New numbers just dropped: DeepSeek V4.1 Flash needs only one-fourth the high-bandwidth memory and one-eighth the SSD storage of its previous full-size model, while running at roughly 200 tokens per second. That’s a model matching last generation’s frontier at a fraction of the cost. I first caught these details in a breakdown from Matthew Berman, the AI creator who put the model through a series of hands-on tests and shared exactly where it shines and where it falls apart.
What grabbed me right away is the honesty here. The benchmarks look almost too good, and the video’s author doesn’t just take them at face value. He runs the model himself, and the results tell a more complicated story. Let me walk you through it.
The stat behind the hype
DeepSeek V4.1 Flash is a 552 billion parameter mixture of experts model. That sounds huge, but the original poster points out it’s actually mid-sized these days, where top closed models reportedly reach into the trillions of parameters. The magic is in the efficiency.
Here’s the part that made me sit up:
- 📊 Only 8 billion active parameters for input and 16 billion for output. Out of 552 billion total, just a sliver of the weights fire for any given answer.
- 📊 On the DeepSeek suite benchmark it scored 74.2, beating both Opus 5 and GPT 5.6 in the creator’s chart.
- 📊 Pricing runs as low as 15 cents per million input tokens off-peak, with output at 60 cents per million off-peak.
Mixture of experts is the key idea. As the video’s author explains it, the model figures out what you’re asking, routes the question to the small “expert” section of its weights that handles that topic, and skips the rest. Less compute per answer means blazing speed and tiny cost.
Why the memory angle matters
The expert digs into something most model launches ignore: memory prices. High-bandwidth memory, the fast RAM that AI chips rely on, has been spiking. He shows how DRAM went from around $10 per gigabyte back up toward $25 during the AI boom, reversing decades of falling prices. Supply is tight because AI is eating it all.
So DeepSeek’s answer is clever. By slashing the memory footprint through algorithmic tricks, they serve way more tokens without buying mountains of expensive hardware. The contributor traces the shrink across versions: eight times smaller from V1 to V3.2, then 13 times smaller to V4 Flash, then another 4x to V4.1. That’s the real story, and it’s why this thing feels so fast.
3 ways you could actually use it
Based on what the creator tested, here’s where this model fits:
- High-volume, everyday tasks. The person who shared it makes a sharp point: most of the economy doesn’t need the absolute best answer. Building websites, drafting PDFs, routine writing. For maybe 95% of use cases, a cheap, fast, capable model does the job fine.
- Speed-critical workflows. He generated a thousand-word essay in about six seconds. If you’re running an app where users wait on output, that responsiveness changes the whole experience.
- Private, self-hosted setups. It’s open weights. You can download it, run it on your own cloud, fine-tune it, and never hand DeepSeek your data. Once it’s quantized, folks with enough VRAM may run it locally.
One bonus the author flags: you can plug this model straight into Codex. Just add the model, give it an API key, and as long as it has a compatible endpoint, you’re set.
Tips and pitfalls to keep in mind
This is where the reviewer earns his keep, because the benchmarks and reality didn’t line up.
- The Rubik’s Cube test failed. He asked the model to build a scramble-and-solve simulation, something he says most models handle. The physics looked fine, but scrambling broke it, colors changed on their own, and the “solve” button just replayed moves in reverse instead of using a real algorithm. He called it a genuine disappointment.
- The paint test came out abstract. Inspired by a Paintbench post from his teammate Alex, he had the model recreate a headshot in a Paint-style tool. The result was stylized with almost no detail. It couldn’t layer brush strokes the way top models can.
- The bullet-through-water simulation was mixed. The app itself had nice controls for muzzle speed, caliber, and spin, but the actual simulation left something to be desired.
The takeaway from the mind behind the review: this is a workhorse, not a genius. When you need the single best answer, you still pay up for a top-tier closed model, and that can be orders of magnitude pricier. But when good enough is truly good enough, cheap and fast wins.
He also lays out the bigger pattern worth remembering. The frontier races ahead, then about six months later an open model matches the previous generation, and six months after that it runs on your own machine. More and more tokens are shifting to these cheaper, faster open models, and he frames that as a healthy thing for the whole ecosystem.
I think that framing is the real lesson here. Don’t chase the biggest model out of habit. Match the model to the job, and you’ll save a fortune without losing much quality.
Want to see the failed Rubik’s Cube, the paint experiment, and the raw speed test for yourself? Check out the full video for all the demos and details.