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They went too far, now the Flash model is competing with their Pro version. Better SWE-bench, better ARC-AGI 2 than 3.0 Pro. I imagine they are going to improve 3.0 Pro before it's no more in Preview.

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.

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 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

Does this imply we don't need as much compute for models/agents? How can any other AI model compete against that?
Pretty stoked for this model. Building a lot with "mixture of agents" / mix of models and Gemini's smaller models do feel really versatile in my opinion.

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

These flash models keep getting more expensive with every release.

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.

Two quick questions to Gemini/AI Studio users:

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.)

Looks like a good workhorse model, like I felt 2.5 Flash also was at its time of launch. I hope I can build confidence with it because it'll be good to offload Pro costs/limits as well of course always nice with speed for more basic coding or queries. I'm impressed and curious about the recent extreme gains on ARC-AGI-2 from 3 Pro, GPT-5.1 and now even 3 Flash.
Even before this release the tools (for me: Claude Code and Gemini for other stuff) reached a "good enough" plateau that means any other company is going to have a hard time making me (I think soon most users) want to switch. Unless a new release from a different company has a real paradigm shift, they're simply sufficient. This was not true in 2023/2024 IMO.

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.

Really hoping this is used for real time chatting and video. The current model is decent, but when doing technical stuff (help me figure out how to assemble this furniture) it falls far short of 3 pro.
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Yet again Flash receives a notable price hike: from $0.3/$2.5 for 2.5 Flash to $0.5/$3 (+66.7% input, +20% output) for 3 Flash. Also, as a reminder, 2 Flash used to be $0.1/$0.4.
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From the article, speed & cost match 2.5 Flash. I'm working on a project where there's a huge gap between 2.5 Flash and 2.5 Flash Lite as far as performance and cost goes.

-> 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.

Thinking along the line of speed, I wonder if a model that can reason and use tools at 60fps would be able to control a robot with raw instructions and perform skilled physical work currently limited by the text-only output of LLMs. Also helps that the Gemini series is really good at multimodal processing with images and audio. Maybe they can also encode sensory inputs in a similar way.

Pipe dream right now, but 50 years later? Maybe

I've been using the preview flash model exclusively since it came out, the speed and quality of response is all I need at the moment. Although still using Claude Code w/ Opus 4.5 for dev work.

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.

Not only it is fast, it is also quite cheap, nice!
This is awesome. No preview release either, which is great to production.

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...

Will be interesting to see what their quota is. Gemini 3.0 Pro only gives you 250 / day until you spam them with enough BS requests to increase your total spend > $250.
Is there a way to try this without a Google account?
Pricing is $0.5 / $3 per million input / output tokens. 2.5 Flash was $0.3 / $2.5. That's 66% increase in input tokens and 20% increase in output token pricing.

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.

this is why samsung is stopping production in flash
I wonder if this suffers from the same issue as 3 Pro, that it frequently "thinks" for a long time about date incongruity, insisting that it is 2024, and that information it receives must be incorrect or hypothetical.

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

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