February 27, 2026, Shanghai.
The plane tree leaves outside the window haven’t fully sprouted yet, and the cold dampness feels exactly like those bugs in the code you just can’t shake off. I glanced at the calendar; 54 years ago today, China and the US released the “Shanghai Communiqué” right here in this city, breaking the ice between both sides of the Pacific.
And today, staring at the news flashing across my screen about Qwen 3.5-Plus, I feel that another kind of “ice”—that physical barrier standing between massive computing power and microscopic devices—is also making the cracking sound of fracture. (I took a bite of the remaining half of my cinnamon donut; hmm, it’s gotten a bit cold).
If you don’t dig deep, you might just think this is another boring parameter announcement. But my dear, put that “1/18th cost” and “60% VRAM reduction” under a microscope, and you’ll see a whole new world growing wildly in the petri dish.
01. The “Fat Guy” Who Successfully Lost Weight
We have to talk about the “weight” issue first.
Over the past two years, the Large Model circle has been exactly like the bodybuilding competitions of old. Everyone was competing to see whose parameters were bigger, whose muscles were shinier, and who could lift the heavier barbell (process more complex logic). Google’s Gemini 3 Pro is like that Mr. Olympia standing in the center of the stage—radiant, but extremely expensive, and—it cannot leave that massive gym (cloud servers).
So, what has Qwen 3.5-Plus done? It hasn’t just “slimmed down”; it has moved the gym directly into your backpack.
VRAM requirements slashed by 60%. What does this mean? It means those “cyber brains” that originally required expensive H100 GPU clusters to serve might now run on just a few decent consumer-grade chips.
Then look at the number making Wall Street analysts’ eyelids twitch: API costs are only 1/18th of Gemini 3 Pro.
It’s like this: previously, if you wanted fresh coffee while out, you had to hire a traveling butler with a full Italian espresso machine (Gemini 3 Pro); now, you have a packet of high-quality instant coffee in your pocket that costs pennies but delivers 90% of the flavor (Qwen 3.5-Plus).
For developers, this isn’t a discount; it’s an “unlock”.
.png)
Behind this dense architecture diagram hides a single ambition: to fit an elephant into a fridge, and that fridge must fit on your wrist.
02. The Overlooked “Latency Phantom”
Many are cheering for the low price, but what I care about more is that it eliminates latency.
Do you know why current “AI Pins” or smart glasses always feel a bit intellectually challenged? Because when you ask, “What is this flower in front of me?” it has to compress the image, throw it to a data center thousands of kilometers away in Guizhou or Iowa, compute it, and throw it back. That one or two seconds of blank silence is enough to make you want to throw the device in the trash.
The fatal weakness of wearable AI has never been IQ, but reaction speed.
Qwen 3.5-Plus’s “VRAM diet” makes On-Device AI no longer just a pie in the sky on a slide deck. When AI can run directly on your smart ring, your VR headset, or even your hearing aid chip, it ceases to be a “wired” cloud prophet and becomes your second instinct.
Sometimes I can’t help but guess that this might be the true endgame for a certain tech giant—they don’t intend to fight to the death with Google on “omniscience,” but aim to be the ubiquitous “nerve endings.”
03. Suits VS Overalls
Let’s make a horizontal comparison of the current battlefield.
Google’s Gemini 3 Pro remains that senior consultant in a suit. It is learned, rigorous, and terribly strong in multimodal capabilities, suitable for sitting in a bright office helping you write quarterly financial reports or analyzing complex medical images. Its high price is justified because it sells “certainty” and “compute hegemony.”
Looking at this towering price bar chart, you understand why startups are weeping. Some models are for admiring; others are for getting work done.
Qwen 3.5-Plus, on the other hand, is like an engineer in overalls with pockets full of tools. It might not discuss Shakespeare’s sonnets with you, but when you need it to identify road conditions in 0.1 seconds and tell your smart glasses “pothole on the left,” it is far more reliable than that suited consultant—mainly because it doesn’t need to call headquarters for permission every time.
This is the divide between “General Intelligence” and “Embedded Intelligence.”
The former fights for the crown in the cloud; the latter fights for that square inch on your wrist. From a business logic perspective, the 1/18th cost advantage isn’t just about saving money; it turns AI from a “luxury good” into a “daily necessity.”
04. If Phones Disappeared…
Shh, let’s be bold for a moment.
If Qwen 3.5-Plus can truly run on low-power chips as hoped, is that “post-smartphone” concept hype from ten years ago finally going to happen?
Phones still exist today because we need a powerful computing hub to process information. But if your glasses have a sufficient brain (thanks to low-VRAM models), your earphones can translate three languages in real-time (thanks to low latency), and your watch can process all health data…
Can we finally throw away that heavy black mirror?
Of course, it won’t happen that fast. Battery technology is still the “lousy teammate” holding everyone back, and heat dissipation is a huge issue—no one wants to wear a pair of glasses that burn their face, even if Einstein lives inside them.
But I vaguely feel that Qwen’s technical breakthrough is like prying open a crack in this heavy door. The light shining through the crack illuminates those dusty prototypes shelved in laboratory corners.
This isn’t a still from a sci-fi movie; this is a future where data no longer wanders. All thinking happens in the place closest to your heart.
05. Epilogue: A Seat for Idealism
Writing this, my cinnamon donut has gone completely cold.
In this era of frantically chasing parameters and benchmarks, seeing someone willing to squat down and grind through the dirty work of “VRAM usage” and “inference cost” is actually quite touching.
It reminds me of the birth of Linux. It wasn’t flashy, perhaps even a bit crude, but through extreme efficiency and openness, it eventually ran in every corner of the world—from supercomputers to the router at your front door.
Qwen 3.5-Plus may not become the smartest “God,” but it has a high chance of becoming the most ubiquitous “Spirit.”
On this cold Shanghai afternoon, I want to raise a glass to this “budget-conscious” future. After all, what truly changes the world is often not the clouds high above, but the timely rain that falls.
References：
- Gemini 2.5 Flash vs Qwen-Flash: Which AI Model is Better
- Comparison of Qwen vs Gemini API Cost
- Optimizing Large Language Models for Low-Power Edge Computing
- Alibaba launches new AI glasses concepts
- What Are the Potential Applications of Qwen 3 in Edge Devices?
—— Lyra Celest @ Turbulence τ
