alt Hacker NewsSimon Willison's post about this gives a good context on why exactly this is happening. While it doesn't mention this in the Artificial Analysis page, this is likely with Max reasoning, which has extremely long reasoning traces:
https://simonwillison.net/2026/Aug/16/qwen-38-27b/
It seems like the token usage is 2.3x GPT Luna Max and almost 2x Kimi K3!
I'm curious if they can make up for this with insanely high tokens-per-second especially when served from hosted providers, though, given how tiny it is (37B!)
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i feel like Simon omitted an important part of how Qwen's "reasoning" levels work. they are just one sentence additions/omissions to the system prompt
xhigh -> "Reasoning effort is set to xhigh. Please think carefully through the task, validate key assumptions, consider plausible alternatives, and prioritize correctness, consistency, and clarity in the final answer."
medium -> no mention of effort (sentence omitted)
low -> "Reasoning effort is set to low. Keep your thinking brief and focused, moving directly to the conclusion without unnecessary elaboration."
in my testing this doesn't seem to produce exactly deterministic thinking levels, because it's just a system prompt nudge. i had instances where medium thought longer than xhigh
I'm running 3.8 27B locally, and the results from the past few days have been excellent. I find raw speed is less of an issue when you can trust the model more to reach the right result.
> insanely high tokens-per-second especially when served from hosted providers, though, given how tiny it is (37B!)
It's a dense model so it will use all of its parameters per token. 37B active parameters isn't tiny at all, it's almost what Deepseek R1 had, and it's 2/3 of what Kimi k3 uses, so it's not going to be “insanely high” tps: it's going to be three times slower than Deepseek Flash (Prefil speed is going to be quite high though, but not token generation).
On the other hand, it used about the same tokens as GLM 5.2 and got 1 point lower score.
The fact that we have a GLM 5.2-class model that can run on two 3090's comfortably at Q8 is absolutely insane. It wasn't long ago that GLM 5.2 was considered amazing for open weight models.
Damn, beating Kimi k3 is crazy. It produces already a ton of tokens.
But I think the next step will be even more thinking on smaller models. Maybe fine-tuned and we get really crazy stuff
It’s still going to chew up context quickly. Surely, some of the added tokens are helping the model, but does it require as many as it generates? What happens on long, multi step tasks as it pushes old tokens out of context? I’m not sure we know the answers to those.
A ThinkingCap variant of Qwen 3.8 27b would be extremely interesting.
https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.6-27B
And then a Bonsai ternary on top of that model.
Qwen models are slower in tokens/s, compared to similarly sized gemma4 and others, and they use more tokens per task, in part thanks to that xhigh default.
On the other hand, there are some of us who are stuck with hardware that has plenty of compute, but limited (V)RAM. The new 27B is just perfect for that.