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r/LocalLLaMA · u/Juulk908701.08.2026, 17:16

Deepseek v4 flash 0731 still not holding up.

The biggest issue with preview was its inability to follow rules prompts and skills. It seems like no matter what you do it ignores them. I've tried first person and second person. I've tried Chinese and English. It does not follow them. That's the only problem with these models and why they're not actually frontier level and not just benchmaxxed. Every user's environment is different and they need to tune the actions and behavior of the model with rules or prompts or skills to exactly what they need to do and if the model ignores then it acts subpar. The newest version of flash has the same issue as the preview version and that's unfortunate.

I run it in native locally. And I've held out making this post cause I know I'm going to get roasted to all fuck but when you can actually run the models locally and when you're not brainwashed by benchmarks and you actually code with them you see the holes. I'm going back to qwen 27b ugh

131

Kommentare

u/RedParaglider201.08.2026, 17:19
This is a valid post I see no reason to flame it maybe you someone out there has some improved template that can help or they have the same problem. I usually don't implement brand new models really quickly because templates are usually spaghetti for a while.
- u/NeedsSomeSnare101.08.2026, 17:44
  "no reason to flame it"... Why are the replies from the top 1% commenters the most disconnected? "Flaming" a post literally just meant many people argued with it, or even just replied to it. That's not the case at all here. Do you just reply to keep your 1% status, regardless of whether your reply actually means anything?
  - u/Borkato101.08.2026, 17:451 Antwort
  - u/Anbeeld101.08.2026, 17:501 Antwort
  - u/RedParaglider101.08.2026, 17:551 Antwort
  - u/[deleted]101.08.2026, 17:56
u/TokenRingAI101.08.2026, 17:22
I'm running it at 3 bit and the model isn't doing well at that quant, it is too lobotomized to be useful. What quant did you try? It's a decent model at full precision
- u/trying4k101.08.2026, 17:26
  What quant are you running at 3bit? And what issues are you seeing?
  - u/TokenRingAI101.08.2026, 17:47
- u/Juulk9087101.08.2026, 17:28
  Native. Full precision
- u/EmPips101.08.2026, 17:29
  The previous checkpoint was unusable until you reached the higher Q3 and Q4 levels of quantization. 13B active can only do so much at low precision I guess.
  - u/live4evrr101.08.2026, 18:01
- u/jld1532101.08.2026, 17:41
  Which 3 bit? The IQ3_XXS from Unsloth was completely broken on launch and has since been updated. The IQ3_S was noticeably better, though I think there will be updates to all their quants. From my testing the IQ3_S DS4F is closer to Kimi K2.6 level than Qwen3.6 27B.
  - u/TokenRingAI101.08.2026, 17:451 Antwort
u/trying4k101.08.2026, 17:23
I had heard someone else talk about this. I'll be curious to hear opinions here since I was considering upgrading hardware to run this. Qwen is great but sucks at design/planning of complex features. I was hoping DS4F helped there. It may help if you put your quant and context as well. Q8 going to be different than Q1.
- u/Juulk9087101.08.2026, 17:28
  I'm running full precision native it's in the thread..
  - u/trying4k101.08.2026, 17:36
u/tomz17101.08.2026, 17:25
There are two possibilities here : \- The entire internet is wrong so far \- OP mis-configured their system
- u/Juulk9087101.08.2026, 17:27
  Great argument have you ran it locally to try it?
  - u/sebt3101.08.2026, 17:373 Antworten
  - u/ObviouzFigure101.08.2026, 17:40
  - u/laterbreh101.08.2026, 17:50
- u/Limp_Classroom_2645101.08.2026, 17:32
  Misconfiguration is possible if OP is experienced the same issues on a frontier models on his setup, otherwise swapping models on a well configured setup should be enough and it should work out of the box, if it doesn't then the model is not good
  - u/Juulk9087101.08.2026, 17:381 Antwort
  - u/laterbreh101.08.2026, 17:511 Antwort
- u/look101.08.2026, 17:39
  DeepSeek V4 preview has been an extremely popular yet extraordinarily mediocre model for a couple months now. Not really a shocker if the GA follows the same pattern. It’s just *really* cheap and not entirely unusable. Cognitive dissonance in the face of contradictory results keeps it in business … because it is *so* cheap people just want to believe. Simple as that.
  - u/silenceimpaired101.08.2026, 18:22
- u/toothpastespiders101.08.2026, 17:47
  The entire Internet has largely been talking about how well it scores on benchmarks, not actually using the thing. Especially on this sub.
u/ShirtuShanks101.08.2026, 17:31
My time with it more or less aligns with yours. It’s great—noticeably better at following GLM 5.2’s schema—but I tried handing off a fairly straightforward website project’s planning to it and it broke almost all major logic that I’d had set in stone.
- u/nomorebuttsplz101.08.2026, 18:27
  I find it also is slower on Mac Studio than GLM 5.2 getting things done, even though t/s is noticeably faster, because it uses more tokens, gets more confused, and makes more mistakes.
u/fastandlight101.08.2026, 17:31
