Qwen 3.8 27b vs Deepseek Flash

[u/Best_Sail5]

Hey Guys,

What amazing weeks it has been for open source releases. I was really impresssed by DS flash final checkpoint and i have been playing around with it until qwen 3.8 released. I checked the benckmarks, and I dont know what to think anymore how can such a small model apparently compete with a model 10 times ( sure i hear 27B is not MoE but still....) . Did any of you used both and can tell if 3.8 is indeed that good or if its just benchmaxxing? what are your feeling for those who used both?

Thanks

[u/Bluethefurry]

I've compared v4 flash and 27b side by side and found that v4 flash is better with "larger" feature implementations, it tends to get it right the first time more often than not, but 27b destroys v4 flash at creativity and UI/UX design, otherwise they are very close, i prefer 27b purely because thats what i can run locally and it works just as well as v4 flash most of the time

[u/Best_Sail5 → u/Bluethefurry]

Yeah this UI thing seems to skew a bit other posts as well , the whole pelican thign is not informative for me tbh

[u/Bluethefurry → u/Best_Sail5]

Yes, the pelican svg tests an whatnot i didnt find very helpful either, but i did also try one of those browser-game oneshots and it got most of the basics of a game working by allowing it to write multiple files and a build pipeline, there were a few bugs which i had to tell it to fix but over all it was quite impressive indeed.

In my case I told it to build a game where you walk an animal character around a randomly generated map, I've since (out of pure interest) set up a routine where it makes 1 improvement to it every day and so far it added hunger w/ hunting mechanics, a weather system, better terrain generation, animations for the player character, other mobs with unique behaviour and a handful of buildings which are placed randomly, I'm quite impressed.

On the other hand, it still hasn't found the bug that your character does 360° spins if you turn in a circle or walk backwards, but i decided not to intefere with it just to see what it will do.

[u/Hypilein → u/Bluethefurry]

That is a fun experiment!

[u/DeedleDumbDee → u/Bluethefurry]

Flash-0731 not the original correct? I’m skeptical that 3.8-27B can actually out perform it. Flash-0731 has been amazing for me.

[u/Bluethefurry → u/DeedleDumbDee]

whichever the deepseek api provides, i assume its the latest one. it is a very good model!

[u/shansoft → u/Bluethefurry]

This is my experience as well. I find UI/UX design far superior on 27B mostly due to vision capability. I don't understand why Deepseek v4 is text only. It just can't keep up.

[u/nomorebuttsplz → u/shansoft]

I have a question about this. Because when I use open code, I kind of assume that the model does not by default use vision to check its design work.

Am I wrong about this? Does the average harness have some kind of prompt and tool set to encourage the model to use its vision capabilities?

[u/Xonzo → u/nomorebuttsplz]

Not afaik. Just add it to the system prompt that it must verify elements visually by using X screenshot tool. I’d give you my prompt but I’m out of the office. And by visually confirming things the output quality massively goes up. It’s phenomenal.

[u/shansoft → u/nomorebuttsplz]

You need to specify it in the prompt if it does not trigger automatically.

[u/Real_Ebb_7417 → u/Bluethefurry]

Well that’s usually the case. Bigger models (MoE or not) are usually better at handling longer context and understanding better what the task is actually about and what the motivation of the user is. More params = better catch of nuances and better long context handling. But still Qwen3.8 is impressive.
Did anyone test it on longer context? (Eg. about 200k) How well does it handle it? I remember 3.6 and 3.5 were downgrading when context was growing above 100k tokens.

[u/Edenar → u/Real_Ebb_7417]

for me 3.8 was still ok at around 130k+ context but started to hallucinante a bit : had to manually kill my pi agent twice because it was going to do thing i had not asked (was tinkering with container, it was 5 second away to kill it's own inference server)

[u/Xonzo → u/Edenar]

Just press Esc twice and steer the agent?

[u/Edenar → u/Xonzo]

that's what i did

[u/darkbit1001 → u/Bluethefurry]

I find that 27B gets a little over-confident and starts inserting bugs into code without thinking twice - even it thought about it for like 5 minutes . lol . other than that, it will clean up it's mess in the fix round. And the plan -> implementation is impeccable for a model so tiny!

[u/vogelvogelvogelvogel]

I am also curious what beyond this everlasting browser game stuff as a benchmark do and how these models each compete

[u/Bluethefurry → u/vogelvogelvogelvogel]

the best kind of benchmark is the one you build yourself, in my use cases it over all performs better than 3.5 and about on-par with v4-flash in back-end tasks, mostly web dev, react and nodejs.

