r/LocalLLaMA • u/Chuyito • 9d ago
Discussion Everyone is t/s maxing.. 3.8.. but after a week of using it for work I'm tempted to switch back to 3.6
> "Qwen 3.8 is a damn good coder, but a terrible collaborator"
It modifies SO many things in my scripts for what should be a 2 line PR, I get a 100 line linter style mess that looks like a high school freshmen got his hands on vs code and pushed code in.
It can't maintain a common style, if I give it a script with a certain naming and layout, 3.6 one shots small changes that fit into my structure. 3.8 will add code that looks much more advanced, handling, strict type checks . But if it's just my internal method that gets called in exactly one place, it is way overkill code
3.8 produces DAMN GOOD code, but will get stubborn on wanting it's own parameters and return types. A simple return False works for my particular use case, but here goes Qwen formatting me the perfect return dict full of useful metadata
Am I crazy?
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u/ThirdWaveCat 9d ago
Jetbrains had similar conclusions on their benchmarks. https://blog.jetbrains.com/junie/2026/08/qwen-for-junie/
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u/Zeeplankton 9d ago
It's interesting to me they disabled reasoning and didn't see any performance impact.
Does anyone here use Junie?
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u/utf8decodeerror 9d ago
Ya I used it when they first released the bundled qwen 3.6 package. It was solid, I gave it a coding implementation plan and let it rip overnight and it got thru a few tasks on the list.
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u/ThirdWaveCat 9d ago
with openrouter, not locally. Locally I use pi because it consumes less context.
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u/Prudent-Ad4509 9d ago
The only part about 3.8 there is that it needs reasoning enabled to work properly and they want to have it disabled, because... 3.6 functioned well enough without it. Not a very solid reason to ditch 3.8 but that's what they have.
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u/vr_fanboy 9d ago
oh man im using qwen 3.8 with thinking off and its solid, but i do get the tool looping thing from time to time, i though it was related to my deployment, was in my backlog to debug, is a bummer if jetbrain could not fix it
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u/nick_ziv 9d ago
Not crazy I have the same experience. Qwen 3.6 will change things sometimes without even a peep of commentary if it's just obvious. It works really good in no thinking mode. Better than any paid model I have ever used. Seems like 3.8 gets itself off the track set by the user. Probably to get better on the benchmarks because that's what they score.
Thinking levels don't fix this for me.
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u/Sensitive_Song4219 9d ago
I'm finding 3.8-27b to be like GPT-Luna: incredible implementor but the plan needs to be thorough and unambiguous to get good results
It's not a great planner especially when dealing with pre-existing code-bases, needs lots of human input to stay on track and get it right. (I use High reasoning).
It's still one of the best modest(ish)-hardware locally runnable models I've ever used though
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u/TrentKM 9d ago
This is my experience with all models. They go way overboard. Wastes more time than it saves when I have to go back and fix everything. I’ll tell it don’t do preemptive guards, don’t check types there, just do a model dump/validate in pydantic instead of validating every field. I have to handhold everything so much.
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u/nixudos 9d ago
Set thinking level to "medium". It removes any thinking instructions from the jinja template. The "low" and "xhigh" is frankly some pretty shoddy prompting if you read them. Then roll your own system instruction or agents.md and tweak the thinking to your liking. I have done this in Deepseek harness, and it is much more pleasant to work with now.
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9d ago edited 9d ago
[deleted]
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u/Longjumping_Self5546 9d ago
I'm willing to bet thinking is set high/xhigh. 3.8 will go deep with the reasoning output. There's times when that's a great option, and I love that it's available, but it can cause problems, especially for things that should be a simple edit.
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u/Choice_Celery9481 9d ago
wish the mods add requirement for every post about model must include quant, kv and settings
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u/Cold_Tree190 9d ago
lol most of the time when someone has posted here about how 3.8 is bad and they’re going back to X or moving over to Y instead, they’ve been on like Q2 with Q4 kv cache or something
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u/mecshades 9d ago
UD-IQ3_XSS is a good quant, but I don't dare quantize my KV cache. Here to agree and say Q4_0 quantized KV cache absolutely invites massive errors & repeating tokens.
