alt Hacker NewsGemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber
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Google somehow managed to snatch defeat from the jaws of success with their AI products.
They literally forced me and my company out of Antigravity by phasing out AI Ultra subscription without any proper product follow-up. Antigravity IDE cannot even have poweruser subscriptions now from Google Workspace an Gemini Enterprise Agent Platform cannot be attached to Antigravity IDE.
Gemini Enterprise Agent Platform has an incredibly abysmal setup process, and if I want to limit spending per-user I have to create projects per user. The fact that you cannot activate Anthropic models on it if the billing still has free credits is almost a joke.
I was a big proponent of Google and Gemini, but they left us reeling with their abrupt product decisions. Forced us to buy $200 subscriptions directly from Anthropic/OpenAI.
Pelicans for 3.6 Flash and 3.5 Flash-Lite (Cyber isn't available to me through the API yet.)
https://tools.simonwillison.net/markdown-svg-renderer#url=ht...
It's a bit disheartening to see no comparison to other models here - and I'm not sure this pushes the curve anywhere. 3.6 flash is more expensive than GLM 5.2 - but seemingly worse, although this post is really light (lite?) on details.
It seemed for a time that Google had finally gotten the ball rolling, but I'm doubting that more and more as time passes. We'll see what happens with 3.5 pro I suppose.
Pricing per million input/output tokens:
2.5 Flash: $0.3 / $2.5
3.0 Flash: $0.5 / $3
3.5 Flash: $1.5 / $9
3.6 Flash: $1.5 / $7.5
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2.5 Flash-Lite: $0.1 / $0.4
3.1 Flash-Lite: $0.25 / $1.5
3.5 Flash-Lite: $0.3 / $2.5
A couple tidbits:
> Beyond today’s releases, Gemini 3.5 Pro is currently testing with partners and we plan to make it broadly available as soon as it’s ready.
> We have started our most ambitious pre-training run yet, for Gemini 4, and are excited by the progress.
It's scary relying on Google's models.
I have a very price sensitive workload that used to run on flash 2.5 lite - it's deprecated now.
The replacement 3.1 flash lite is a lot more expensive, but now also has a sunset date.
3.5 flash lite is even more expensive.
So the price is rising and you have no choice but to keep paying more and more.
It is both less intelligent and more expensive than GLM-5.2, while being closed weight.
No word about updating Jules, which is still stuck on 3.1 Pro. I get that it's probably niche but I've really appreciated basically being able to give directions to Jules on my phone, then reviewing and merging a GitHub PR fifteen minutes later. It's been great for getting some progress in on a few personal projects during my commute when I can't exactly pull out my laptop.
Anyone have any good alternatives?
Bottom line it looks about on equal footing with GLM 5.2 in terms of both overall intelligence and cost per task, while being significantly faster (in fact it is the fastest model on artificial analysis as of rn [0])
Really, what's up with Gemini still not supporting connectors/MCPs/plugins/whatever-they're-called-this-month on web? It makes it a non-starter for any kind of serious use.
Wow, Laguna S 2.1 (released today) just destroys Flash-Lite underly and completely. What a weak and embarrasing release from Google.
3.5 Flash-Lite seems available in US region, as was 3.5 Flash; but 3.6 Flash looks Global only so far when pinging. If Google employees are watching, will this issue go away?
The mention of an "ambitious" gemini 4 pre-train signals to me that 3.5 pro is probably a lost cause.
That being said, it seems that Gemini is still the best image analysis model, so hopefully 3.6 flash builds on this even more.
Their naming scheme is confusing. Branding has never been Google's strong suit and their marketing copy is pretty bottom-of-the-barrel[0]. Anthropic has a pretty clear set of models but Gemini decided to rebrand their Flash as Flash Lite (and presumably the future will see a Flash Lite Mini, a Flash Lite Mini Nano and a Flash Lite Mini Nano 3B) which confuses the pricing to high hell.
This plus the Vertex, AI Studio, Gemini, Antigravity. It's honestly too confusing to use. I need to use Gemini just to decide on which platform and which model to consider.
0: Famous Kurian Tweet: "We're announcing Duet AI for Google Workspace will now be Gemini for Google Workspace. Consumers and organizations of all sizes can access Gemini across the Workspace apps they know and love. We're introducing a new offering called Gemini Business, which lets organizations use generative AI in Workspace at a lower price point than Gemini Enterprise, which replaces Duet AI for Workspace Enterprise."
Spent half an hour just now benchmarking it against my current 3.5 Flash pipeline excited only see it regressed slightly (0.1% - 0.2% at most, for feature extraction work)
Seems like this is mostly a cost play by Google, hoping this doesn't bring 3.5 Flash capabilities to an end of life, and that 3.6 catches up or gets better.
