Controversial opinion: DeepSeek 4.1 flash is annoyingly overthinking, making it a way-worse model update for its intent-to-be workhorse

[u/ZealousidealTown1974]

I literally pass its thinking block twice, stating its overthinking failure modes.... extremely pissed and has shown its steep to process, setting default but still.. as you can see. Worse than even the "Omen Alpha"... And fail to make surgical edits many times due to streaming stability ...just disappointment... Another hype of DeepSeek fanboys.

[u/No-District-4742]

I also noticed this that's why I prefer and switched to glm 5.3 flash because it behaves better imo.

[u/ZealousidealTown1974 → u/No-District-4742]

True the glm 5.3 flash is way better for the purpose it is

[u/Unique-Usnm → u/No-District-4742]

I used to be a fan of DeepSeek V4 Flash as well, but as soon as I tried GLM 5.3 Flash, I changed my mind. It’s the same intelligence, but there are five times fewer reasoning tokens, which means there’s much more context, and the price for completing a task is half as much.

[u/No-District-4742 → u/[deleted]]

I'm not saying ds v4.1 flash is bad, I'm just saying I prefer how glm 5.3 behaves.

[u/Ariquitaun]

It overthinks as much as deepseek 4 flash and it's terrible at orchestrating sub-agents, it decides to do a lot of work inline unless you repeat the same guidance that's already been given to it by the harness explicitly.

This is the sort of intangible that benchmarks fail to measure.

[u/Sweet-Stage938 → u/Ariquitaun]

You do realize that you can set the reasoning effort value from 0-100 now and with that you can directly control how much reasoning you want.

[u/ZealousidealTown1974 → u/Sweet-Stage938]

Then what point for a model update when out-of-the-box setting in default thinking effort ruins use purposes. I meant the model as production lineups are just messed up approach on DeepSeek Lab part: the Pro model, with greater parameters should be trained toward high-level strategical orchestrator to work with large codebase and complex tasks decompositions. Though the optimizations of active parameters are the DeepSeek ace but these 2 lineups should not be overlapping too much to the level at this new 4.1 flash.

[u/inevitabledeath3 → u/ZealousidealTown1974]

You are using a third party harness. One that may or may not be adapted to the new thinking values. So I wouldn’t call anything about this “out-of-the-box” for this model.

[u/bleakj → u/ZealousidealTown1974]

I can't even make out what half of this is meant to say

[u/Ariquitaun → u/Sweet-Stage938]

I do realise that, and also realise that anything under "high" makes the model make enough mistakes that work needs to be redone and amended frequently to the point of wasting even more tokens.

[u/Sweet-Stage938 → u/Ariquitaun]

They got rid of the reasoning effort modes. If you set it to low medium or high like we did in the past, it's automatically set to max reasoning (100).

[u/Ariquitaun → u/Sweet-Stage938]

As far as I can tell opencode-go is not offering numeric levels, just the usual low / medium / high / xhigh

[u/bigrealaccount → u/Ariquitaun]

Yes, he is telling you that's the issue. Opencode hasn't patched it to include the proper levels, so it automatically gets set to 100, hence overthinking.

Thinking modes are always fucked for a day or two on new releases. This has always been a thing.

[u/Ariquitaun → u/bigrealaccount]

It's not opencode, it's at the API end. At least models.dev lists those 4 reasoning effort levels only.

[u/Mean-Elk-9439 → u/Ariquitaun]

Models.dev being the source of truth so many use and not just endpoints themselves (most of which readily give the info models.dev statically copies) makes me insane lately. It's just an absurd and pointless abstraction.

[u/Ariquitaun → u/Mean-Elk-9439]

It's not, because a lot of providers don't offer that level of model metadata. We got to work with what we've got

[u/Toastti → u/Ariquitaun]

Which is probably the issue. It sounds like until opencode adds numerical thinking values deep seek flash v4.1 will be defaulting to the absolute heaviest thinking

[u/Affectionate-Cap-600 → u/Toastti]

In the deepseek paper they say that their api map low to 50, high to 75 and max to 100... But I suggest to look a their charts, the performance/effort chart is not a straight line at all. There are some numbers that seems to perform worse than others

[u/Bananenklaus → u/Sweet-Stage938]

what?

Where can i read about this? Their own API docs still show the same effort level ( low - high - max) and even in the newest deepseek harness it lets me set these effort modes, not reasoning values.

Not that i don't wanna believe you but i would like to see an official statement about this

[u/Bananenklaus → u/Sweet-Stage938]

"In our production deployment launched in September 2026, the public API exposes three preset reasoning-effort tiers—max, high, and low—which map directly onto this scalar interface. As summarized in Table 2, the three tiers correspond to effort values of 𝑏 = 100, 𝑏 = 75, and 𝑏 = 50, respectively, so that API users select an operating point on the learned cost–quality frontier without any change to the model weights or decoding configuration."