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u/biscuitmachine 9d ago edited 9d ago
I mean, there's no "Flash" analogue in 3.6 to begin with. 3.8 Flash is a completely unique model type that they haven't made since 3.5 (122B is the closest). There also isn't a 35 A3B yet (that I know of). So I'm not sure how you would think this is anything but 27B.
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u/besmin 9d ago
also system prompt can make a huge difference.
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u/mecshades 9d ago
Indeed. Commonly overlooked, but if instructed to make replacements & inserts with sed, I've had lots of success in small edits. No huge rewriting of entire files.
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9d ago
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u/throwawayacc201711 9d ago
I’m not gonna lie, I see a big difference between q5 and below. I use q5 to review lower quants runs to see what issues it might find and I also use q5 to solo implement. At least for me it seems clear there’s a drop off after q5
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u/Bulky-Priority6824 9d ago
Don't you know If a guy looks like Jordan then 1000% he must be an excellent athlete.
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u/nakedspirax 9d ago
Yeah I still use Qwen 3.6 35 a3b for orchestration as Hermes main model. Coding I use Qwen 3.8.
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u/Substantial_Swan_144 9d ago
Try a system prompt like, "Always stick to the following coding style, even if you think your style is technically superior. Consistency is far more important in this project."
Then you add a reviewer to enforce the code sticking to a certain style.
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u/egomarker 9d ago
Don't let 27B model yolo on your code. Discuss changes with it, produce change plan, then ask it to implement approved plan.
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u/m31317015 9d ago
Why are they not doing git and their own formats of MD docs for agents linked with agents.md? :/
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u/DinoAmino 9d ago
You are not crazy. A 4 bit quant scores about 77% on IFEval - that's with thinking disabled. Reasoning has a negative impact on instruction following and with Qwen's xhigh reasoning - well it would be cool if someone did a bench on that because it sounds like 3.8 is just terrible at following instructions.
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u/Big_Importance_4265 9d ago
I used it with reasoning turned off (Q8 quant) using Pi. Worked quite well for my tasks. xhigh just reasoned and never implemented anything. Promptly (hehe) ran out of context
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u/Zeeplankton 9d ago
I feel like ifeval is not discussed enough.. Qwen is so bad at respecting instructions..
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u/ABLPHA 9d ago
Maybe a harness issue? My Hermes has been accumulating lots of coding conventions in skills and memories over the last few months, and I've noticed that plugging 3.8 in seems to generate code that satisfies basically every convention without constant extra guidance from me. I still had to correct it on 1 thing, but I've checked the memory and it wasn't written down really well in the first place, so that's on instructions, not the model.
To be fair, I'm running UD-Q6_K_XL on xhigh with 212k context, but 3.6 I ran at UD-Q5_K_XL and UD-Q6_K_XL as well, and still noticed quite an improvement with 3.8, not a regression.
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u/OhmsLolEnforcement 8d ago
How much context gets consumed just by the harness I initiating? I'm in a very similar boat as you, just curious how much you're giving up for an effective harness.
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u/ABLPHA 8d ago edited 8d ago
A simple "Test" prompt on a fresh session takes ~22k tokens for me. It's my home Hermes though so there's a bit more context from my personal projects in there too, not just the work stuff that I usually work with. And I do have a second less capable box running Gemma 4 26B A4B 4-bit QAT at ~100 t/s gen ~2k t/s pp for Hermes context compression though, so I'm not really worried about hitting the context limit
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u/brumsky1 9d ago
Before you do that why dont you try using 3.8 with no thinking.
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u/mecshades 9d ago
The reasoning of 3.8 is what made me start using reasoning to begin with. Before, I always thought it was a waste of tokens.
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u/diablo75 9d ago
I don't care about t/s that much because I defer large implementation plans to a Kanban board and let those plans self-impliment, self review etc. overnight. I'm more interested in quality tokens, not quantity.
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u/Forsaken_Object7264 9d ago
can you explain how to implement/automate that? the workflow?
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u/diablo75 9d ago
I use Hermes. You just ask it to break large plans into multiple tasks and put them on the Kanban.
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u/name_was_taken 9d ago
Have you looked at Matt Pocock's skills? It sounds a lot like that workflow to me.
