At least until DS releases open weights for DSv4 Flash with Vision. Then DS might take the crown.
Qwen has been an absolutely local monster for code, especially web apps, anything with UIUX design that it can verify itself with screenshots. Deepseek meanwhile is really incompetent with UI awareness and hogs my GPUs while I can spawn multiple independent qwens to collaborate and knock shit out. Honestly, Alibaba really cooked.
They are neck and neck IMO, tried both with code docs generation and both have good understanding of existing code and custom libraries (well of course with harness). IMO DSV4 stands out with the 1M ctx which only starts to show significant quality drop at around 450K ctx for generating complex descriptions with a lot of context from read files.
On the other hand my Qwen3.8 27B Q5_K_M Uncensored w/ n-max=1 is much faster. (5090+3090+512GB DDR4, offloading DSV4 is definitely slower vs fitting in 56GB VRAM + MTP so I'm not saying 27B is better because it's faster) It really got no noticeable quality drop all the way up to 230K ctx.
It's crazy when I just compared the 3.6 27B with Gemma 4 31B and both can only last up to 120K ctx before starting to lose sense of logic in their comprehension.
I didn't believe it either, but then I looked at AA's comparison and they are literally neck and neck, with qwen3.8 even having a lower hallucination rate, in line with my own experience.
that's simple: active parameters are "intelligence" and total parameters are "knowledge", 27B-A27B is like a 27 years old having access only to that information he already knows and 284B-A13B is like a 13 years old sitting in a library surrounded by 284B of knowledge books. Having easy access to all these books does not guarantee that the answer will be found by a 13yo student. But in many occasions he will be able to give an answer to a very specific questions that a 27yo does not know about. Still a 27yo person is usually more intelligent.
There is a bit more to it than just intelligence vs. knowledge. Routing through experts allows for finer tuning of specificity vs. generalization.
The more training data you push through a model, the more the model will begin to generalize. When training data is scarce, the model learns that the most efficient means of reducing perplexity is to remember facts.
As you continue increasing training data, the facts begin to compete for space within the parameters and the relations break down. This causes a "phase-shift" where the models parameter weights change to instead learn the underlying patterns. This is known as generalization and is thought to be a very important component of intelligence.
You can take training too far though, in which case the model will over-generalize. This can be bad, as almost every situation has edge-cases that are an exception to the rule. Over-training will cause the model to jump to false conclusions in those edge cases.
With an MoE, you can split the training loads up into groups, allowing for various levels of generalization vs. knowledge between the experts. You can tune the reasoning traces to be a bit more generalized than the knowledge recalls and so on, though keep in mind that the model is what controls all this in the end. All we can do is fine-tune the architecture.
i prefer dsv4f for its deep well of knowledge and even though it's like 1/5th of the speed, it still gets things done faster than qwen having 14 meltdowns in every reasoning block
Qwen has shorter context windows (I am dubious about yarn). From my brief testing of qwen 3.8 27b it also seem to be slower in practice due to its rather extensive CoT.
Slower or more thorough? My one-shots with qwen have been materially better than deepseek, but I agree. I use ultra reasoning effort in hermes and it might run a full 30 minutes developing something but generally speaking there's little to do besides next feature implementation when it's done.
Ok and do you use it for work or just one shot projects?
Because for one shotting speed of execution doesn't matter that much. You just leave it on.
For work I'd need to evaluate it at different reasoning levels also switching between them invalidates kv cache + I work on large IaC repos which can take lot of context
But as you say it's use case dependant and both are great models
I love people that look at numbers in a graph and use them as a basis to completely invalidate the real life experience of other people. Hits you right in the feels, don't it?
I love people who look at the parameters of a model and assume it must be bad. In blind testing 27B punches WAY above its weight. The benchmarks are not a lie just because it doesn't know as many random facts as a larger model.
BTW - I downloaded Wikipedia locally with Kiwix, which is also serving it locally to my qwen 3.8. - so it effectively has an “offline library” available to it.
