DS isn't better than Sol and Gemini in the benchmarks, but after working with them all for a while now I'm beginning to understand why people find DS _feels_ so much better.
First, it's the tok/s and TTFT from providers. Deepseek blazes through work - at quite good quality - and you don't find yourself waiting for your agents to finish.
Second is the openness. Being able to see the CoT and not being randomly blocked for cyber review. You're never wondering if it's stuck or if it will randomly fail. Combined with the great cost value.. My next subscription will probably be Z.ai
I have to ask what's everyone's issue with the requests to China. We've had 25 years of American big tech basically capture all our data, and use that to make insane profits.
The government that is most likely to use your data against you is the government of the country you live in. If a foreign government wanted to use your data against you the easiest way for them to do it would ironically be to send it to your government.
Makes sense in short term & for personal queries. A German or Norwegian model provider shouldn’t tattle on US Southerners seeking reproductive care.
Long-term & software engineering fronts: I hear occasional railing against “US hegemony”. Anyone concerned that if a new tech gives rise to a different country, they’ll become the source of ire instead? (This isn’t my field, at all.)
The argument has never been that it would be better (a domestic EU provider would obviously be better than either from a data handling perspective). But its not obviously worse
Valid question. I have no 100% factual answers and neither does anyone else here.
So I go with feeling, and that feeling says that China is worse. The feeling is partially supported by the fact that China is softly backing Russia in its attack against the west.
Trump's USA is obviously completely horrible, but he is also in large part a product of Russian influence, which again is part of the larger block in which China resides.
"The west" is a stupid idea America uses to force themselves and Israel into the same league as EU countries.
And China is not really supporting Russia, they make money off the war and want to keep it going as long as possible so both Russia and Ukraine become weak and more open to their influence. Is it evil? Absolutely, but you can't compare them to USA when Trump is actively doing regime change operations, deporting its own citizens and putting sanctions on the ICC.
Chinese communists want to stay in power at all costs but America wants to expand its power into the whole world. It's not the same level of danger at all.
Yes I unfortunately trust those who are motivated by money more than ideology. Greed, while horrible and ugly, is at least very predictable.
Messianic, or even utopian or pretty much any ideology, can lead to absolutely insane levels of destruction. You just don't know what true believers of any sort are willing to do.
To be fair, east and west are in relation to England, not the US.
Trump’s a smelly orange shit but the US at least pretends to have data privacy protections and we can’t legally compel companies to have back doors… yet. I think. It’s very hard to keep up with all the nonsense these days.
Opencode also has a setting to not route to China, though this would probably just disable the models that do not have providiers in alternative locations.
I’ve jumped between mimo and ds quite a bit lately. Mimo seems to be slightly better in terms of what it can achieve in terms of end results. But it gets there very slowly, and in a super convoluted way. Sometimes it’s almost frustrating to read its reasoning. Also I’ve noticed that it tends to not use tools in the harness as much as other models.
I usually default to ds, and if it gets stuck try mimo but usually just for fire and forget tasks. After a bit I come back and see what it did.
I mean... I ported some of my childhood Finnish shareware games for Linux and Rust with DeepSeek V4.1 Flash. It took about six hours of spelunking and finally I was pretty happy with the results! I did it twice. Tokens for both of the games were around 3 euros.
Feels better to me in the sense that it doesn't try to be too clever. When I ask it to do things it actually generates code that a developer would write. That still lets me build software in layers like I do mentally. And it keeps you in control of where abstractions are.
Some people would say that you don't need this at all with the frontier llms. But when extending existing software or writing software that fits into an existing ecosystem, I find I still need to offer a lot of guidance. And it's better that the context window isn't flooded with code that was reverted after I looked at it.
> Some people would say that you don't need this at all with the frontier LLMs
Hit the nail on the head. Development methodologies pivoting at the whim of frontier models have been one of the things that have been worrying me as of late.
it doesn't worry me too much tbh. This is really just cutting corners and going this route leaves you with some kind of additive ship of theseus piece of software you don't understand and require your frontier model to work with, which just... becomes more additive and gross.
right now we're in some proverbial heyday of everyone seeing how absolutely grand of a thing they can make with frontier models.
over time i expect some old design philosophies to take hold with people who use LLM's successfully and find a place again paired with developers who understand why such layered methodologies are important... because this whole AI thing wasn't the silver bullet CEO's and crazy execs thought it was. They will eventually get sick of the support burden of badly designed stuff. i anticipate a bit of pendulum swinging back towards "still using AI, but we need smart people to do it because it's expensive and we want it done right". Right now a lot of companies are being extremely reckless imo and they haven't fully realized/felt the cost implications of trainwreck software yet.