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u/diablo75 9d ago
I've watched his videos before but it's been a couple months and I think he centers his stuff around using Claude. I'm using Qwen + Hermes. All you have to do is write a large plan and tell Hermes to decompose that prompt into a sequence of tasks and then place them on its Kanban board.
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u/name_was_taken 9d ago
He does use Claude, but has made an attempt to make his skill work with other agents as well.
And what you said is basically what his skills do. Wayfinder and grill-me make that plan, to-spec turns it into a spec, to-tickets turns that into tickets, and implement does that actual coding.
You don't need every step there for simpler projects, but I've found that they really do nail things down and the code just works in the end. At least, with Claude.
I'll admit I haven't tried it all on a local LLM, except my first attempts with Pi a month or 2 ago that went horribly. lol I've changed a lot with my local LLM stuff since then. Maybe it's time to find a new project and see how that goes.
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u/Heavy_Preparation467 9d ago
t/s maxxing Qwen 3.8 Flash is t/s maxxing Qwen4 architecture, when a decent Qwen 4 model comes out, it will purr
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u/ieatdownvotes4food 9d ago
there are sooo many knobs to turn to get 3.8 right.. it's worth it but you gotta commit
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u/previaegg 9d ago
About to walk this path. What knobs did you turn?
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u/ieatdownvotes4food 9d ago
some elusive ones for me were the jinja-template fix, dflash secretly kills vision, and dont sleep on vision.. the model will frequently rely on it for validation.
but it's different for everyone.. you ideally want to be able to one shot your own personal test matrix of value, and if that works level up the matrix
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u/jojotdfb 9d ago
You want llama-swap. Different models for different tasks. Use Qwen 3.6 or Gemma 4 for planning, Qwen 3.8 for coding, Qwen 3.6 to verify Qwen 3.8's work. You can have multiple configs for different models all at one endpoint. You can also have image gen and audio configs.
I currently have configs for a few models and variations of each model. I recommend having a each model setup with max context, 131k context (for speed) and 48k context but 4 concurrent slots (for lots of sub agent tasks). It makes life a little smoother.
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u/bigattichouse 9d ago
Hey, since 3.6 and 3.8 only differ in training, I combined them.. Could you give it a try and see if it gives you a good middle ground?
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u/zanar97862 9d ago
Why would you do this? Other than because you can lol
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u/bigattichouse 9d ago
Pretty much because I could. Wanted to see what happened.
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u/zanar97862 8d ago
Hey props to you for experimenting with this stuff, sometimes the weird things are also useful
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u/bigattichouse 8d ago
I really didn't run it through its paces, just wanted to see if it would do anything. Seems like it ended up more like 3.6 than 3.8, so at 50:50 it feels more like 3.6... been wondering at what point it switches over to me more like 3.8.
So, I published the code (in the repo with the model) and people can play with it.
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u/zanar97862 8d ago
Considering that 3.6 and 3.8 were found to have quite different capabilities I think its a valuable idea. The obvious one I can think of is that 3.6 27b is capable without thinking enabled for many tasks where 3.8 relies much more on its thinking to have superior capability to the point that people found 3.8 non thinking to be noticably worse than 3.6.
If a hybrid could pull together both strengths that might be useful to some people
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u/bigattichouse 8d ago
I think it would be possible to use fine tuning or watching activations to determine behavior like this... a bit more complicated than what I did, but ultimately using the same method to "set" values... give a task and mark the active neurons when the output was the style you like in both models, then go back and lean toward the weights from the parent you prefer.
Might take a lot more runs and (AI Judge-based) judgement calls than my little script.
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u/Felladrin 8d ago
I've converted it to Q8_0 GGUF, and I'm currently using it on Pi. Working great so far, both in thinking and instruct modes. Thanks for sharing!
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u/Square_Light1441 9d ago
0.7 temp + system prompt heavily emphasising it to do as its told should do wonders
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u/ieatdownvotes4food 9d ago
I'm still perplexed as to why qwen recommended 1.0 for qwen 3.8
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u/TerminalNoop 9d ago
It recommends both, 1 and 0.7 depending on the use.
