> Gemini 3 Flash is able to modulate how much it thinks. It may think longer for more complex use cases, but it also uses 30% fewer tokens on average than 2.5 Pro.

https://artificialanalysis.ai/evaluations/omniscience

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.

claude is coding model from the start but GPT is in more and more becoming coding model

When I ask Gemini 3 Flash this question, the answer is vague but agency comes up a lot. Gemini thinking is always triggered by a query.

This seems like a higher-level programming issue to me. Turn it into a loop. Keep the context. Those two things make it costly for sure. But does it make it an AGI? Surely Google has tried this?

   - "Thinking" is Gemini 3 Flash with higher "thinking_level"
   - Prop is Gemini 3 Pro. It doesn't mention "thinking_level" but I assume it is set to high-ish.

https://deepmind.google/models/gemini-robotics/

Previous discussions: https://news.ycombinator.com/item?id=43344082

Fast = Gemini 3 Flash without thinking (or very low thinking budget)

Thinking = Gemini 3 flash with high thinking budget

Pro = Gemini 3 Pro with thinking

Summarize recent working arxiv url

And then it tells me the date is from the future and it simply refuses to fetch the URL.

so they get lapped a few times and then drop a fantastic new model out of nowhere

the same is going to happen to Google again, Anthropic again, OpenAI again, Meta again, etc

they're all shuffling the same talent around, its California, that's how it goes, the companies have the same institutional knowledge - at least regarding their consumer facing options

https://news.ycombinator.com/item?id=46290797

thinkingConfig: { thinkingLevel: "low", }

More about it here https://ai.google.dev/gemini-api/docs/gemini-3#new_api_featu...

https://artificialanalysis.ai/evaluations/omniscience

https://youtu.be/4p73Uu_jZ10?si=x1gZopegCacznUDA&t=582

https://artificialanalysis.ai/evaluations/omniscience

Prepare to be amazed

More experts with a lower pertentage of active ones -> more sparsity.

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.

https://epoch.ai/benchmarks/simplebench

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?

Kara Swisher recently compared OpenAI to Netscape.

the reason this matters is slowing velocity raises the risk of featurization, which undermines LLMs as a category in consumer. cost efficiency of the flash models reinforces this as google can embed LLM functionality into search (noting search-like is probably 50% of chatgpt usage per their july user study). i think model capability was saturated for the average consumer use case months ago, if not longer, so distribution is really what matters, and search dwarfs LLMs in this respect.

https://techcrunch.com/2025/12/05/chatgpts-user-growth-has-s...

The most terrifying thing would be Google expanding its free tiers.

I do feel like it's not an entirely accurate caricature (recency bias? limited context?), but it's close enough.

Good work!

You should do a "show HN" if you're not worried about it costing you too much.

Might be using flash for my MCP research/transcriber/minor tasks modl over haiku, now, though (will test of course)

This has been true for at least 4 months and yeah, based on how these things scale and also Google's capital + in-house hardware advantages, it's probably insurmountable.

Ghibli moment was only about half a year ago. At that moment, OpenAI was so far ahead in terms of image editing. Now it's behind for a few months and "it can't be reversed"?

https://lmarena.ai/leaderboard/text-to-image

https://lmarena.ai/leaderboard/image-edit

Then you realise you aren't imagining it.

Just do it.

I use a service where I have access to all SOTA models and many open sourced models, so I change models within chats, using MCPs eg start a chat with opus making a search with perplexity and grok deepsearch MCPs and google search, next query is with gpt 5 thinking Xhigh, next one with gemini 3 pro, all in the same conversation. It's fantastic! I can't imagine what it would be like again to be locked into using one (or two) companies. I have nothing to do with the guys who run it (the hosts from the podcast This day in AI, though if you're interested have a look in the simtheory.ai discord.

I don't know how people use one service can manage...

Definitely has not been my experience using 3 Pro in Gemini Enterprise - in fact just yesterday it took so long to do a similar task I’d thought something was broken. Nope, just re-chrcking a source

Maybe someday future models will all behave similarly given the same prompt, but we're not quite there yet

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.

