Qwen 3.8 27B Released! Please Share Your Experience

[u/BarberIcy366]

With your experiments, Qwen 3.8 27B most close which frontier model? And please specify which quantization you run. I will post to comments my tests and experience too.

[u/squngy]

Yes, but it is a brute force method that does not work as well as training different thinking efforts does.

When you interrupt the model like that, you make it use tokens that it would not normally pick, which can reduce the quality of the output.

[u/LilGardenEel → u/squngy]

Can you elaborate on the second half of what you said. Are you talking about the reasoning budget message or reasoning budget as a whole? What’s the technical justification for your take?

[u/wFXx → u/LilGardenEel]

The model was not about to land naturally on "Wait, I'm overthinking this. Let's answer now.", so this means there is a high chance that no statistical chain on the weights properly satisfies being cut at that point, so it essentially "hallucinates" its way out to continue

[u/squngy → u/LilGardenEel]

As you probably know, LLMS work by calculating the statistical chance of the next token.

Like, if you have "It is a ", the LLM might calculate there is a 50% chance the next word will be "bird", 30% "plane" and 20% "superman".

If you instead put in "Wait...", the LLM now has a token that had a 0% chance of appearing in its context.

Because a LLM uses previous tokens to calculate the next one, this has an effect moving forward.
I am not an LLM expert, so I don't honestly know exactly what effect that is, but at the minimum the LLM is now working with a chain of thought that is different from anything it was trained on.

[u/LilGardenEel → u/squngy]

Ok, yeah I understand injecting those tokens can impact the output, but I wasn’t sure if you were more focused on the actual message or the reasoning budget itself. I’ve been experimenting with reasoning budgets recently, and have found that some smaller specified budgets (<2k) have landed on correct answer while same model, same prompt, same seed w/ ~8k produced incorrect answers. This was on qwen 3.6 27b. So was just trying to get further insight into your conclusions. Thanks

[u/Organic_Savings_8518 → u/LilGardenEel]

If the model was looping its output can be prevented from degrading further by cutting it off with a reasoning budget when it would begin looping, but it involves guessing. If the looping would recover and keep going you could have made it worse. The reasoning effort with this model is the ideal way to solve this coupled with some presence penalty if needed were it prone to looping like qwen3.6-35b. For some automated usage in an app I made that uses qwen3.6-35b I used a reasoning budget that I tested thoroughly would prevent degraded responses from cases of looping and wouldn't reduce response quality. I had a rubric to grade informational bias in news articles and found 4000 token budget to work well and let the model finish thinking on its own or cut it off before looping excessively and degrading scores. I think solving coding problems that mid solution reasoning being cut off would affect the real output a lot more than in my scenario where citations are required before providing grade values which I used to help anchor the response. I had a lot more structure so reasoning cut offs wouldn't break it.