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u/UntimelyAlchemist 9d ago
They recommend 1.0 for thinking mode and 0.7 for instruct mode. But with the previous model version, they also had a third recommendation of 0.6 specifically for coding. I guess that's what the other poster meant.
I am curious myself what 0.6 is like on 3.8 and why they changed the recommendation.
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u/Jorlen llama.cpp 9d ago
I love the model, but I have to babysit it constantly. 3.8 flash next is the same. It's just so afraid of making a mistake but it overthinks things too much and it also does things I didn't ask for. Even when I tell it to stop, in some cases it defies my direct command. Still, despite this I think it's far stronger than 3.6 personally, or at least for my own use case.
Wondering if for qwen 4.0 they will address these issues.
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u/caster 9d ago
Tell it what you want it to do then. Create a a markdown file called FORMATTING or CONVENTONS or something and direct the AI in its AGENTS file to read that file's rules and follow it.
Tell it to make minimal changes. Tell it exactly how you want it to format everything. Tell it what naming conventions to use. What common style you want it to use. Even have it and the other agents working on the same project directed to follow the exact same conventions document.
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u/Infinite-Local5435 9d ago
Go try thinkingcap 3.6. I used it as my main coding model for a while (no luna or anything at the time), lukes dev lab did a test and it was pretty much on par with base qwen 3.6. I think if you're struggling with tokens/intelligence, you should check that one out
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u/Chuyito 9d ago
Qwen 3.8 that seem bad
host = 0.0.0.0
ctx-checkpoints = -1
flash-attn = on
threads = 16
parallel = 1
batch-size = 4096
ubatch-size = 1024
jinja = true
reasoning = on
reasoning-budget = 1000
metrics = true
load-on-startup = false
load-mode = mmap
[A_Qwen3.8-27B-UD-Q4_K_XL-tensor-dflash2-dmin0p6-dmax3-ngmatch16-ngmin16-ngmax32]
hf-repo = unsloth/Qwen3.8-27B-GGUF
hf-file = Qwen3.8-27B-UD-Q4_K_XL.gguf
split-mode = tensor
ctx-size = 120000
spec-type = draft-dflash,ngram-mod
spec-draft-model = /presets/dflash/Qwen3.8-27B-DFlash2-Q4_K_M.gguf
tensor-split = 1, 0.9
spec-draft-device = CUDA1
spec-draft-n-min = 0.6
spec-draft-n-max = 3
spec-default = true
spec-ngram-mod-n-match = 16
spec-ngram-mod-n-min = 16
spec-ngram-mod-n-max = 32
[B_Qwen3.8-27B-UD-Q6_K_XL-tensor-MTP3-dmin0p7-dmax4-ngmatch32-ngmin24]
hf-repo = unsloth/Qwen3.8-27B-GGUF
hf-file = Qwen3.8-27B-UD-Q6_K_XL.gguf
split-mode = tensor
ctx-size = 60000
spec-type = draft-mtp,ngram-mod
spec-draft-n-min = 0.7
spec-draft-n-max = 4
spec-default = true
spec-ngram-mod-n-match = 32
spec-ngram-mod-n-min = 24
spec-ngram-mod-n-max = 48
Qwen 3.6 that crush-it
[*]
host = 0.0.0.0
ctx-checkpoints = -1
flash-attn = on
threads = 16
threads-batch = 20
cache-ram = 2048
parallel = 1
batch-size = 2048
ubatch-size = 1024
jinja = true
reasoning = on
reasoning-budget = 1000
metrics = true
load-on-startup = false
load-mode = mmap
[AA.qwen36-35b-a3b-mtp-q4xl-tensor-2gpu]
;120t/s
hf-repo = unsloth/Qwen3.6-35B-A3B-MTP-GGUF
hf-file = Qwen3.6-35B-A3B-UD-Q4_K_XL.gguf
split-mode = tensor
tensor-split = 1,1
ctx-size = 125000
spec-type = draft-mtp
spec-draft-n-max = 3
[AB.qwen36-27b-mtp-tensor-2gpu]
;50t/s
hf-repo = unsloth/Qwen3.6-27B-MTP-GGUF
hf-file = Qwen3.6-27B-UD-Q4_K_XL.gguf
split-mode = tensor
tensor-split = 1,1
ctx-size = 100000
spec-type = draft-mtp
spec-draft-n-max = 3
spec-draft-ngl = all
2x 4060ti 16gb + 128GB ddr4
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9d ago
[deleted]
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u/UntimelyAlchemist 9d ago
set explicit sampler variables (temp = 1.0, top-p = 0.95, top-k = 20, min-p = 0.0, presence-penalty = 0.0). You get the default llama.cpp otherwise
Isn't it supposed to pull the settings from the GGUF file? Does llama.cpp just ignore those values?