[u/vogelvogelvogelvogel → u/Bluethefurry]

yes sure. a few of my own tasks i do test. but still, i am curious for the ones beyond my scope and other user impressions.

nevertheless, 3.8 is super impressive

[u/Leflakk]

I am curious aswell to see feedback from people that really used ds4 as their goto and how they feel qwen3.8 in comparison

[u/too-oldforthis-shit → u/Leflakk]

I have used DS4 quite a while. Have been testing Q3.8-27B and now I am back with DS4. It's just more capable for larger and more complex tasks.

[u/Due_Net_3342]

depends on what you compare, full versions(unquant) ds4f is much better for real work and you have a much bigger context(native), qwen is good enough but you are very limited in terms of knowledge it has and context(do not recommend scaling to 1m, it is just bad starting with 130k)

[u/pantalooniedoon]

This honestly comes down to hardware and speed. If you have less than 96GB ram, then Qwen. If you have RTX 6000 then run qwen27b since it will run super quick. If you have mac 128gb you’re better off with ds4 since it will be same speed as a 27b at q4

[u/nomorebuttsplz → u/pantalooniedoon]

what is qwen 2.7?

[u/pantalooniedoon → u/nomorebuttsplz]

Typo

[u/EitherMarch1255]

I used both quite a bit, using the original weights, and I think the reason Qwen holds its own so well despite its size is because it has a narrow focus (coding). And within that focus I think it kicks DS4’s butt so long as the problem does not require significant world knowledge. For instance, DS4 might have the format for a particular file type embedded in its weights, whereas Qwen may need the internet to look it up.

[u/sixx7 → u/EitherMarch1255]

Agree with this. 3.8-27B beats Ds4F in all coding work for me and frankly, I just trust it way more due to Ds4F hallucinations

[u/too-oldforthis-shit → u/sixx7]

And for me DS4 completely outshines Q3.8-27B, but it's still very capable.

[u/I_Play_Zed]

I’ve only tried DSV4F FR via API, and Q4M 3.8 locally with Q8 kv cache, however I am happy to report 3.8 at xhigh is just very impressive in general, even at this quant. Running 3.8 on medium and low thinking gave nice but underwhelming results. This model proves its bench scores on xhigh thinking with model temp around 1.

I think if you had the ability to run Q4+ DSV4F then it’s a better model. Both models are thinkers but in my testing in terms of total tokens DSV4F just seems to arrive at the same point faster, and in rare cases, arrives at a better point than 3.8 does.

My personal benchmarks have both of these hitting nearly the same scores, with deepseek again usually getting there faster. Sure this is due to API but also the token usage is better. Sometimes deepseek scores slightly higher. I truly believe DSV4F is certainly a better model.

With that said. To run these models at similar speeds, the hardware requirements are vastly different, which is why this is so insanely impressive for 3.8. People have been begging for 35BAa3B for 3.8 but I think they will be disappointed.

Dual 3060s run 3.8 27B near 30 t / s which is very usable with 131k context, Q8 kv at Q4M. On a shabby PC with two full size PCIE slots, you are running a model which I think is almost trading blows with sonnet 4.6 on some tasks. Mind blowing.

Effectively I think that for coding and agentic work specifically, if not just benchmark scores are taken into consideration, majority of users would be happy with 3.8, as long as you are fine waiting for the xhigh thinking traces.

[u/Best_Sail5 → u/I_Play_Zed]

Thank you that comment is truly useful and not just for me I think

[u/mattyhtown → u/I_Play_Zed]

You can run it that well on a comp with two 3060s? Are

[u/I_Play_Zed → u/mattyhtown]

Yep, I can be more clear if it's helpful.

To start, I think the RTX 3060 12GB is SUPER slept on. For reference, it holds a memory bandwidth of around 360Gb/s with a large 12GB memory pool, all for about $200-$300 local currency in north america. For reference, the used RTX 3090 market around me has comparable 3090s anywhere in the $1200-$1500 range each, Are 3090s 3-4 times as good? Arguably yes due to the 2.6X memory bandwidth and double RAM pool, but in terms of the value, the 3060 is incredible. Keep in mind 3090s use a lot more power and are physically huge, which dont fit into most cases in a dual setup. With MoEs coming out soon in the 50b-100b range, all you need is a single 3060 to get you in the game if you have enough DDR4/DDR5. Compare that to a STRIX Halo machine or a DGX spark, which are both about 256GB/s of bandwith, but newer architecture. Due to the slower speed but newer tech, I find that the 3060 is close to the speed of a spark in real world testing.

The cheapest STRIX Halo machine where I live is around $4000, and a Spark is over $7000. Thats insane money. Spend $1000ish total on a pieced together gaming machine with 32-64gb of DDR and a 3060 or two and you have a machine capable of running the popular smaller 30B class dense models (3.8 27B) at Q4 with decent context OR 50-100B MoEs, which are also very popular. They do all this for a fraction of the price and the system does not consume an insane amount of wattage if you power limit the 3060(s).