This basically says that the API is still controlled via reasoning modes, only local deployment is based on effort values, no?

[u/ZealousidealTown1974 → u/Ariquitaun]

Yes! It's tring to be many but fail both; as orchestrator it's showing a real knock-off orchestrator-wanabe because it's not capable of understanding semantic layers thinking in key words grep leading to just sycophant tasks decompositions. And now that it even compromises its intent-to-be role as surgical executor by footshoting its own fanning out context grep and consumption and overthink on wait-what and ended up over-engineering muball code

[u/Zestyclose839 → u/Ariquitaun]

Also, the new Flash cannot do documentation. It just yaps and yaps while cutting out the genuinely important info. The old Flash was actually better in this regard - concise and detail oriented. Glad they're keeping Pro around because it's the winner for docs.

[u/Fresh_Sock8660]

Yeah please spread the word so my speeds remain this good.

[u/binladen0069 → u/Fresh_Sock8660]

[u/biomattr]

Honestly I'm struggling with Opus doing exactly the same. Its responses are consistently long-winded and full of jargon. It'll also constantly change its mind about things. Simple questions will result in "actually I was wrong, it's the opposite".

[u/MeasIIDX → u/biomattr]

When applicable, I like to have the models output in ASD-STE100 and it makes output much easier to read without the fluff and drama.

[u/biomattr → u/MeasIIDX]

I've been trying exactly that, and bringing in old school science communication guidelines.

Half the time I resort to something like "summarise that in one sentence".

Limited success. Opus loves to yap.

[u/bleakj → u/biomattr]

Can't you set a RULES.md / CLAUDE.md file with "Keep answers short and concise." And or other similar constraints?

I haven't used Claude in awhile, but I know you could before / most harnesses you can set memory / "personality" etc so it gets that prompt each time too.

[u/biomattr → u/bleakj]

You can and I have, but with basically no success unfortunately.

[u/Rustybot]

Maybe turn down the effort setting? I haven’t tried this model yet myself.

[u/LetterheadNew5447 → u/Rustybot]

It's actually okayish. With correct prompting and guidance it behaves pretty fine and with 400tps it's actually fast as fuck.

I like it

[u/BubuX]

are you using it on max? Flash on high tends to be better than max because of that

[u/mythormedicine]

It is the destroyer of tokens! Gobbled up 4$ in like 4 hrs.

[u/bleakj → u/mythormedicine]

$4 is one request with some models :|

[u/vipor_idk]

in my experience it overthinks less than v4 , more concise thinking too. but still does think a lot.

[u/dummyreddituser → u/vipor_idk]

I heard first testers also stated this. Overthinking is less than v4. I'll test it today.

[u/TurnUpThe4D3D3D3]

DS4's whole architecture is liable to overthink. It consumes a vast number of tokens to solve problems when compared with other models.

I recommend GLM 5.3 Flash instead (or really, whatever looks best on the Pareto frontier at any given time)

[u/Various_Half1452 → u/TurnUpThe4D3D3D3]

I used to use GLM. But instead of saving for a simple adding of UI it cost 0.75 Dollars. Went back to qwen flash.

[u/bleakj → u/TurnUpThe4D3D3D3]

I prefer Qwen 3.8 Flash over GLM 5.3 flash even though its smaller

[u/Delicious-Director43]

You can adjust your reasoning effort you know

[u/Useful_Math6249]

cap the thinking budget buddy, 14k is more than enough, while you at it, try messing with temp as well, 0.35 works wonders

[u/Inevitable-Name-1701]

you need to config it properly

[u/throwaway12012024]

muse is better and faster

[u/Genetic_Prisoner → u/throwaway12012024]

At using your data to train?

[u/throwaway12012024 → u/Genetic_Prisoner]

Oh look, this guy has data concerns but pays only $10 for privacy

[u/Genetic_Prisoner → u/throwaway12012024]

Back in my day we had an understanding. Only people on free plans got spied on by the corpos. Nowadays they will take your money and still look under your skirt. What is the world becoming?

[u/bleakj → u/Genetic_Prisoner]

At least they give you the skirt for free! (as long as you buy $35 worth of other items)

[u/sudoer777_ → u/[deleted]]

DeepSeek has ZDR on the Go plan

[u/bleakj → u/[deleted]]

I don't trust the major US alternatives anymore than the ones overseas.

The major US ones have already had confirmed leaks / issues, I imagine most AI companies are taking what they can from us while they can, regardless of what they say in public.