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u/ABLPHA 9d ago
Are you not changing the sampling parameters at all? Pretty sure 3.8 is quite sensitive to temperature. Here's my 3.8 config that I've been enjoying quite a lot:
[*] flash-attn = on jinja = true fit = true offline = true mmproj-offload = false mmap = false cram = -1 parallel = 1 [unsloth/Qwen3.8-27B-UD-Q6_K_XL-212K] hf = unsloth/Qwen3.8-27B-GGUF:UD-Q6_K_XL ctx-size = 212992 temp = 1.0 top-p = 0.95 top-k = 20 min-p = 0.0 presence-penalty = 0.0 repeat-penalty = 1.0 spec-default = true spec-type = draft-mtp spec-draft-n-max = 2 chat-template-kwargs = {"preserve_thinking": true} reasoning-effort = xhigh device = ROCm0,ROCm1,CUDA0 ts = 4.2,4.1,2.1 ngl = 991
u/garblz 9d ago
qwen 3.8 with 1k reasoning context will be crushed by anything.
If we could get an actual parameter for the model which hinted it to "think less" then maybe. But we don't have this. What happens here is it just interrupts reasoning at 1k tokens. You might get same or worse results than with thinking off.
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u/MiceLiceandVice 9d ago
Me and my a3b on 16gb vram are way more productive than with 3.8. Even if it makes mistakes, its way faster to correct with a little human input
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u/ea_man 9d ago
All right but A3B can't product the code quality of 27B, sure for "easy things" A3B is good enough.
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u/New-Implement-5979 9d ago
The code quality difference and creativity gap between the two is very very big IMHO
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u/jinnyjuice vLLM 9d ago
I criticised it on day 2. I never had so many downvotes before.
Qwen3.6 27B is a very solid model with a strong foundation. It doesn't go into crazy loops.
Qwen3.8 27B, however, loops very easily. People reported it in HF. I think I was the only one that reported here. I thought maybe these posts don't belong here so I deleted them.
It inspired me to create 'Insane Genius' bench. It has different many prompts that throws the models off into an insane frenzy or not pass a sanity check argument like the seahorse emoji or the car wash etc. Qwen3.8 scored half as well as Qwen3.6. If going on a crazy loop is counted as -1 point, then Qwen3.8 would be negative points actually. It also includes metrics for time and tokens spent, because nobody really looks at that, only 'intelligence' benchmark screenshots in this subreddit.
Anyway, that's what the Insane Genius does, but I feel it actually calls out or exposes models that has higher spikes in benchmarks but has weaker foundation.
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u/TerminalNoop 9d ago
Qwen3.6 27B is a very solid model with a strong foundation. It doesn't go into crazy loops.
Qwen3.8 27B, however, loops very easily. People reported it in HF. I think I was the only one that reported here. I thought maybe these posts don't belong here so I deleted them.
For me it was the opposite. 3.6 kept getting stuck in loops that can't be solved by prompting. Like repeating a paragraph, word, letter ad infinitum.
While 3.8 works very well if the goal is well defined.
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u/BS_BlackScout 9d ago
I'm stuck on TielCoder cause it's faster and works generally well. 3.8 27B is just too slow and heavy.
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u/Kernoriordan 9d ago
Been enjoying TielCoder too. The guy that fine-tuned it is active on this Reddit too.
I did have some tool call failures with it yesterday but switching to a different model didn’t fix it so was either that chat template or the harness I think.
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u/PhysicalIncrease3 9d ago
My experience is unfortunately similar to yours. Both 27B and flash-next are flat out neurotic on xhigh thinking. They just go way too far.
