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Gemini 3 Flash: Frontier intelligence built for speed

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Re: Gemini 3 Flash: Frontier intelligence built for speed

#501

Post by inshard »

MillionOClock wrote: post #499
inshard wrote: post #495

Tested it on Gemini CLI and the experience as good if not better than Claude Code. Gemini CLI has come a long way and is arguably likely to surpass Claude Code at this rate of progress.

What are your favorite features? I recently downloaded it and also use Codex CLI and GitHub Copilot in VS Code but I don't really know what specific features it has others might not have.

The UI is better - they box the specific types of actions the orchestrator agent takes with a clear categorization. The standard quality of life shortcuts like type a number to respond to an MCQ are present here as well. They use specialized sub agents such as one with big context window to find context in the codebase. The quotas appear to be much more generous vs CC. The agent memory management between compacting cycles seems to have a few tricks CC is missing. Also, with 3.0 Flash, it feels faster with the same level of agency and intelligence. It has a feature to focus into an interactive shell where bash commands are being executed by the orchestrator agent. Doesn't feel like Google is trying to push you to buy more credits or is relying on this product for its financial survival - I suspect CC has some dark patterns around this where the agents runs cycles of token in circles with minimal progress on bugs before you have to top up your wallet. Early days still.

Re: Gemini 3 Flash: Frontier intelligence built for speed

#502

Post by eldenring »

nowittyusername wrote: post #486
skerit wrote: post #276

Earlier quoted context omitted.

Pulling ahead? Depends on the usecase I guess. 3 turns into a very basic Gemini-CLI session and Gemini 3 Pro has already messed up a simple `Edit` tool-call. And it's awfully slow. In 27 minutes it did 17 tool calls, and only managed to modify 2 files. Meanwhile Claude-Code flies through the same task in 5 minutes.

Knowing Googles MO, its most likely not the model but their harness system that's the issue. God they are so bad at their UI and agentic coding harnesses...

I think Claude is genuinely much smarter, and more lucid.

Re: Gemini 3 Flash: Frontier intelligence built for speed

#503

Post by ozim »

lambda wrote: post #360
prettyblocks wrote: post #313

Earlier quoted context omitted.

I don't think tricky niche knowledge is the sweet spot for genai and it likely won't be for some time. Instead, it's a great replacement for rote tasks where a less than perfect performance is good enough. Transcription, ocr, boilerplate code generation, etc.

The thing is, I see people use it for tricky niche knowledge all the time; using it as an alternative to doing a Google search. 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 hallucinat…

That’s riding hype machine and throwing baby with bath water.

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.

Re: Gemini 3 Flash: Frontier intelligence built for speed

#504

Post by jasonjmcghee »

unsupp0rted wrote: post #131
samyok wrote: post #4

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

How good is it for coding, relative to recent frontier models like GPT 5.x, Sonnet 4.x, etc?

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)

Re: Gemini 3 Flash: Frontier intelligence built for speed

#505

Post by Imustaskforhelp »

user34283 wrote: post #396
leemoore wrote: post #354

Earlier quoted context omitted.

What demographic are you in that is leaving anthropic in mass that they care about retaining? From what I see Anthropic is targeting enterprise and coding. 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 fin…

Enterprise is slow. As for developers, we will be switching to Google unless the competition can catch up and deliver a similarly fast model. Enterprise will follow. I don't see any distinction in target markets - it's the same market.

Yeah, this is what I was trying to say in my original comment too.

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.

Re: Gemini 3 Flash: Frontier intelligence built for speed

#506

Post by Palmik »

awestroke wrote: post #494
Palmik wrote: post #492

Earlier quoted context omitted.

No offense, but that seems like a poor benchmark. These initial vibe checks are easily swayed by personal brand biases.

The brand bias is heavily against Google, not in Googles favor

In context of AI I'm mostly seeing anti-OpenAI pro-Google bias.

Re: Gemini 3 Flash: Frontier intelligence built for speed

#507

Post by Imustaskforhelp »

FuckButtons wrote: post #295
xbmcuser wrote: post #271

Earlier quoted context omitted.

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 p…

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.

Yeah I have a similar opinion and you can go back almost a year when claude 3.5 launched and I said on hackernews, that its good enough

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.

Re: Gemini 3 Flash: Frontier intelligence built for speed

#508

Post by danpalmer »

nl wrote: post #485
danpalmer wrote: post #462

Earlier quoted context omitted.

I thought it was generally accepted that inference was faster on TPUs. This was one of my takeaways from the LLM scaling book: https://jax-ml.github.io/scaling-book/ – TPUs just do less work, and data needs to move around less for the same amount of processing compared to GPUs. This would lead to lower latency as far as I understand it. The citation link you provided takes me to a sales form, not an FAQ, so I can't s…

Sorry I meant Groq custom hardware, not Grok! I don't see any latency comparisons in the link

The link is just to the book, the details are scattered throughout. That said the page on GPUs specifically speaks to some of the hardware differences and how TPUs are more efficient for inference, and some of the differences that would lead to lower latency.

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.

Re: Gemini 3 Flash: Frontier intelligence built for speed

#509

Post by clarkmoreno »

Palmik wrote: post #506
awestroke wrote: post #494

Earlier quoted context omitted.

The brand bias is heavily against Google, not in Googles favor

In context of AI I'm mostly seeing anti-OpenAI pro-Google bias.

Facts. These HN threads are half astroturfing and paid shills. Near impossible to decifer authentic takes that are not actual colleagues or people IRL

Re: Gemini 3 Flash: Frontier intelligence built for speed

#510

Post by danpalmer »

mips_avatar wrote: post #453
nl wrote: post #409

Earlier quoted context omitted.

> GPUs are necessarily higher latency than TPUs for equivalent compute on equivalent data. 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…

I'm pretty sure xAI exclusively uses Nvidia H100s for Grok inference but I could be wrong. I agree that I don't see why TPUs would necessarily explain latency.

To be clear I'm only suggesting that hardware is a factor here, it's far from the only reason. The parent commenter corrected their comment that it was actually Groq not Grok that they were thinking of, and I believe they are correct about that as Groq is doing something similar to TPUs to accelerate inference.
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