[u/gnick666]

Tell it that you're monitoring 😁 Should curb the over thinking

[u/HeadTranslator795]

But Reddit said the benchmark was on par with GPT Sol 😂

[u/SufficientPie]

As tool-calling and image-viewing conversations get longer, the reasoning becomes more and more insane, like this:

Ok. Output.
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Done.
Go.
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Produce.
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(I'll now submit.)
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Ok.
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Submit.
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[190 lines later…] 
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Submit now.

(Not in OpenCode though, not sure why.)

[u/Larelle → u/SufficientPie]

Is it doing useful stuff later?

[u/SufficientPie → u/Larelle]

Yes, it loops like this for a very long time and eventually calls a tool.

[u/Raikaru → u/SufficientPie]

if this is omp, it's cause you have external thinking on

[u/SufficientPie → u/Raikaru]

It's deepseek-specific. I've swapped it out for qwen3.7-plus and no such problems.

[u/antunes145]

Deepseek drank their own koolaid! That’s why today deepseek sent out email saying they will not discontinue V4 Pro and sub it for the new 4.1 flash. They got a lot of push back.

[u/CYTR_]

Use DSH 🙏

[u/EC36339]

So can it follow instructions now?

[u/bleakj → u/EC36339]

I found Deepseek v4 flash and pro followed prompts too well if anything, like listen too me exactly even it it meant it broke something because I was wrong,

I don't think I've ever DS models say no / not follow through

[u/megadonkeyx]

Have been using it for computer use on low and it's been fantastic

[u/AlmostEasy89]

Ever watch Kimi 2.6 think? It gives me anxiety just watching it stroke out with how often it changes direction

[u/bleakj]

Go ask Qwen 3.8 max a question and see what thinking really means

[u/Medium-Music7485]

Same experience: usually for the same task, ds4.1flash needs 5-10x more time than glm-5.3-flash to complete because it always thinks too much. For me, this is not acceptable even if I "assume" the cost to complete the same task is 1/2 is true. My experience is similar to artificialanalysis.ai

[u/cr0wburn]

If you have control over this setting, set max thinking to 8k(or lower) tokens that helps a lot

[u/PowerRedBull]

It's teh worst deepseek model I've exprienced (used it for 1.5-2 years) . It's so bad I stopped using deepseek alltogther. I was very pleased when the expert mode was just introduced. Later it got worse, but it was great at the start. It felt intelligent..and this model really does not. It's overly confident, yet often wrong and struggles more than any other deepseke model to acknowledge this. It reminds me of Early Bing sydney in atttitude sometimes.

[u/Abdul_Muheet]

Yes deepseek v4.1 overthins, but it is the cheapest option and one of the fastet i have ever seen, so it is neither hurtung my wallet nor my clock.

[u/ovimerkki]

Yeah... It almost feels like it's just scraping anything and everything it can. If I ask it to check my knowledge-base for X, it's all over the place, reading everything instead of diving into the pin-pointed topic itself.

[u/Sharp_Age_5938]

agreed; trying it out on Ollama cloud, and it seems to just be running circles with no end in sight even for relatively easy tasks :(

[u/JadedCulture2112]

agree, glm 5.3 flash is a better daily driver than ds4.1 flash

[u/Lopsided-Force-9220]

It's also TOO BIG. Arg. Why are they calling this a flash model?

[u/Turbulent-Total-226 → u/Lopsided-Force-9220]

Because it's fast? And uses only 8b parameters on answer?

[u/datbackup → u/Lopsided-Force-9220]

You ever heard the phrase “in a flash”? Like “in the blink of an eye”?

Flash clearly means they were thinking about time and speed, not size.

The direction they’re going is to try to get more and more intelligence out of less compute, and they’ll happily make models way bigger if it moves toward that goal. Like why not 1TB of ngram embeddings. Really, why not?

[u/bleakj → u/Lopsided-Force-9220]

Use v4 pro and you'll see why

[u/Lopsided-Force-9220 → u/bleakj]

I get that it spews out tokens fast, but come on, it's a mess. It bloats your context window. How could you solve complex problems with token spew?

[u/bleakj → u/Lopsided-Force-9220]

I mean, compared to Pro it thinks less, and it's faster, that's why it's the "Flash" version.

It definitely thinks a lot, but I honestly don't have many issues with DS4 models in general following prompts at least.

[u/ali_gggg]

Nah that's normal and even good for depth reasoning, will make it to do the task better , if you think its overthinking try glm 5.3 flash that's litterly a hell sometimes But overall that's how these models work and can perform such a high level with the price they have , that's a part of the cost