I was looking at a model showdown benchmark from AIHubMix on GitHub.

The setup is straightforward: same prompt, one shot, real generated HTML artifacts, and no manual cleanup. The round I focused on was the "Global View" task: build a live-data 3D flight/logistics dashboard with a dark globe, day/night terminator, atmosphere, glowing flight arcs, glassmorphic stat cards, orbit/zoom controls, and an auto-demo style experience.

The four models in this round were:

• Kimi K3
• GPT-5.6 Sol
• Claude Fable 5
• Gemini 3.6 Flash

My subjective impressions after opening the generated HTML outputs:

Kimi K3

This felt like the best overall balance. It generated a complete dashboard with a 3D globe, flight arcs, labels, statistics cards, an activity table, a risk panel, 3D/2D toggles, and interactive controls. While it wasn't quite as visually refined as the strongest-looking output, it followed the prompt very well and felt like something you could genuinely build upon.

GPT-5.6 Sol

This was the most polished visually. The composition, glassmorphism, copywriting, route visualization, and overall SaaS-style presentation looked the closest to a production-ready landing dashboard. If visual presentation is the main priority, this was my favorite.

Claude Fable 5

Claude produced a strong result with a large globe, detailed logistics tables, operational statistics, and a richer information layout. It followed the prompt well, although I personally found the overall visual presentation a little less refined than GPT's.

Gemini 3.6 Flash

Gemini generated a functional dashboard with a globe, statistics, activity data, and controls. It completed the task, but compared with the others the layout felt more minimal and the overall presentation wasn't as visually rich.

My personal ranking for this benchmark:

• Best visual polish: GPT-5.6 Sol
• Best overall balance: Kimi K3
• Most detailed alternative: Claude Fable 5
• Most lightweight implementation: Gemini 3.6 Flash

The interesting part is that there isn't a single "winner." It depends on what matters most. If you're optimizing for first-shot visual quality, GPT stood out to me. If you're looking for a strong balance between completeness and overall efficiency, Kimi K3 was the most interesting result. Claude delivered a solid middle ground, while Gemini offered a simpler but still functional implementation.

My takeaway:

When comparing one-shot HTML or app-generation benchmarks, it's useful to evaluate more than just aesthetics. Completeness, prompt adherence, implementation quality, and overall efficiency can matter just as much as visual polish, especially for workflows where you'll iterate multiple times.

Curious which output everyone else would pick after looking through the generated artifacts.

Gemini holds the highest market share in text and pretty much owns the market for inage generation yet some random folks on this sub claim that no one uses Gemini

Source: Openrouter

https://openrouter.ai/rankings/image?view=month

This is embarrassing to google, but no worries wait guys Google will comeback soon cuz I love google and have faith on then (Google Deepmind)

I asked for the benchmarks (reasoning) of the latest models from different AI families and it gave me an outdated data.

It's a new conversation as well. Funny how 3.6 Flash is the latest model and was advertised as something that is better compared to previous models, but failed on a simple task.

Hey everyone,

If you let the model focus purely on design, it is fast and cheap and allows super fast iterations, this whole test took about 0.28 credits while generating ui for a full app you see the video posted is in realtime generating a Flutter project

I also tested compared to Gemini Flash 3.6 created better results out of the box (see the image) but took about 3.09 credits and longer time what do you think?

https://reddit.com/link/1v39e6d/video/y5dguipwjqeh1/player

Google just released Gemini 3.6 Flash while many people were expecting Gemini 3.5 Pro to arrive and compete with models like Claude and Kimi.

I've been testing Gemini 3.6 Flash, and so far I haven't noticed the improvement I expected. In my personal tests, it sometimes feels worse than Gemini 3.5 Flash, which was one of the strongest lightweight models I've used.

I understand that 3.6 Flash may be focused more on efficiency and agentic workflows rather than a huge jump in raw intelligence. However, I'm curious about other people's experiences.

Has anyone seriously compared Gemini 3.5 Flash and 3.6 Flash?

Is 3.6 Flash better for coding, reasoning, or multimodal tasks?

Are there any areas where it clearly improves over 3.5 Flash?

Or does 3.5 Flash still perform better for many use cases?

I'd like to hear real-world experiences and benchmark results.

Glad I can help train the new model.

So if anyone still isnt aware of this at this point, after briefly claiming the knowledge cut off time is March 2026, google changed the time to Unknown in aistudio

This is quite weird for me, what could possibly be the reason behind this?