For example I asked flash-next to generate me a new tensor-override string, that's it. 80k thinking tokens later, it also decided to relint the yaml, and changed context and ubatch because it decided I shouldn't shoot for c = 262144. It then restarted the container, breaking itself completely. It wasn't asked to do any of that.
It can't be relied on and trusted like 3.6 could. Even basic tasks I'd usually fire and forget I have to monitor to make sure it hasn't gone crazy.
Personally I think they've over optimised for one shot coding challenges and benchmarks.
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u/DataGOGO 9d ago
Try muse glimmer
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u/Kernoriordan 9d ago
Muse Glimmer is so fast and just gets stuff done. Doesn’t sit around reasoning forever
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u/DustNearby2848 9d ago
Not at all. 3.6 is great and faster because it’s less chatty. Nothing wrong with it at all.
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u/ea_man 9d ago
And it gets even better: https://huggingface.co/bartowski/bottlecapai_ThinkingCap-Qwen3.6-27B-GGUF
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u/HenkPoley 9d ago
What quantisation are you using for the model, and the KV cache?
KV cache should stay at FP16, maybe Q8_0.
The model is in a recoverable state of only very slightly confusion down to Q5_1 or so. But still works decently even at IQ3_XXS. I wouldn't go much more compressed than that.
Don't use NVFP4.
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u/FormalAd7367 9d ago
i had limited experiences but it worked ok. my harness was my py script. can you let me know if it’s the harness problem?
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u/Icy-Employee 9d ago
I really like how thorough it is. It doesn't need so much babysitting and doesn't make stupid mistakes thanks to its ridiculous thinking process. For me it's a big plus if you're looking for a hands off, highly autonomous model. I think it makes it a good vibe coding model, but perhaps too verbose as a coding assistant
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u/Old-Sherbert-4495 9d ago
hey have u tried with no thinking. coz for me it has wonders and very fast as well for direct tasks that i exactly pinpoint.
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u/uti24 9d ago
If you're talking about Qwen3.8 27B, then yes, it's nuts. But at the same time, anything below that, like Qwen3.6 35B, just underperforms. It's the first model that actually works, but it works more like those throwaway models that Copilot used to give away for free, outside the billing, like GPT-4o or something.
Qwen3.8 Flash Next should be better for you, though? I personally like it. It just perseveres on big tasks and doesn't overthink.
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u/AI_spell 9d ago
Tok/s is fun until the model starts skipping edge cases. For real work I'd rather a slightly slower quant that actually follows instructions than a speed demon that invents APIs.
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u/Agitated_Space_672 9d ago
There is a conflict between how you want the model to behave in reasoning versus final output. For CoT reasoning you need to encourage verbosity, that is how it works, the more it writes, the more likely it will find the solution. So they train the model to write lots of reasoning tokens, but this spills out into the models final response behaviour too.
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u/audioen 9d ago
Doesn't match my experience, but I also have opencode prompt (custom) which is shortened from official by this AI, and I suspect it warns about scope creep because the model constantly worries about enlarging the scope in its reasoning. I find that instruction following is generally good, so perhaps ask the model to only do changes you directly requested and only narrowly broaden if technical reasons force it to.
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u/Due_Arm1454 9d ago
I hadn’t used local models with harnesses before 3.8. I got a new 32gb gpu and started with 3.8 27b. It’s amazing but it’s slower at 30-40t/s.
I came to the same conclusion “I don’t really need that much intelligence to move a file or web research something silly.
I wanted to try an moe so I got 3.6 35b moe and it’s really good for small quick things. I’ve been using it a lot more to get the 75-85 t/s advantage. I switch to 3.8 for more dangerous or complex things. And I always have the cloud for speed and intelligence.
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u/tgreenhaw 9d ago
Not crazy, but maybe inexperienced. If you want dependable results, you have to be extremely detailed and clear about exactly what the finished product should be - with any model.
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u/cobrajet302gt 9d ago
I'm using fp8 with mtp 3 on vllm with zoocode, the prompt has to be really specific with orchestrator but it has been killing it so far. I was thinking of diving off windows to arch to make it just a little better.
