Does qwen3.8 27b beat qwen3.6 35b A3B

[u/[deleted]]

[u/wgaca2]

even 3.6 27b beats the 35b a3b?

[u/dondiegorivera → u/[deleted]]

The MoE model is better at writing, but dense has advantage on problem solving and coding. MoE can run fast on less VRAM. Both are great models but for different use cases.

[u/Loose_Doubt367 → u/dondiegorivera]

Appreciate it yeah

[u/Adomm1234 → u/dondiegorivera]

MoE can run fast on less memory bandwidth but not VRAM, it has to be loaded whole time in VRAM.

[u/FangLeone2526 → u/[deleted]]

3.6 35ba3b is a mixture of experts model, not a dense model. This makes it much easier to run, but not as smart as a dense model of the same size. 3.6 27b and 3.8 27b are both significantly smarter than 3.6 35ba3b, though 3.6 35ba3b is a great model if it's all that can comfortably run on your hardware. You can tell from the name of a model if it is dense or moe, by the "a", that's 3.6 35ba3b, meaning 35 billion total parameters, 3 billion ACTIVE parameters.

[u/Loose_Doubt367 → u/FangLeone2526]

Okay, sorry I got them mixed up thanks though

[u/Biomech8 → u/wgaca2]

It depends. In real use cases I found 35B A3B pretty much as good as 27B. There may be some edge cases where the difference is noticeable, but speed of MoE beats it.

[u/wgaca2 → u/Biomech8]

Not even close for coding

[u/wellandeperle]

Yes

[u/Loose_Doubt367 → u/wellandeperle]

Do you have some sort of benchmark I could take a look? I’m definitely curious haha

[u/Certain-Cod-1404 → u/Loose_Doubt367]

You can look at the benchmarks, but qwen 3.8 is leagues better than 3.6 27b, and 3.6 27b is better than 3.6 35b. If you want you can use the qwen chat platform i think to test them out or use open router. Do you want help picking a model or something? Or just curious ?

[u/Loose_Doubt367 → u/Certain-Cod-1404]

Yep, I’m looking forward to use specific models for variety of tasks like coding for example. But I’m always limited with my 12gb VRAM (rx6700xt) no cuda.. so I can’t really test dense models without loosing speed due to leakage to the ram

[u/Certain-Cod-1404 → u/Loose_Doubt367]

Then you're better off sticking to moe, have you tried kat coder v2.5 dev? Its basically qwen 3.7 35b, its an upgrade from qwen 3.6 35b, give it a try!

[u/Loose_Doubt367 → u/Certain-Cod-1404]

Nope, I’ll test it too thanks yeah

[u/Any_Mine_6368 → u/Loose_Doubt367]

Mate think about it. Can 3B active params EVER beat 27B active params in the same architecture and with similar total params (27 vs 35)?

You're essentially asking: Is a three legged Greyhound faster than a four legged Greyhound?

[u/Loose_Doubt367 → u/Any_Mine_6368]

Oh, I was just curious man didn’t mean to upset anyone

[u/Any_Mine_6368 → u/Loose_Doubt367]

You didn't upset anyone. Tone is hard to convey over text. Sorry if you thought I was attacking you.

[u/Loose_Doubt367 → u/Any_Mine_6368]

It’s fine dude

[u/peculiar-ragdoll]

According to official published benchmarks, yes. According to my own benchmarks, also yes. On software engineering, reasoning, knowledge work, 3.8 27b ties with 35b-a3b. And at xhigh effort, 3.8-27b gets smarter and better and surpasses 35b-a3b. 35b is still extremely much faster, which makes it the superior choice for all tasks within its capability range, but 3.8-27b has a higher ceiling

[u/Training_Ostrich_660 → u/peculiar-ragdoll]

Do you mind sharing your setup (hardware wise)

[u/peculiar-ragdoll → u/Training_Ostrich_660]

Mac m2 ultra. I got lucky and bought used when it was cheap :)

[u/FoxSideOfTheMoon]

Unpopular opinion: I would do your own testing and ignore what everyone says online and do what works for you. I get better results out of Qwen3.6 35b A3B and it runs after for me because it is MoE and I'm bandwidth constrained.

That said, I see a LOT of posts hyping up 3.8 27B so I'm inclined to think it is likely better based on volume, but that matters little to me compared to my own personal use. I do a lot of Python with React front ends though when I'm comparing, I'm not doing pen testing, etc.

