshitting. Usually, I get a long-winded answer that is PURE Barnum-statement, like "Skatval is a rural area known for its beautiful fields and mountains" and bla bla bla.

Even with minimal thinking (it seems to do none), it gives an extremely good answer. I am really happy about this.

I also noticed it had VERY good scores on tool-use, terminal, and agentic stuff. If that is TRUE, it might be awesome for coding.

I'm tentatively optimistic about this.

-> 2.5 Flash Lite is super fast & cheap (~1-1.5s inference), but poor quality responses.

-> 2.5 Flash gives high quality responses, but fairly expensive & slow (5-7s inference)

I really just need an in-between for Flash and Flash Lite for cost and performance. Right now, users have to wait up to 7s for a quality response.

1, has anyone actually found 3 Pro better than 2.5 (on non code tasks)? I struggle to find a difference beyond the quicker reasoning time and fewer tokens.

2, has anyone found any non-thinking models better than 2.5 or 3 Pro? So far I find the thinking ones significantly ahead of non thinking models (of any company for that matter.)

I also had it summarize this thread on Hacker News about itself:

https://gist.github.com/simonw/b0e3f403bcbd6b6470e7ee0623be6...

  llm \
  -f hn:46301851 -m "gemini-3-flash-preview" \
  -s 'Summarize the themes of the opinions expressed here.
  For each theme, output a markdown header.
  Include direct "quotations" (with author attribution) where appropriate.
  You MUST quote directly from users when crediting them, with double quotes.
  Fix HTML entities. Output markdown. Go long. Include a section of quotes that illustrate opinions uncommon in the rest of the piece'

Firstly, 3 Flash is wicked fast and seems to be very smart for a low latency model, and it's a rush just watching it work. Much like the YOLO mode that exists in Gemini CLI, Flash 3 seems to YOLO into solutions without fully understanding all the angles e.g. why something was intentionally designed in a way that at first glance may look wrong, but ended up this way through hard won experience. Codex gpt 5.2 xhigh on the other hand does consider more angles.

It's a hard come-down off the high of using it for the first time because I really really really want these models to go that fast, and to have that much context window. But it ain't there. And turns out for my purposes the longer chain of thought that codex gpt 5.2 xhigh seems to engage in is a more effective approach in terms of outcomes.

And I hate that reality because having to break a lift into 9 stages instead of just doing it in a single wicked fast run is just not as much fun!

its almost as good as 5.2 and 4.5 but way faster and cheaper

Also I don't see it written in the blog post but Flash supports more granular settings for reasoning: minimal, low, medium, high (like openai models), while pro is only low and high.

I'm more excited to see 3 Flash Lite. Gemini 2.5 Flash Lite needs a lot more steering than regular 2.5 Flash, but it is a very capable model and combined with the 50% batch mode discount it is CHEAP ($0.05/$0.20).

I also had similar issues with Claude Code in the past. Everyone should take a page out of Bun's playbook. I never had `bun update` fail.

Edit: Also, I wish NPM wasn't the distribution mechanism for these TUIs. I suspect NPM's interplay with global packages and macOS permissions is what's causing the issue.

Also, I hate that I cannot send the Google models in a "Thinking" mode like in ChatGPT. When I send GPT 5.1 Thinking on a legal task and tell it to check and cite all sources, it takes +10 minutes to answer, but it did check everything and cite all its sources in the text; whereas the Gemini models, even 3 Pro, always answer after a few seconds and never cite their sources, making it impossible to click to check the answer. It makes the whole model unusable for these tasks. (I have the $20 subscription for both)

I just always thought the taste of gpt or claude models was more interesting in the professional context and their end user chat experience more polished.

are there obvious enterprise use cases where gemini models shine?

I am playing with Gemini 3 and the more I do the more I find it disappointing when discussing both tech and non-tech subject comparatively to ChatGPT. When it comes to non tech it seems like it was heavily indoctrinated and when it can not "prove" the point it abruptly cuts the conversation. When asked why, it says: formatting issues. Did it attend weasel courses?

It is fast. I grant it.

I hate adding -preview to my model environment variable

ChatGPT still has 81% market share as of this very moment, vs Gemini's ~2%, and arguably still provides the best UX and branding.

Everyone and their grandma knows "ChatGPT", who outside developers' bubble has even heard of Gemini Flash?

Yea I don't think that dynamic is switching any time soon.

No matter the model, AI Overview/Results in Google are just hallucinated nonsense, only providing roughly equivalent information to what is in the linked sources as a coincidence, rather than due to actually relying on them.

Whether DuckDuckGo, Kagi, Ecosia or anything else, they are all objectively and verifiably better search engines than Google as of today.

This isn't new either, nor has it gotten better. AI Overview has been and continues to be a mess that makes it very clear to me anyone claiming Google is still the "best" search engine results wise is lying to themselves. Anyone saying Google search in 2025 is good or even usable is objectively and verifiably wrong and claiming DDG or Kagi offer less usable results is equally unfounded.

Either fix your models finally so they adhere to and properly quote sources like your competitors somehow manage or, preferably, stop forcing this into search.