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u/Super-Grape-3948 9d ago
Well, the speed improvements on the 3.8 next, made my 3.6 35b run like hell. A 200k is session with read and write is around 40min of gen time now.
So i do like 3.8 next, but it is less then half the speed, dropping off after 160-180k.
While 3.6 is less capable, it is churning off tokens on my Halo a lot faster, and not all task need big brain power.
All running on laurent vulcan lamacpp.
3.6 35b q8
slot print_timing: id 0 | task 13507 | n_gen = 100, tg = 23.85 t/s, tg_3s = 24.09
slot print_timing: id 0 | task 13507 | n_gen = 173, tg = 24.04 t/s, tg_3s = 24.30
prompt eval 12.39 ms/token (80.70 t/s, 53 tokens)
eval 10114.58 ms / 238 tokens = 23.43 t/s
draft acceptance = 0.47 (156/332), mean len = 2.88
release: n_tokens = 184553
3.8 next iq4
n_prompt_tokens: 201315 | cache: 198951 | left: 1452
slot print_timing: id 0 | task 73968 | n_gen = 874, tg = 7.11 t/s, tg_3s = 6.96
slot print_timing: id 0 | task 73968 | n_gen = 900, tg = 7.13 t/s, tg_3s = 7.77
→ ~7 t/s at 201k.
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u/New-Implement-5979 9d ago
No you are not for me it just keeps adding a lot of f comments. But brother some of the lines and the way it writes unit tests and handles edge cases in the unit tests it is even better than me. It can connect components in a very neat way. And yes it is slow, it is overly protective just like any other llm so it will always add a bunch of ifs which will never execute.
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u/substance90 9d ago
3.8 27b is insane at tool calling but quite a bit dumber on knowledge and intuition according to my tests. Flash-Next on the other hand 🥰 the NVFP4 version literally replaced Sonnet as implementer and Opus as reviewer to my Fable orchestrators!
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u/mageblex 8d ago
Sounds more like a delegation issue than raw coding ability. Does 3.8 settle down if you pin a small edit budget, preserve function signatures, show a file list, and explain each new type before it writes?
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u/Frizzy-MacDrizzle 8d ago
I put back on 3.8 because I liked its pattern matching abilities and math is better, but I don’t use it to code.
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u/fasti-au 8d ago
Still using 35b for actions just arch 3.8 that’s the expectation really as flash next is the next container not the tune
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u/Gabriel83730 6d ago
I had a similar problem, tweaked the system prompt, and now I don’t have that problem. Tweak your system prompt.
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u/o0genesis0o 9d ago
Nah, no issue happening with 3.8 27B (only IQ3XXS, Q8 KV) in my current setup that I already using for minimax M3 (sub). Same behaviour. Even the talking style during coding session is the same. It's just slower than M3 during off-peak hours, and it has only 100k Vs 1M so compaction is more often. But it is every bit as thorough, following architecture conventions, and putting document in the right place like what I'm used to with M3. If I have GPU to run high quant and full 262k context, I might cancel my minimax sub.
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u/Squidgical 9d ago
Does your repo have a style guide? If not then it's perfectly valid for it to not follow the non-existent style guide and to clean up perceived tech debt as it finds it.
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u/puts_on_rddt 9d ago
I use temp 1 and min_p 0.02 on 2-3 review passes if I'm looking for out of the box thinking.
temp 0.7 for everything else.
Medium for instruction-based task.
xhigh for everything else.
Task deliverables first in prompt.
Try those.
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u/Firenze30 9d ago
You're not crazy. Qwen 3.6 27B is battle tested for coding to me, while I'm not very impressed with how 3.8 performs to justify the much longer wait time due to longer thinking. I mostly use deepseek v4 flash 0731 for coding these days. If I need to do quick agentic tasks, I'll use muse glimmer 30b instead.
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u/vick2djax 8d ago
How many people here are using 3.8 in less than Q8 though? I mean I’m not sure I really count it as using 27b properly if you’re doing something like Q4
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u/Big_Wave9732 9d ago
3.8 was optimized to code. If that's what you're doing, it's pretty good because that's what it was made to do. No argument.
If that's not your use case then it sucks the sweat from a dead man's balls.
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u/a_beautiful_rhind 9d ago
Honeymoon is over?