Put your (wild) guesses here ;)

Google's a multi-trillion dollar company btw

P.S. I tried 3.6-flash, it's mid, not too much difference spotted from 3.5-flash but it works

Google, what are you doing?

EDIT: I want to clarify something because, although it seemed obvious to me, many people seem to have missed the point. My post wasn't intended as a critique of the capabilities or performance of Gemini 3.6, which I haven't even had the chance to test yet.

I simply wanted to verify whether its internal knowledge had actually been updated to March 2026 as google stated in AI studio. This is why I ran the test without web search enabled; even Gemini 3.1, despite its January 2025 cut-off, can answer correctly if it can browse the web.

EDIT 2: This is in response to everyone who commented on the post claiming I was wrong and that LLMs don't work that way.
If that's the case, explain why Google has just updated the model card on AI Studio, changing the knowledge cutoff from March 2026 to "unknown".
Funny that, isn't it?

I could be wrong, I could be lucky, but Gemini 3.6 Flash (High) has been legitimately so fun to vibe with in Antigravity. I was used to waiting like hours for GPT 5.6 and Fable to do a feature. 3.6 Flash does it in like, 5 minutes. Wtf is this model? I knew to never lose hope in GDM.

I imagine this will bite me in the butt and I'll come back to write about how shit 3.6 Flash is. Praying I don't, lol.

He's the cofounder of Winsurf(Coding tool like Cursor, talent acquired by Google), rn he lead the Antigravity, just some hrs ago he posted this, does anyone use antigravity that much? Cuz he said here they have increased limit.

Was it necessary?

How many of u guys Google AI studio and Antigravity lmk guys? And if u then lmk for what Especially? Or u guys use only codex , CC, Cursor, Opencode, kimi code, qwen code etc?

Thanks for the extra info brochacho

Honestly, not everyone has access to it yet, but I was randomly selected for early access and, in my opinion, it's pretty insane for Google but isn't a massive advancement. These are just my personal impressions after testing it.

I was casually testing Gemini while grabbing Burger King when I randomly noticed the new model the moment I opened the app. My first thought was: "Yo...? Alright, let me test it before posting anything."

So far, I've seen it available in:

• Gemini App
• Google Antigravity
• Google AI Studio

As for coding, there weren't any public benchmarks when I first tried it. Based on my own prompts, though, it performed noticeably better than Gemini 3.5 Flash and Gemini 3.1 Pro for the tasks I tested. From my experience, it also seems more reliable and appears to have improved memory and coding performance. Those are just personal observations, not benchmarked results.

I'm happy Google decided to ship another model instead of staying quiet. It let me continue working on my Google AI project without interruption.

Regarding Gemini 3.5 Pro, there still hasn't been an official release. There have also been reports and speculation that Google may be using a new foundation model trained from scratch rather than simply fine-tuning Gemini 2.5 Pro, but Google has not officially confirmed those architectural details, so treat those claims with caution.

As for the delay of Gemini 3.5 Pro, it looks like Google instead released additional Flash-family models, including Gemini 3.6 Flash, Gemini 3.5 Flash-Lite and the security-focused 3.5 Flash Cyber, while continuing work on the rest of the lineup.

UPDATE (Edited)

Google has officially released its benchmarks, capabilities, and everything else you need to know! Everything here from Google's blog are proven facts. You may also alternatively search up or visit the website, but this link is official. And like expected, you'll see noticeably better benchmarks than previous models. Try it out! For me, it's quite incredible, but I'm curious to see what people have built with it so far. The model is available for all tiers including free tier. It doesn't rank much, but it's a step forward. In my opinion, it's better than staying on the same model for the next few to several months.

Additionally, I would like to apologize for misunderstanding this as an 'early access' but either way, this is a coverage and thank you for your understanding. At first it was posted way before the post and I didn't find any other posts yet covering that, so I decided to create this one. It is a global rollout, by the way.

[Gemini 3.6 Series releases & additional information — Click here to view!]

Google focusing on efficiency instead of benchmaxing is super smart and 3.6 is still very capable for the majority of use cases.

99% of people don't give a damn about frontier model benchmarks and the cost effectiveness for the average business user is exactly what companies are looking for.

Nobody needs an AI god to create a report or organize a spreadsheet quickly. They just need it to be fast and efficient.