Not really. “Flash” generally means fast or cost-efficient, not specifically MoE (Mixture of Experts).

- DeepSeek V4 Flash: 284B / 13B active MoE

- DeepSeek V4 Pro: 1.6T / 49B active MoE

- DeepSeek V4.1 Flash: 552B / 8B input, 16B output MoE

Every frontier model is MoE these days! Flash or otherwise. Sparsity just gets you more bang for your buck.

Oh, really? I didn't know this. Sorry.

Then maybe they can be merged once one gains a comment?

Most probably the last version they had access to for benchmarking, or otherwise.

I think it clearly points to where they expect themselves to be, Opus 5.5 and Astra or Sol 6.1 are models that shouldn't exist at those prices without the price competition.

And no new releases from Chinese labs likely means their models that they could compare against those models are still baking largely because they lack compute so severely they need to train models for far longer, or figure out training on Chinese hardware.

Either way it's clear open models a lagging by ~1-2 months compare to frontier, depending on what frontier means to a person. Honestly most new models are just not that much better than at edge cases, they still fail a lot for the kind of stuff I do, but those edge cases matter a lot for un-interrupted long horizon work.

I personally am starting to see a wall here in llm development but that might just be me, given that people are being forced to scale models up considerably for frontier performance.

It's scary thinking that modern models will likely be at best 2-3% better in benchmarks or only useful in 1-2 unique edge cases compared to base models for 100x cost increase in compute required, it's getting absurd.

I can't imagine how Fable models can retain any value once you have to go 100x larger, while 27-32B models start to catch up to the current frontier for coding or other narrow/focused tasks in a year or so.

I know, it is never enough, more, more, give me more. But the fact is that no one has broken though yet to offer a frontier like open weight over 13 tps in consumer hardware. It would be usable at 13tps for me, especially considering the benefits and advantages of privacy, infinite tokens, availability and no refusals/restrictions. To this day Minimax and GLM are very strong coders but have the same issue, inference speed. We need a real break through, one that thinks out of the box (Tesla turbines style perhaps).

If all you're going to do is chat with an AI, you don't need a 400B parameter MoE model.

13 TPS is unusable for agentic coding, because if decode is that slow, then prefill is absolutely abysmal, and agentic work live and die by prefill speeds.

He is trying to be faster than his friends, not the bear.