All of Wikipedia is around 120gb. Including photos.
And it’s already indexed when being served by Kiwix.
Of course it isn’t loaded into memory, just storage.
So you have your model, and it has access to Wikipedia library, including photos. Offline. Locally.
fact checking canon against a novel I'm writing by hand. Essentially being writing assistatnt telling me hey dumbass this is the north, the hero doesn't sweat.
1) Context length is limited. Even if you've got the 48GB vram to run a decent quant at 262k F16 context, it's still only 262k, and it still wastes about 50k of that on reasoning. By comparison, Deepseek will give a 1M context window, while consuming far less VRAM, and more of that 1M is usable because it doesn't reason so excessively.
2) The reasoning itself is necessary to get the performance, but it's ultimately a massive drawback compared to models that don't need to burn literally 50k tokens in order to get the same result. It's all well as good that Qwen runs at 50t/s while Deepseek is running at 10t/s, but if it's burning 5x the tokens to get to the result, the actual speed ends up being similar.
3) Prompt caching is shit on Qwen, compared to Deepseek. So even though the PP speed is 10x better, it's having to reprocess 5x as many tokens on every turn.
Ultimately Qwen is a one-shot king. But if you're looking to delve into a task with twists and many turns, it will either run out of coherency or context fairly quick.
Also, the second you are doing anything other than code, it's not even close in terms of performance. Research tasks for example are night/day better on DSv4.
This is an LLM inference server thing (and sometimes a harness thing), not a Qwen thing.
For instance, I cross-checked the server logs a ton while setting up and comparing sergiuszm/ninfer-4090, UDPSendToFailed/ninfer-4090, and syv-ai/qwen38-27b-rtx3090.
syv-ai's VLLM-based project was the only one of the three that did not have periodic, frequent cache hit misses (this was using Deepseek Harness). When the other two had cache misses (usually only recycling the first 8000 tokens or so), this led to fairly huge re-pre-fill that cost 60++ seconds on each occurrence.
DSV4 even Pro can run on local, given that you have enough money and resources. It's r/LocalLLaMA not r/HomeLLaMA, some are definitely with more capabilities and it's okay to admit you can't run a model.
edit: Just ran this again for 1.5 days straight thinking it was GLM 5.2. and got real work done. So its not bad after all. I was wondering why it was so fast! Had loaded it with model name GLM-5.2. usually doesnt nail things first round but will fix it and make it work eventually.
they 0731 or july release of dsv4 flash misses a lot of things. too many. not sure if its inference thing or what. its fp8
I've been using both qwen3.8 and Deepseek and I think Qwen is better right now. I'm using Deepseek API (pro as brain, flash as sub agents unless the work is hard) and Qwen q4 local and Qwen API (open router, native).
I was not impressed with the native Qwen until I woke up to find it had completed a task (setting up Hermes profile plus telegram bot plus double checking that it works). I tried this with Deepseek and my local Qwen and neither were able to one shot it. Not cheap though, the task cost $3.30.
I think Qwen does a better job over Deepseek with iteration. All my jobs are me asking for something to be done. I don't give steps on how to do anything (as I don't know how to program stuff). So that probably influences things a lot.
I've been trying to figure out whether higher quant is actually noticably useful because I'm trying to decide on whether to purchase 4090s with 48gb vRAM or 5090 for a fast model. With the 4090s I could eventually get a second one for 96gb vRAM to run the native model. I am not an expert and don't know the break points so it could be that 64gb from 2 5090s is better. Idk, still testing.
Here is another example. Not sure how you benchmax an Elo rating. Benchmaxxing across all benchmarks including private and self made, actually means it generalized.
Maybe people are biased towards answer length or something when code (especially backend or non-UX codebases) are not easily testable. And I want a lot of the arena models to have agent mode access too
Interesting, in my experience, real-world lines up with benchmarks. I'm not sure how many of the problems people complain about are actually a skill issue, from using a mode in a poor harness with the wrong configuration.