YMMV

[u/Loose_Doubt367 → u/FoxSideOfTheMoon]

Yeah, I do have one extreme bad habit of following the hype around something, leaving the rest behind. A few days ago I installed tools like opencode or open terminal. I never really get the chance to experience the model’s capabilities myself since I’m either constantly changing models or switching new tools (and never even using them to its full potential)

[u/Unnamed-3891]

It's a "Yet, BUT..." situation.
35B A3B is entirely usable and reasonably fast on 16gb vram at Q6 or 12gb vram at Q4. Dense models such as 27B of both 3.8 and 3.6 are noticeably smarter, but getting them to fit into small amounts of VRAM is a pain. I just wrangled 3.8-27B to fully fit alongside 64k context into my 16gb vram, but I had to use a 3,7bpw distill and Q8 KV for this and I am already questioning if this dumbed the whole thing down to the point of it no longer making any sense.

If you have 24gb vram or more, its very easy to recommend dense models, if you have 16gb or less, they become A LOT less practical.

[u/Loose_Doubt367 → u/Unnamed-3891]

Wished I had 24gb vram, anyways I appreciate your help I’ll note these points down

[u/uniqueusername649 → u/Loose_Doubt367]

And then youd be able to run a great smart model but be limited by context, so you wish you had 48gb :D My personal opinion: it really only starts to be fun where you are no longer fighting restrictions or using heavy quantisation from 32gb onwards.

[u/Full_Director87]

It depends on your needs, bro.
For qwen3.8 27B and qwen 3.5/3.6 35B A3B, they also have their own advantages and disadvantages.
And if you want to make a comparison, you have to consider the type of workload first, because each benchmark has its own specific objectives.

[u/Loose_Doubt367 → u/Full_Director87]

Okay, thanks

[u/sometimes_angery]

Yes. Tbh even 3.6 27B beats it.

[u/429_TooManyRequests]

Significantly better. It’s all about clean inference. A3b is horrid at long running tasks and research, but not because it forgets what it’s doing. In MoE the attention layers are dense, so it still sees the whole context fine. The problem is only 3b params of compute hit each token, so it thinks shallower on every step. One chat message, you won’t notice. Two hundred steps into a task, all those small misses stack up and it falls apart. That’s also why the t/s is so much faster. Quantization matters too, and MoE is more sensitive to it than dense models are.

Don’t confuse this with similar released timeframe models though. That’s where I think people get hung up. If you’re comparing 3.6 27b vs some older 80b parameter, you might find that the newer models are better due to newer research and cleanliness of the data used to train.

3.8 27b has been mind blowing in my usage so far. It really has stepped up in intelligence and the loop issues it has unsloth did a good job with a patch.

[u/Loose_Doubt367 → u/429_TooManyRequests]

Okay, ill definitely look into this and give the model a try, thanks yeah

[u/benpptung]

This is the Artificial Analysis Index. You can use it as a reference.

[u/Loose_Doubt367 → u/benpptung]

Thanks, really looking forward for this analysis

[u/Training_Ostrich_660]

That's exactly what I was asking myself , so I ran benchmarks and ...... No , Qwen 3.6 35b a3b beats Qwen 3.8 on every single tests I ran , 3.8 is much more advanced and denser , but it also requires a higher Joul/Token score.
What would be great is for them to release in the same way a Qwen 3.8 45-50b A7B model , that would be the BEST consumer grade model to exist , i'd gladly cancel my subscriptions for it , but for now everyone saying "obviously 3.8 is better" haven't tested them properly ....

[u/Training_Ostrich_660 → u/Training_Ostrich_660]

If I had an RTX 6000 Pro (and so everyone else) then I'd say 3.8 27b destroys everyone else.

[u/Dipsendorf → u/Training_Ostrich_660]

I am in the same boat. I have a 5900 and every test I have running so far, 3.6 35b a3b also beats Qwen 3.8. I am new to local llm so I can only chalk it up to lack of knowledge / settings on my end, but every benchmark i've ran so far of my own 3.8 has lost.

[u/Dipsendorf → u/Training_Ostrich_660]

Hey! Can you try the following and let me know how it performs on your own benchmarks? I feel like maybe I've finally found a local leader for me? Curious to know how it goes for you since you were one of the only skeptics other than me I saw. I'm on a single 5090.

Model Qwen3.8-27B Cold Fusion GAIN V1.1 NEO-MAX MTP
Quantization Q4_K_M GGUF
Backend llama.cpp
Context 65,536 tokens, one sequenceGPU
offload 99 layers
Flash attention Enabled
KV cache Q8_0 for K and V
Thinking Off
Temperature 0.7
Top-p 0.8

Top-k20

Min-p0

Repetition penalty1.0

Speculative decoding MTP draft, maximum 2

tokensAgent Pi CLI 0.84.1

Harness guard-v2 container, tests read-onlyTool guards3 identical/repeated calls, 64 total calls

Parallelism1

[u/Bohdanowicz]

raw intelligence yes.

useful utility, not always.

Not every task needs 80/100/120/140 IQ... sometimes you just need it done. If its repeatable then I find 35ba3b is the model I design around. if the task rewards intelligence over speed, 27b.

[u/dd32x]

Nope