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Posted by logickkk1 10 hours ago

Gemini 3.6 Flash, 3.5 Flash-Lite, and 3.5 Flash Cyber(blog.google)
https://console.cloud.google.com/agent-platform/publishers/g...
589 points | 471 commentspage 4
waldrews 5 hours ago|
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?
jdthedisciple 5 hours ago||
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])

[0] https://artificialanalysis.ai/models/gemini-3-6-flash

sega_sai 7 hours ago||
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
nsbk 10 hours ago||
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.

hmokiguess 5 hours ago||
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.

thevinter 9 hours ago||
I struggle to see any value in this when DeepSeek is still a thing.
kzrdude 3 hours ago||
It's smarter than DeepSeek v4 Pro (preview) on several benchmarks, like HLE.
anthonypasq 9 hours ago||
multimodal + latency
parasti 7 hours ago||
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).
zwaps 4 hours ago||
Here's the issue:

GLM 5.2 is better, also cheaper, and almost as fast.

So essentially, a big L for Google. Combine this with them not being able to produce a frontier model this generation... hmm implications

WarmWash 4 hours ago|
3.6 is roughly 50% faster, which isn't totally insignificant for being marginally more expensive.[1]

[1]artificialanalysis.ai

zwaps 4 hours ago||
Sure, but there's no sota alternative from Google. That's it, and its beaten by GLM 5.2 on every measure except somewhat speed.

I find that quite staggering. GLM is open weights

Narkov 2 hours ago||
Speed is definitely a marketable quality. All these things are a trade-off and solely measuring against SOTA I don't feel is always helpful.
mrandish 5 hours ago||
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.
culi 5 hours ago|
Google's Knowledge Graph is a massive advantage no other competitor has. I don't think they've fully utilized its full potential but I don't know if any other company could've built something like Scholar Labs
Andrex 4 hours ago||
Knowledge Graph + automated transcriptions of almost every YouTube video = giant untapped moat of data
weird-eye-issue 50 minutes ago||
It's not exactly untapped, my AI company has scraped YouTube transcripts for 3 years now for RAG
xnx 9 hours ago|
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.
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