The New DeepSeek Moment! And what it could mean???
The AI Company Builder Memo
SubscribeSign in
The New DeepSeek Moment! And what it could mean???<br>Ft. deepseek-v4-flash-0731 edition
Karthik Ravi<br>Aug 04, 2026
19
Share
For the uninitiated, here’s a bit of a catchup on the AI news of the day (or week I guess — I’m a little late to the game with these). High level here’s what happened:<br>Moonshot AI released Kimi K3: A massive 2.8 trillion parameter open model that now sits alongside some of the best closed models out there.
Alibaba released Qwen3.8-Max: Another monster with 2.4 trillion parameters and some pretty insane results across coding, reasoning, and agentic tasks.
Then DeepSeek released V4-Flash-0731: It has 284 billion total parameters, with only 13 billion active at a time, but somehow gets surprisingly close to these much larger models on several major benchmarks.
Let’s start with the biggies (no pun intended hehe). K3 and Qwen3.8-Max are uber massive.<br>Kimi jumped from 1 trillion parameters in K2.6 to 2.8 trillion in K3, nearly tripling in size in a single generation.<br>The jump for Qwen is even crazier. The original open Qwen3 flagship had 235 billion parameters. Qwen3.8-Max has 2.4 trillion. That’s more than a 10x increase in a little over a year.<br>If you chart out these parameter jumps, you’d probably jump to the conclusion that the future of intelligence is going to be predicated massive model size increases.
And if this trend continued, you could make the argument that Dario Amodei’s almost erotic fascination with model size was correct.<br>But the fascinating thing that happened in tandem was DeepSeek.<br>While Kimi and Qwen were busy stuffing another trillion parameters into their models, Wenfeng basically said, “Hold my beer.”<br>DeepSeek went in the exact opposite direction.<br>And somehow, the smaller model absolutely rips. V4-Flash-0731 has 284 billion total parameters, with only 13 billion active at a time. Kimi K3 activates 104 billion and Qwen3.8-Max activates 95 billion. Despite that enormous difference, DeepSeek scored a 50 on Artificial Analysis’s Intelligence Index, compared with Kimi K3’s 57. DeepSeek is activating about one-eighth as many parameters as Kimi while getting surprisingly close to it in overall intelligence.<br>It also scored six points higher than DeepSeek’s own V4-Pro Preview. V4-Pro has 1.6 trillion total parameters and activates 49 billion, while V4-Flash-0731 has 284 billion total parameters and activates just 13 billion.<br>That makes Flash roughly 82% smaller overall, with 73% fewer parameters active at a time. DeepSeek didn’t just get closer to the frontier with a smaller model. It somehow made its smaller model better than its larger one.<br>The memory footprint makes this even crazier. Kimi K3’s released weights take up around 1.56 terabytes, while Qwen3.8-Max would require roughly 1.2 terabytes at 4-bit precision. DeepSeek V4-Flash-0731’s official checkpoint is around 167GB. That makes DeepSeek’s weight footprint roughly nine times smaller than Kimi’s and seven times smaller than Qwen’s.<br>To be clear, the 13 billion active parameters only tell us how much of the model is working on each token. The full model still needs to sit somewhere in memory (although you can offload some or even all of it to solid-state storage if you’re cool with waiting longer). But 167GB is something you can fit inside a very large workstation. Kimi and Qwen are firmly in “please show me your data center” territory.<br>Now I don’t want to stop here because, quite frankly, this is where everyone stops. They think it’s super cute that a smaller model is pumping at the Opus 4.8 levels before going back and fornicating over K3 and Qwen 3.8 Max.<br>And to be clear — I’m not making the argument that those releases aren’t groundbreaking in their own right. I mean, after all, they are basically competing with the frontier from Anthropic and OpenAI within weeks of their corresponding frontier releases.<br>But this flash model is probably more groundbreaking in ways that people don’t actually realize. I’ll lay out my case for this. But to jump the gun a bit — I actually think this release changes the way models are going to be trained and deployed. And uber foreshadowing — I think this could be a real thorn in the butt for OpenAI and Anthropic.<br>Ok so I made bold claims —> so let me try and and make the case through two main perspectives<br>Perspective 1: Bigger Models = Better Data Generators
There’s a very good reason these companies keep building enormous models. More parameters give a model more room to absorb information from an absolutely disgusting amount of training data.<br>A model takes trillions of messy, contradictory and often terribly written tokens and compresses the patterns inside them into its weights. It learns which ideas are related, which words tend to appear together and how concepts fit inside a shared mathematical space. The result is not a searchable copy of the internet, although larger models...