When you say running native locally, do you mean at Fp8? Could your issues be quant related? Are you quantizing kv cache? I was just looking at spinning up a 4bit quant to test on my infrastructure for agentic use (more reasoning than coding, and some data analysis) based on the benchmark numbers. It seems like it might not be worth it.
- u/Juulk9087101.08.2026, 17:35
  I'm saying full precision unquantized. https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731
  - u/fastandlight101.08.2026, 17:38
  - u/Phaelon74101.08.2026, 17:382 Antworten
u/BrewHog101.08.2026, 17:35
I'm wondering if it's a harness thing. I still don't understand how I cannot run long horizon tasks with OpenCode, but I can with Pi. OpenCode seems to be what so many YouTubers have success with, but it's just total crap when it comes to oversight. I have to constantly tell it to continue from just stopping, and it restarts a lot of what the process anyway went through. Same model on Pi doesn't have that issue. I'm testing with Openrouter, so not local yet.
- u/justgetoffmylawn101.08.2026, 17:45
  Interesting. I've tried DS4 with OpenCode and been kinda unimpressed, even though I really like using it on both the web (but not for coding there) and the API for my own projects. Tried it briefly with Reasonix, but haven't used Pi.
  - u/BrewHog101.08.2026, 17:52
- u/NandaVegg101.08.2026, 18:22
  It is not easy to break 2026 mid-to-large models significantly at harness level no matter what it does, assuming that everything the model supposed to do can be done easily through bash/terminal. I suspect the difference between OpenCode and pi is just that pi's prefill is very small (around 3000\~4000 tokens) whereas OpenCode is usually larger, which would cause more confuse for smaller active parameter models. However, there are some notable break patterns in more recent model that are heavily RL'd for agentic benchmarks and/or distilled from other closed models. GLM 5.1 was pretty resilient for whatever you do in prefill, but if you mess with GLM 5.2's reasoning prefill (like adding "Let's think it step by step..." at the beginning of reasoning trace) it breaks pretty hard with spitting out more garbage, language drift, etc.
  - u/BrewHog101.08.2026, 18:31
u/MaterialSuspect8286101.08.2026, 17:37
Could you confirm if the same behaviour also exists in the official API end point? This might be useful to verify if there are some subtle bugs in the local inference engine you are using
- u/Juulk9087101.08.2026, 17:53
  I suppose this could be the case. Maybe the official API endpoint has a different model than what they released for local.
  - u/shing3232101.08.2026, 18:001 Antwort
- u/BrewHog101.08.2026, 17:56
  I'm not having those issues on the API at all. Most of my use cases are not coding by nature, but require heavy tool use and extensive skills. I'm using Pi as my harness most of the time, but currently testing with OpenCode for this model.
- u/VotZeFuk101.08.2026, 18:27
  IMO DeepSeek kinda messed up by not providing an official Jinja template. Looking at Bartowski and Unsloth .ggufs, they have different Jinjas, one of which is rather barren and the other is full of *'hey-lil-bitch-please-think-hard'* pleas and commands. See for yourself on HF. I mean, I know Unsloth are promoting this as fixes and improvements, but I'd rather take what the actual DeepSeek team has to offer :/ It is truly an unfortunate mess in that regard.
u/segmond101.08.2026, 17:38
How are you using it to code? Which coding harness?
- u/[deleted]101.08.2026, 17:48
  [deleted]
- u/Hoppss101.08.2026, 18:01
  Also curious about this too
u/sdexca101.08.2026, 17:38
Idk I code with them and they follow instructions just fine? Are you sure that you don't have some issue running it locally? Maybe something is wrong with your local environment? Have you tried using the DeepSeek Platform API for once just to compare?
u/LikeSaw101.08.2026, 17:38
I've noticed it when I tried the preview version (haven't tried 0731 yet). Maybe its related to default temperature 1.0 the model being too creative and not following instructions well enough.
- u/trying4k101.08.2026, 17:54
  That seems possible to me. I didn't see anything in the documentation for DS4F but Qwen 3.6 27b uses a value of 1 for more creative output and a value of 0.6 for more strict output. It could be similar here?
  - u/Fluxing_Capacitor101.08.2026, 18:10
u/negus123101.08.2026, 17:39
Seemed pretty good to me? I was running the 8 bit quant too. Had it write up an MCP server for an image model so i could give it “sight”
u/fugogugo101.08.2026, 17:39
what does "does not follow prompt" means?
- u/ttkciar101.08.2026, 18:08
  It means poor instruction-following competence. Models should do what they are instructed to do, but some ignore instructions and make up their own task.
u/Hoak-em101.08.2026, 17:41
I threw it in the same harness that I used to make up for the issues with previous GLM models (GLM-5, GLM-5.1, and in some ways GLM-5.2) which is forgecode. This gives it semantic search, a buncha reminders, an enforced TODOlist and plan -> execute requirement, alongside an automatic tool-fixer. This thing CHUGS like it is amazing how fast it can get through work compared to GLM-5.2. Yeah, it uses a lot more tokens, but the output is the same or better. It seems a lot like the GLM series after 5 to me -- potentially brilliant (opus-level) but ya gotta tie it down at certain points.