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.

For example, the Gemini 3 Pro collection: https://blog.google/products/gemini/gemini-3-collection/

But having everything linked at the bottom of the announcement post itself would be really great too!

It's when it becomes difficult, like in the coding case that you mentioned, that we can see the OpenAI still has the lead. The same is true for the image model, prompt adherence is significantly better than Nano Banana. Especially at more complex queries.

The merger happened in April 2023.

Gemini 1.0 was released in Dec 2023, and the progress since then has been rapid and impressive.

Well worth every penny now

Just tried once again with the exact same prompt: GPT-5.1-Thinking took 12m46s and Gemini 3.0 Pro took about 20 seconds. The latter obviously has a dramatically worse answer as a result.

(Also, the thinking trace is not in the correct language, and doesn't seem to show which sources have been read at which steps- there is only a "Sources" tab at the end of the answer.)

But for anyone using LLM's to help speed up academic literature reviews where every detail matters, or coding where every detail matters, or anything technical where every detail matters -- the differences very much matter. And benchmarks serve just to confirm your personal experience anyways, as the differences between models becomes extremely apparent when you're working in a niche sub-subfield and one model is showing glaring informational or logical errors and another mostly gets it right.

And then there's a strong possibility that as experts start to say "I always trust <LLM name> more", that halo effect spreads to ordinary consumers who can't tell the difference themselves but want to make sure they use "the best" -- at least for their homework. (For their AI boyfriends and girlfriends, other metrics are probably at play...)

Edit: And just to add an example: openAI's Codex CLI billing is easy for me. I just sign up for the base package, and then add extra credits which I automatically use once I'm through my weekly allowance. With Gemini CLI I'm using my oauth account, and then having to rotate API keys once I've used that up.

Also, Gemini CLI loves spewing out its own chain of thought when it gets into a weird state.

Also Gemini CLI has an insane bias to action that is almost insurmountable. DO NOT START THE NEXT STAGE still has it starting the next stage.

Also Gemini CLI has been terrible at visibility on what it's actually doing at each step - although that seems a bit improved with this new model today.

Founders are special, because they are not beholden to this social support network to stay in power and founders have a mythos that socially supports their actions beyond their pure power position. The only others they are beholden too are their co-founders, and in some cases major investor groups. This gives them the ability to disregard this social balance because they are not dependent on it to stay on power. Their power source is external to the organization, while everyone else is internal to it.

This gives them a very special "do something" ability that nobody else has. It can lead to failures (zuck & occulus, snapchat spectacles) or successes (steve jobs, gemini AI), but either way, it allows them to actually "do something".

Google is great on the data science alone, every thing else is an after thought

>Fast = 3 Flash

>Thinking = 3 Flash (with thinking)

>Pro = 3 Pro (with thinking)

My logic test and trying to get an agent to develop a certain type of CRDT implementations (that is published and thus the model is trained on to some limited extent) really stress test models, 5.2 is a complete failure of overfitting.

Really really bad in an unrecoverable infinite loop way.

It helps when you have existing working code that you know a model can't be trained on.

I don't view this as a "new Flash" but as "a much cheaper Gemini 3 Pro/GPT-5.2"

In fact so far, they consistently fail in exactly these scenario, glossing over random important details whenever you double check results in depth.

You might have found models, prompts or workflows that work for you though, I'm interested.

We've seen this movie before. Snapchat was the darling. Infact, it invented the entire category and was dominating the format for years. Then it ran out of time.

Now very few people use Snapchat, and it has been reduced to a footnote in history.

If you think I'm exaggerating, that just proves my point.

Just go outside the bubble plus take a bit older people

Of course they are. Founders get fired all the time. As often as non-founder CEOs purge competition from their peers.

> The only others they are beholden too are their co-founders, and in some cases major investor groups

This describes very few successful executives. You can have your co-founders and investors on board, if your talent and customers hate you, they’ll fuck off.

"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?

https://en.wikipedia.org/wiki/Tensor_Processing_Unit