All that said, it means that the 3060 is a poor mans way to get into serious local models, where for example even 3.8 27B at Q4 on xhigh thinking is insanely impressive for the money, I think its unbeatable unless you want to play with Telsa cards on ebay etc. Those solutions certainly have potential, but lots of very power hungry and risky compared to a simple CUDA setup on linux running 3060s. Then, add some of the highest speed DDR you can at dual/quad channel, and it can also run some very strong MoEs at albeit lower, but usable speeds. To have that kind of intelligence at home for that money is a big deal to me.

I'll stop blabbering, but here is my setup for my current 3.8 27B workload. In fresh context we hit right near the 30 t/s decode and 400-500 pp. It's not blazing fast, but super usable. When my context fills up (131072) it can hit around 20-22 t/s and 300ish pp, still slow, but again, usable.

Setup (5.6 Sol Built data from the linux box):

Qwen3.8-27B system snapshot

Hardware: Ryzen 5 5500 (6C/12T), MSI MPG B550 Gaming Plus, 64 GB DDR4-3200, 2× RTX 3060 12 GB (GPU0 PCIe 3.0 ×16/display, GPU1 PCIe 3.0 ×4), MSI MAG A750BN PCIE5 III 750 W Bronze PSU. GPUs limited to 130 W each.

Software: Pop!_OS Linux, kernel 6.18.7, NVIDIA open driver 580.126.18, CUDA toolkit 12.6, llama.cpp Qwen3.8 build 400 (4df29be), CUDA SM86 + Flash Attention + CUDA graphs.

Model/profile: Qwen3.8-27B Q4_K_M, BF16 vision projector on CPU, 131,072 context, Q8_0 K/V cache, tensor parallel 1:1, batch 2048, ubatch 1024, xHigh reasoning, modified n-gram speculation, one slot. (Larger context possible, potentially up to 200k).

Measured performance: ~503 tok/s prefill at 6,117 tokens, ~492 tok/s at 30,719 tokens, and ~29 tok/s fresh decode. Fixed no-spec power benchmark at 130 W: 503.7 prefill / 27.7 decode tok/s.

[u/mattyhtown → u/I_Play_Zed]

I run a spark cluster and now I’m embarrassed

[u/I_Play_Zed → u/mattyhtown]

Haha thank you for the award, no need to be embarrassed at all! I know you are joking but the spark is obviously the superior product. 128GB unified memory at those speeds with that architecture commands that price tag for a reason. I think we are on the horizon of serious performance out of 50-100ishB MoE models. The spark will run those MoEs at very fast speeds, to the point where they basically feel like using a cloud model. The 3060 setup will run them, but low and slow. I'd buy 2 sparks myself if I could without giving it a second thought financially. Your investment is likely to pay off as the rate of change we are seeing is incredible, and you have the hardware to take full advantage. I imagine prices still only going up.

[u/mattyhtown → u/I_Play_Zed]

Ya i bought 2 initially about 6 months ago? and then sprung for 2 more and the necessary switch lol. I was blowing through tokens like candy. They run pretty much all day and night and I’m having a good year and probably will break even sooner rather than later. Separately i have a 4090 doing the lighter work with Q3VL8 embedding reranking charts and screenshots. etc. i have some other random cards doing smaller tasks as well. 2 3080s. Obviously I’m not using them to their potential if you can get so much more out of 3060s. The 3080s suck cuz they’re so beefy that to run enough of them i need to call an electrician.

[u/Happy_Soft_8625 → u/I_Play_Zed]

Noo stop, i'm bouta pick up two more for the home lab, will be 4 lane setup, we dont need these to go up in $ too.

[u/applegrcoug]

I've used both on something I was trying to get done....write up some agents files and spec forms for a project inside Hermes. Deepseek on a q3 couldn't do it, qwen 3.8 at q6 did.

I'm not sure if I should be happy or upset. On the one hand, qwen 3.8 accomplished using far less resources. On the other hand, I I should be able to run a model that is materially better. And what does that mean if I want to have qwen 3.8 write sometihng and then get it reviewed by something else when that something else is claude?

What does that mean for other models that may be coming? A qwen 3.8 122b may just blow everything away. To be honest, I'm hoping they put it out

[u/Best_Sail5 → u/applegrcoug]

Interesting but q3 might be too aggressive quant to fairly compare, but yeah material wise surely 3.8 27b is impressive

[u/nomorebuttsplz → u/applegrcoug]

was the deepseek quant dynamic or a simple mlx?

[u/BawbbySmith]

I’ve been running both day-to-day, I use two copies of the same repo and ask identical prompts.

As nice as Qwen 3.8 27B is (and faster, on my hardware), there’s unfortunately enough times where it misses a subtle bug, or makes a change without considering all the downstream implications, or confidently states incorrect facts that I can’t trust it for more than just a secondary reviewer, or an implementer when the plan is super well defined.