u/MrGuest1101.08.2026, 17:45
I have it running tp2 with rtx6k in vLLM and it’s amazing so not sure what you’re doing differently. Make sure temp 1 top p 0.95 for coding
- u/TokenRingAI101.08.2026, 17:59
  Which specific quant are you using on RTX 6000?
u/samsteak101.08.2026, 17:46
Curious what others think about this
u/laterbreh101.08.2026, 17:46
I downloaded the model straight from deepseek's hugging face. I swaped the directory for my vllm start script, and ran it through our same agentic loops. Internal measurements for our workloads in the last 24 hours were clearly improved. There must be something wrong on your end. DS4 Flash has been our workhorse and its been reliable and surprising in its ability. Long running long horizon tasks broken into phases and orchestrated cycles with the same model it performs great. Our harnesses are built entirely around opencode.
- u/Fit-Cost-7226101.08.2026, 18:29
  damn whats your setup that you can run it locally?
u/No_Medium205101.08.2026, 17:47
If you're not using the official API and the official harness config on their docs it's not valid
- u/ttkciar101.08.2026, 18:06
  If you're using the API then you might be in the wrong subreddit.
u/sullenisme101.08.2026, 17:55
you must've had really bloated contexts. mine stuck to its cardinal rules consistently
u/ResidentPositive4122101.08.2026, 18:03
This has been a thing w/ deepseek models since the very first 6b and 7b coders they released in ~23. They are extremely "stubborn", likely due to the heavy RL regime. They are great IF your task closely matches the task types they've been trained on. You want to solve math problems? Great. You want it to analyse a math problem and offer you an analysis on a particular facet of the problem? Tough luck, it will still solve the problem :) (simplified, but that's basically it). What I found w/ dsv4-preview is that you need to let them work in their style. If you want precise rules followed, simplify till they work, and then add another task in another window, to follow "migration" from what's done to how you want done. Also, if you have many rules all over the place, simplify them and have them in one place. Also separate rules by "topic" and have several passes as described above. When you get your tasks in a row and dsv4 "gets it", it's a workhorse. But don't expect to come w/ setups from SotA and have them work 1 for 1. Split, iterate, review, improve. That's how you get them to be useful.
u/MyHobbyIsMagnets101.08.2026, 18:03
Works fine for me
u/wayne_oddstops101.08.2026, 18:04
I've grown used to herding the fucker. System prompts and skills instructions definitely need to be stronger w/ DeepSeek. No hedging language or weak instructions written like they're general advice. Tell it exactly what it should do. If your skills require a certain workflow, list the steps and their order. Don't overload w/ negative "Do not" constraints. Firm positive instructions. Provide varied output examples if your workflow depends on it. I could go on and on. I had to write a skill for Claude just to tell it how to write instructions for DeepSeek.
u/CatchDublinSurprise101.08.2026, 18:04
I have read other reports of a very specific weakness in the DS4-flash model of being unable to maintain separate knowledge and perspectives among characters in creative writing and role play. But I have not seen anything that makes me believe this is a symptom of a broader problem.
- u/silenceimpaired101.08.2026, 18:10
  Do you use models for creative writing? If so what models?
u/shing3232101.08.2026, 18:05
if you running local, it does not follow instruction. you should compare official API first before posting your issues. I am guess you are running Sglang？ that particular inference has issue as I heard friend running it and its unusable. so far I have great experience on the official API even comparable to Grok 4.5 or GLM52
u/Potential-Leg-639101.08.2026, 18:09
With a proper orchestrator setup (for example with GLM 5.2) also the „old“ DSV4 Flash was really good. You cant expect, that it can do wveeythin
u/Its_Powerful_Bonus101.08.2026, 18:09
I’m not convinced with any opinions about models which wasn’t tested extensively by users. It’s difficult to believe since yesterday it was possible to check if model behaves as described. I have exactly opposite experience based on refactoring 3 of my side projects and doing a lot of Hermes agent work with ds4 flash yesterday and today. I’m very happy with it comparing to Hy3, ds4 flash preview, minimax m2.7. Qwen 3.6 was also my daily driver when I had to process a lot of information (\~1000 t/s TG is nice with 10-20 concurrent requests) and IMHO Qwen 3.6 27b is very good, but it is another league vs ds4 flash 31.07. Even if ds4 flash is benchmaxed it’s better than anything else now available in similar size. It will be much more reasonable to wait 1 week to come to final conclusions in terms how model behaves. Cheers!
u/jonydevidson101.08.2026, 18:12
You're missing info.

