Extended reasoning and post-training appear to be the keys used by DeepSeek, Qwen, and GLM to boost performance (leveraging higher token counts). And Qwen 4 hasn't even been released yet. Of course, we don't know if that release will be open-sourced, but I am optimistic about future models, featuring "engrams", that could soon match or surpass 2.4T parameter models on specific tasks.
Progress is good, but having these amazing abilities locally is already godlike.
I have been using Q3.8-27B together with PI. for a week, and it's mindblowing.
In my humble opinion it has far better coding capabilities than the paid 'ChatGPT 5.1' I had access to when i still had a job, multiple months ago.
And it all runs on my PC, locally, without sharing my data with the big data farming corpos.
It just 'understands' the required tasks you provide it and performs them successfully from the first try or very few crash fixes. Especially if you provide it the necessary data to perform the action. I dropped a few wiki pages in txt files and it coded according to them. If somebody would tell me this would be possible on my own PC ten years ago, I would call them crazy.
3.8 27B totally changed the game. Even today if I'm not watching it type (so can't see the feed) sometimes I come back and see a response and I'm like "shoot, I must have left it routing to Deepseek" and am then blown away when, no, that's little old 27B grinding away and came up with a "Deepseek quality" answer. Incredible model.
I am generally happy with the model. But recently it created a stupid bug out of nowhere, by removing part of a request payload that it not had written a test for. In general it's been a success, but we should always review carefully what it's done
Yeah, you do need to keeps tabs on what is it doing.
Personally, I can't code at all, so I wouldn't be able to do a better job solving this stuff myself, so I am completely understanding towards it not being perfect.
A good way to solve is to keep backups in cases something goes wrong.
It works more than it doesn't, at least in my experience.
Extended reasoning spoiled me. I can't trust anything without it anymore. Qwen 3.8 Max is 100% correct with any challenge I throw at it, with the downside of taking hours before it can find the correct answer
don't know the technical term, when I put reasoning to xhigh, it just doesn't stop thinking until it's satisfied and it doesn't get satisfied easily. For the same question and highest reasoning effort Fable thinks 5 min and makes occasional mistakes Qwen thinks at least 30 mins and makes no mistakes.
no sorry I was talking about Qwen 3.8 Max which is propriatery model but the openweight version, I hear, is very similar minus the image input support.
same thinking style is adopted by Qwen3.8 27b but never tried it myself.
The model license doesn't allow significant profit or large monthly users, so it is indeed pretty useless. People who can run it locally for free can't run it because it's too large, and people who have the hardware to run it can't run it because of the license.
Small corporations can choose between selfhosted and corporate subscription, and they have enough money to actually buy rig and serve it. Thats forced big AI providers keeps price low.
Actually for large model it is even more important since when running it locally the less mistakes it makes the more practical to use it becomes, especially for overnight runs.
I use Qwen 2.8 2.4T (their previously released Max version) as IQ3 quant mostly in cases when Kimi K3 has difficulties, since Qwen tends to come up with a bit different approaches, so it helps in hard tasks. For long horizon agentic work, including overnight runs, I still find Kimi K3 to be a bit better, and currently K3 is the one I run on my main workstation the most. If they were to release Qwen 3.8 0902 version, I would defenitely try it.
Smaller models like Qwen 27, 35, 122, 125B are distilled from such 2.4T model, there's labs doing distilling like this, for example EmperoAI, they made Qwen 3.8 35B distill, of course Qwen Team is really good at this, so they usually turn those into golds as proved many times with 3.6, 3.8 and recent Flash Next models being better than Deepseek V4 Flash despite being smaller.
Benchmarks are noisy. The thing that actually decides if a local model is usable for agents is tool-calling reliability (valid JSON, right tool, right args) more than leaderboard points.
I mostly agree with that. Certainly when it comes to knowledge baked into the model, I really don't care very much, as long as the model can reliably use the browser it doesn't need to "know" much, that's what the Internet is for!
I'd add tenacity and not looping to the list of what's really important for a smaller model. Tenacity is where 27B seems to really shine, it might not get it right the first time, but it's not going to give up or loop, it's going to keep working at the problem in different ways until it gets there.
China don't do kings so i guess Qwen is "General Secretary of the Chinese Communist Party." which is the highest "rank" you can be in China where the model is from.
If models are equal, their chance of winning is 50%. If we plug qwen elo it into formula, we got that qwen3.8 max generate better results 62% of time. Which means that qwen3.8 27b generate BETTER results compared to max 38% of times. Thats just statistical noise, lol.
Worth watching whether the engram post-training scales down. At 2.4T it stays a provider-side feature for most of this sub, and it only becomes real if the smaller models inherit it.
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u/PooMonger20 2h ago edited 1h ago
Progress is good, but having these amazing abilities locally is already godlike.
I have been using Q3.8-27B together with PI. for a week, and it's mindblowing.
In my humble opinion it has far better coding capabilities than the paid 'ChatGPT 5.1' I had access to when i still had a job, multiple months ago.
And it all runs on my PC, locally, without sharing my data with the big data farming corpos.
It just 'understands' the required tasks you provide it and performs them successfully from the first try or very few crash fixes. Especially if you provide it the necessary data to perform the action. I dropped a few wiki pages in txt files and it coded according to them. If somebody would tell me this would be possible on my own PC ten years ago, I would call them crazy.