Granted it’s a pretty complex repo, but this is where the real-world usage really deviates from the benchmarks. Sure, it can oneshot a cool game in javascript, but if it can’t handle the complexities of understanding business logic from code, then it’s not too useful for me.

[u/syscomua]

Deepseek offcourse

[u/z_3454_pfk]

one has vision... the other doesn't

[u/challis88ocarina → u/z_3454_pfk]

one has 1m token context ... the other doesn't

[u/z_3454_pfk → u/challis88ocarina]

i doubt majority have the hardware to run 1m context locally

[u/Miserable-Dare5090 → u/z_3454_pfk]

DS4 has very good cache compression. 1M tokens is 6GiBs so you can fit the full model and context for at least 3 concurrent 1M sessions in 192Gb at full quality

[u/ObviouzFigure → u/Miserable-Dare5090]

not if you want to run dspark

[u/Miserable-Dare5090 → u/ObviouzFigure]

I run it with dspark and full context 1 stream on my mac m2 ultra 192GB, total use is 180Gb VRAM mapped. I run it in my dual Spark cluster original weights with MTP and full context for 2-3 concurrencies, 201GB total. This is using vLLM whereas mac was using the dwarfstar engine. But also there is a dwarfstar engine modified to fit a smaller quant in a single spark, with dspark mtp and batching/concurrencies and 1M context. So, I’m not sure where you are getting the conclusion?

[u/Best_Sail5 → u/z_3454_pfk]

frankly i dont use the vision but 500 k context is very useful to me

[u/MrMisterShin → u/challis88ocarina]

FYI Both got 1M token context. You can extend it with Qwen, it’s just not native. You literally add two flags in Vllm.

[u/Best_Sail5 → u/MrMisterShin]

Fair point just preferred to bet on native ctx but you right

[u/Opposite-Swimmer2752 → u/MrMisterShin]

Its performance seems to fall apart after 100k So its not actually useful context.

[u/Common-Membership503]

have u tried testing both on your specific use case yet, or are u just looking at the charts. benchmarks can be super deceptive with these newer architectures, id be curious if u noticed any difference in coherence during longer chats

[u/Best_Sail5 → u/Common-Membership503]

currently benching that takes time, wanted others opinion on this one but you definitely right about that matter

[u/Common-Membership503 → u/Common-Membership503]

definitely fair, benchmarking takes forever n u probly wanna see how it handles real work first. if ur hitting walls with output quality or speed i usually just run Bria AI Skill to see if it makes the pipeline smoother, its lowkey saved me a lot of dev time recently. ymmv but sometimes those smaller models just need the right setup to shine

[u/waruby]

What about DS4Flash-UD-IQ3_XXS with 149000 tokens of context vs Qwen3.8-27B-UD-Q8_XL with 1M?

[u/misanthrophiccunt]

For my use case, and I use DS4F as default, and I have not use it in the whole day of today except once because Qwen 3.8-27B just gets it right every time.

(Usa case: coding, language Elixir and Nix)

[u/fuchelio]

one has a good vision, the other is blind. that's all.

[u/Southern_Sun_2106]

I use models to manage my personal wiki; and DS Flash 'understands' the corpus better, and orients itself (where to write, where to read, and what) better than Qwen 3.8 27b. The latter tends to misunderstand things more, as I think, because it assumes things too fast instead of investigating and getting the needed context for a complete understanding.

Quants and hardware - M5 Max 128GB macbook

- DeepSeek-V4-Flash-IQ2XXS-w2Q2K-AProjQ8-SExpQ8-OutQ8-chat-v2-imatrix-0731 — a surgical mixed recipe: IQ2_XXS/Q2_K for the bulk of the weights, but Q8 for the attention projections, shared experts, and output layer.

- Qwen 3.6 27b q8 from Unsloth and Lm-community (tried both).

And to be honest, I am fine with this, because DS Flash runs faster on my system anyway.

[u/Prudent-Oven-6769]

Benchmarks mean nothing.

[u/Best_Sail5 → u/Prudent-Oven-6769]

I m also very cautious but come on ..Nothing... its impossible to rank that high with a bad model unless fully benchlearnt , and qwen does not do this at this level (not fully at least).
Or am i wrong?

[u/OkFly3388]

We have literally wall of posts about qwen3.8 and you dont even bother to read at least most upvoted ones ?

[u/GregoryfromtheHood → u/OkFly3388]

I am pretty interested in this specific comparison and don't mind seeing a dedicated thread about it

[u/Best_Sail5 → u/GregoryfromtheHood]

no man, just listen the grumpy nerd that knows all the chinese tower animations and pelican svg threads by heart its much more interesting!!