I have a side business selling custom fingerprint jewelry and I use gemini nano banana to clean up customer submitted fingerprint images. This was a step I used to do by hand at 10 - 15 minutes per image and nano banana is the first model that is able to do the task (it is astonishingly good at it). I can't wait to see what the next nano banana can do, hopefully its released soon.
In other good news "the model has been trained to minimize refusals for beneficial uses.".
Otherwise, this news feels like a tiny incremental improvement on Gemini Flash series to make it more efficient with token usage, subagent and cost. Nothing big.
Regarding their benchmark scores on CyberGym, I wonder why they didn't compare their 3.5 Flash Cyber model with Fable 5. I mean they included Mythos and GPT-Cyber, so why not Fable 5 too?
They also mentioned Gemini 3.5 Pro is in testing and its about to become available very soon. Another thing maybe worth discussing is the announcement of pre-training Gemini 4. Sadly, not much technical details to discuss on. Many comments in here seem to mostly be about how Google is behind the others, but honestly, is it really worth the investment to be #1 in Artifical Analysis every week?
Google seems to have anorexia when it comes to model intelligence. They have an internal hard constraint on price per token it seems, and they are trying to squeeze out intelligence with limited compute.
I wonder if there is something with their TPU cycles that makes them want to postpone training a new model. My guess is that they have been on the same base model for 6 months and they may have waited for the next gen TPUs to train Gemini 4, which greatly limits how much intelligence they can increase and forces them to do cost efficiency increases.
Unfortunately says more about how competitive 3.5 pro would be today at the frontier if they forgo it for 3.6 flash.
I often use Gemini free web chat because it's generally quite good at web search-related questions (apparently it has direct token-level access to the Google Search index) but I noticed in the last two weeks output quality of 3.5 Flash seriously degraded. Maybe they were switching over systems.
Wow - Google does not even bother to show benchmarks of these models compared to the frontier and Chinese labs - only against previous versions. I'm not surprised. Having worked there for years it was amazing just how inwardly looking the company is.
Google has not changed. Following two facts are like tautologies by now.
1. Their AI efforts are very fundamental research oriented. They are really good at it.
2. Their productization sucks. The end products gets little attention compared to competition. It can be canceled at any time. You should never build anything around Google only APIs, AI or not.
3.5-lite is the real showpiece here; agentic models of this size are a huge value-add for 90% of knowledge work agent tasks
Pelican svg and a near-perfect 3D MacBook at max effort for $0.16, about a fifth of Fable's price.
Fable 5 still wins on detail with no visible errors, but it's close. And this isn't a memorized pelican;
https://playcode.io/blog/macbook-svg-benchmark#gemini-3-6-fl...
Why would 3.6 flash perform a little worse than 3.5 flash on Artificial Analysis Coding Index...
https://artificialanalysis.ai/models/gemini-3-6-flash?intell...
Tons of guardrails, lazy model, super confusing plans, expensive 3.5/3.6 flash and lite and 3.5 pro MiA?
Rough patch for google ai
I have just tried to switch to 3.6 instead of 3.5 in antigravity and it seems to constantly spit "critical instruction: STOP CALLING TOOLS NOW. YOU MUST WAIT FOR WAKEUP. ". I think I will switch back to 3.5
Kind of excited about this. 3.5 Flash on Antigravity has surprised me recently on a hobby project. When given opportunity to plan, it can deliver on tasks that would take me a while on my own and generates responses at blazing speeds - compared to what I'm used to at work with Opus 4.8 (granted I don't use Opus 4.8 on my hobby projects so just anecdotal). While with Gemini CLI I would just watch it run in circles and run out of 5h allowance before anything useful is produced (or even approached).
I'm more excited for 3.5 pro. Gemini has fallen behind in some areas, but is still one of the best multimodal models.
Has anybody found any models better at image or audio analysis?
It is 17% more token-efficient than 3.5 and performs significantly better in coding and tool usage benchmarks.
It is also cheaper than 3.5:
> This enhanced efficiency is also combined with a lower price than 3.5 Flash. At $1.50/1M input tokens and $7.50/1M output tokens, 3.6 Flash reduces the overall cost per agentic task, making agents more cost-effective to build and run.
I struggle to see any value in this when DeepSeek is still a thing.
Always happy to see new Gemini releases as IMO Antigravity Pro 16.67/mo plan (Annual) is still the best plan available and have been pretty happy with Antigravity IDE.
If it wasn't for Gemini/Antigravity I'd have to go with a Max Claude plan, as it stands now I can get by with just a Claude Pro plan to get Opus when I need it, whilst using Antigravity as my day-to-day workhorse.
