When Astra launched, I think Artifical Analysis showed that it was on par with GPT-5.6 Sol and lower than Opus or something like that? Then, they updated the scoring.

I hope that more open source models, including this model, to be "as good to use" as Astra.

Too bad that DeepSeek AI went beyond 470b weights (which is a somewhat realistic limit for a 2x 128GB unified memory machine cluster like Strix Halo or Nvidia Spark).

That means that to make the model fit into memory there you need a quantisation of lower than 4bits per weight (which is usually bad) to fit it into the available memory.

> Causal Encoder-Decoder (CED) architecture: a 40-layer Transformer organized as a 20-layer causal encoder followed by a 20-layer decoder. With CED, the decoder's global KV cache is projected from the final encoder hidden states rather than derived from each decoder layer's own hidden states. This allows the model to activate only 8B parameters per token during prefill and 16B during decode, substantially improving cost efficiency for input-heavy agentic workloads.

> these designs reduce the global KV cache footprint to 890 bytes per token — roughly 1/4 of DeepSeek-V4-Flash.

Faster prefill, lower kv cache (~1GB / 1m context is insane).

> The model supports a continuously controllable reasoning effort setting (integer 1–100) that trades inference cost for accuracy.

Benchmarks are benchmarks, to be seen if they translate to real-world use, but they seem to have focused a lot on post-training with "agentic" scores looking good. "world knowledge" is obviously lower than higher param models.

I was hoping for a bit more, but it's still 100% faster for a very good price, so I won't complain.

[1] https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/...

In terms of coding and command line capabilities I'm also very interested to see a head-to-head of it vs. qwen 3.8-flash-next Q8 which is something like 190GB of memory used when loaded into llama-server. It fits very well in all sorts of 256GB or under class machines.

DeepSeek launching v4.1 flash cheaper and more capable than v4 pro - https://news.ycombinator.com/item?id=49624603 - Sept 2026 (216 comments)

Oh interesting, I can assume what the benefits is for including the Encoder, but whats the downside? I’m thinking GPT (which is decoder only) ruled out Encoder for a reason?