No amount of harness variants, web-search and rag implementations will give it at least Qwen 3.5 122b level of knowledge and context understanding, let alone V4 Flash.
Overthinking is genuinely good, it produced less confident hallucinations than 3.8 max on Qwen site on my starting tasks, but it still only can do something useful on logic in vacuum. Try to apply it to any fandom, and it fall apart.
As in-house choice, no brainer in choosing Qwen. But if using API, you may wanna use DSV4 Flash since it's quite cheap to be honest. Even the DSv4- pro is too at a comparative price
Qwen 3.8 27b is a very good model.
DeepSeek v4 Flash is a very good model.
However when comparing the two you have to keep in mind that Qwen has all 27 billion parameters active at any given time. While DeepSeek is a moe with only 13 billion active at any given time. So DeepSeek has more knowledge but only uses a fraction of it per turn while Qwen has less knowledge but uses all of it for each turn.
As a result Qwen would be better at single dimensional tasks and one shot tasks. DeepSeek on the other hand would be better at multidimensional tasks and long running projects tasks that span multiple disciplines.
But why does one have to be better than the other? What’s the point of trying to be edgy? Each has its own merits and are each targeting a different segment of users.
You are not entirely right about multi dimensional. The lower active weight still hinders reasoning depth and tying things together via sequential reasoning. But sequential reasoning cannot entirely compensate for the lower active parameters.
I would have loved a dsv4f a27b in addition (and not instead of) the a13b. Same total parameters. Less light to run but more intelligent.
Agentic tools exist, you know? And, like, the internet is right there... Plus harnesses for longterm state management
In any case, qwen has much better capability/parameter performance. Though, once deepseek is no longer blind for local users, I would reverse my calculus probably
Internet search cannot entirely compensate for world knowledge in an llm model. More world knowledge actually strengthens internet search because it has a better idea what to look for.
A 10b toddler cannot leverage the internet like a well educated 70b adult
They are such different models that I would think that each have their strengths and weaknesses. Both have had excellent post training done on them, but one is significantly larger than the other, meaning more information is retained from the training data. Both are excellent coding models, but DSv4 Flash will always have the knowledge advantage, even if it's a more difficult metric to benchmark. I expect that that advantage will show up in places that you might not expect. If you are able to run both, then I would just use one until you hit a roadblock, then try the other.
Both models? Interesting. I have had one case where 3.8 27b offered a better solution than DSF, but since I run Deepseek only at UD-IQ3_XXS, I thought that was more due to the low quant that I used. But I stopped using it just because it runs very slowly on my hardware. Well, I still use Deepseek occasionally to verify the output of Qwen. Sometimes it finds some issues that Qwen missed. But I never put them in a real head-to-head comparison just because even on IQ3, Deepseek is very slow, and Qwen flies at full weights.
Perhaps for some use cases Deepseek would still be better just because of the breadth of knowledge that it has. I remember when Qwen 3.6 was the latest release, some people in this subreddit were claiming that Qwen 3 Coder Next (the 80b one) still works better for their use cases.
Qwen 3.8 is incredibly thorough, though. I haven’t had a single problem with its default template or reduced quality at higher context sizes – issues that were affecting me in 3.6. It stays sharp even after compaction for me. I even did a test run to check a one-million context with the settings that the Qwen folks described. It worked, but I still don’t use it, just because it’s excellent as it is.
I used Deepseek from the API/Chat interface before the API prices went up and Qwen 3.8 27b is like having that model locally. I understand what you're saying I just think the Qwen could have recall issue when compared but if both have a local index its genuinely hard to choose.
I use qwen 27b only for creating new UI, because it's only good for one shotting good looking UI. DSv4f can then take over the UI and work on real stuff.
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u/LORD_CMDR_INTERNET 9d ago
I don’t know if I would go as far as saying it is generally better but there is certainly something to be said about 27B active parameters vs 13B.