Unfortunately Gemini Flash became too expensive to use as a general purpose model (i.e. for AI features in Apps), luckily there are plenty of cheaper Chinese models to fill that gap now.
Don't know why are they even pursuing Gemini. Just download the Kimi, call it Kimini and serve it on your GPU. Maybe then train next architecture based on this!
Feels like they released this to ride the wave of press of GPT-5.6, Kimi K3, and Qwen 3.8. Doesn't feel like Google has much substance with this post except a bump in version and tweaked their pricing.
Gemini 2.5 Flash-Lite has been my go to for cheap document processing at scale (especially with 50% off batch mode), but they are really boiling the frog with pricing increases with each version:
gemini-2.5-flash-lite: $0.10 input / $0.40 output
gemini-3.1-flash-lite: $0.25 input / $1.50 output
gemini-3.5-flash-lite: $0.30 input / $2.50 output (a 6.25x increase over 2.5!)
Now watch them deprecate Gemini 2.5 Flash-Lite in the coming months...
Nice to see that it's cheaper than 3.5
LLM reception is truly extreme, even worse than AAA game releases.
Ever frontier lab lived it at least once : missing the frontier by a few months triggers extremly negative reactions, then you take back the lead for 2 weeks, and the hype cycle repeats.
3.6 Flash scores exactly the same as 3.5 Flash on the Artificial Analysis index. Better on some tasks, worse on others. Mostly within what I'd consider the noise window. Looks pretty much indistinguishable from 3.5 Flash, at least on these benchmarks: https://artificialanalysis.ai/models/gemini-3-6-flash
Looks like 3.6 Flash is the first model with their newest pretraining run (cutoff date is 2026/03), long after 2.5 series.
For anyone wanting a faster overview: I ran the Gemini 3.6 Flash and 3.5 series release notes through NotebookLM and generated a short video summary. Link: https://www.youtube.com/watch?v=SUFBhvQ2tY4
IMO Gemini has the best free tier models/app for everyday use. Muse-Spark is perhaps just slightly better, but has none of the connectivity to my GApps (for things like “create a recipe in my Google Docs from this image”).
Plus they are probably running these things on every Google search so saving tokens is a huge win for them.
I'm a big fan of the Flash-Lite models. They're exceedingly fast and deliver great outputs for high volume use cases where you need to process requests at scale. Can't wait to try the newer version.
I deeply wish Google would focus on models like Gemma. Small, powerful, open-weight models you can run on phones or regular computer hardware.
Flash Lite: 0.3/m and 2.5/m
Deepseek Pro: 0.435/m 0.87/m
That's wildly ambitious pricing by Google. You can maybe get away with spicy pricing at the SOTA edge but at the lower tiers everything is a lot more price sensitive.
So 3.6 Flash is a somewhat of an admission that Google miscalculated by charging 3-5x for 3.5 Flash what it did for 3.0 Flash (3x input and output costs plus large token inefficiency changes) despite only modest improvements?
3.5 Flash Lite is only a hair cheaper than 3.0 Flash, but I think 3.0 Flash is a massively more capable model?
Proof-of-life release while they figure out how to have a competitive frontier model release. My hunch is they pushed too far in the "omni" model direction, that they made something so ungainly, it wasn't as good for normal tasks.
It does seem like their releases are getting closer together. I get the feeling they realized they were trying to roll out to their entire ecosystem and now they’re focusing more just directly on the AI model itself. I think give it a little time and they’ll start to be one of the competitors too.
I was expecting 3.6 Pro. It's been so long since the last Pro model...
"..and in some benchmarks like DeepSWE by Datacurve, we observe up to 65%, all at a lower cost per output token."
"3.6 Flash delivers higher precision with fewer unwanted code edits and reduced execution loops, as seen in DeepSWE (49% vs. 37%)"
So which one is it? 65% or 49%?
I wonder how big the Pro model is that Google is using behind the scenes to train these smaller ones.
Going on baseless speculation, the lack of accompanying pro models with these flash releases either means: 1) the model is too big to be economical, 2) google doesn't have the compute to serve the big model, 3) their big model has too many alignment issues to serve to the public.
edit: looks like benchmarks are up on https://artificialanalysis.ai/models/gemini-3-6-flash. It's solidly middle-of-pack. However, if you want to be most fair to flash, look at the intelligence vs time per task and intelligence vs outputspeed benchmarks. This is a very fast model.
edit 2: I use antigravity from time to time and in my experience, 3.5 flash is an underrated model, so long as you know what it's good for. It's very good at frontend (much better than gpt 5.5) and it's fast, so it's a great tool for iteration. I expect 3.6 to be no different.