What inference engine?

What harness? Which version?

What system prompt, rules, skills?
u/Potential-Leg-639101.08.2026, 18:13
Via API it seems to be really strong. Running it locally makes no sense to me with that still incredibly cheap prices.
u/Artistic_Okra7288101.08.2026, 18:17
My experience is somewhat similar, but I found the harness makes all the difference. A vanilla Pi.dev and both models suck, but Qwen3.6-27B sucks worse. Claude Code in a project repo with memories and planning mode, Qwen3.6-27B works nearly as good as Sonnet 4.5. DeepSeek v4 Flash (preview) seems to ignore most of the setup in the same project and same harness (Claude Code), just changing out the model.
u/BP041101.08.2026, 18:26
I've been testing 0731 through OpenClaw for agentic workflows and yeah, the rule drift is real — even with explicit system prompts reinforced every 8 turns, it starts ignoring constraints around message 12. Separate system prompt that re-injects every N turns helps but it's a bandaid. Might be a positional bias thing with the longer context.
- u/Juulk9087101.08.2026, 18:30
  This is exactly the route that I tried to take to fix it as well. I even tries to make a chat template with reminders built into it as well. Everything seems to act as a Band-Aid and it never gives you the confidence to let the agent actually open up.
u/Budget-Juggernaut-68101.08.2026, 18:27
I have a simple pipeline that was using mimo v2.5/mimo v2.5pro for and tried changing to Deepseek V4 flash, but it was failing validation checks for the output.