I just use a suite of tests to enforce the mechanical standards (dry, encapsulation, etc) and arch documents for shape + reviews of new work against the two. I’m not seeing any significant drift.
Yea, this is one thing that gets me. Vibe coding and freestyle prompting gets wildly different results. Starting from a set of specs and expectations routinely gets me reviewable and we'll tested code. Sure, running the full test suite alot slows things down and uses more tokens, but when I hit a five hour or weekly window, I just roll up my sleeves and start working without the model because the code is layed out the way I expected and is designed to fit my meatbag workflow and pace.
Interesting. I’ve been using GLM 5.3 Flash for a couple months and found it to be great. What has it been doing that you found to be “too clever”? It sometimes gets stuck in loops. I once asked it to do a code review of a simple pattern and it went off on a 45 odyssey trying to analyze too much. But other than those, it’s been a pretty good daily workhorse. And you can’t beat the price. Even Deepseek 4.1 Flash is more expensive. And I switched to GLM 5.3 Flash from DS 4 Flash, my previous daily driver, which I also found quite good. Anyway, my point isn’t to start a “which model is better” subthread, but was just curious what behavior you’ve seen where it’s “too clever.”
Huh, doesn't DeepSeek take the longest because it overthinks way too much? Haven't tested this release yet but was very disappointed with past ones because it takes an eternity to do anything because it constantly overthinks and questions itself way too much instead of just starting.
It thinks a bit more, but if the cache reads are free or almost free, and it blasts through 10x faster than GPT Sol/Terra/Luna, it doesn't really matter. Just don't use the max thinking for implementation, go with high or low.
I feel like I’m getting good value out of my subscription and they have the cheapest GLM-5.3 API rates I could find in Europe. I moved to them recently from Mistral for the better prices.
They do have a slightly different daily inference capacity allowance, but it’s actually not too bad once used to it. I was forced to up my game and figure out what GLM-5.3 Flash and Deepseek-4.1 Flash could actually do for me, rather than just throwing everything at GLM-5.3. Quite happily chugging along with a nice balance across them now.
I am hoping Melious make the new DS 4.1 Flash 1017 available soonest.
Assuming canadian or dane, but maybe there are other places he’s threatened. There are canadian inference providers running deepseek, and definitely eu ones idk about ones based in denmark.
The economic sovereignty of any nation, but especially smaller ones like canada and denmark, is harmed by sending our money out of the country when there are options available in country.
> The economic sovereignty of any nation, but especially smaller ones like canada and denmark, is harmed by sending our money out of the country when there are options available in country.
I respectfully disagree. I subscribe to the comparative advantage economic principle, so I'm fine with international trade.
What I am saying isn’t inconsistent with comparative advantage. International trade is fine, but only when it’s not a commodity easily obtained inside your own nation. Open model inference is a commodity. There is no real lost opportunity cost from using canadian owned GPUs or chinese owned ones. In fact, as a country with abundant cheap power, and physical space, canada’s probably a better place than many to run inference. That’s what I meant by “when there are options in country”.
To add to this, Fireworks (and many competitors, e.g. enumerated here[0]) offer zero-data retention inference, to anyone. DeepSeek cloud does not. It's part of their terms that anything you send them is fair game for them to train models on.
It’s pretty clear despite what they want you to think Anthropic and OpenAI train on everyone’s data who has a subsidized plan. Only way you get ZDR is by paying API prices. Also note that it is zero retention, not zero training.
> "Also note that it is zero retention, not zero training"
The "green" badges explicitly say the opposite:
> "Private: this provider does not train on prompts and does not retain prompt data."
I.e. for the example of Fireworks, this is the precise language they use to describe ZDR:
> "More technically: prompt and generation data exist only in volatile memory for the duration of the request. If prompt caching is active, some prompt data (and associated KV caches) can be stored in volatile memory for several minutes. In either case, prompt and generation data are not logged into any persistent storage."
I'd rather my data go to China and directly to labs that release open weight models (DeepSeek is MIT licensed still); instead of closed labs and the NSA/CIA; who has far better ability to hurt me than China does.
Without getting too much into politics; over the past several decades, one country is going around starting wars, regime changes and other interventionism, or initiating economic warfare (Huawei sanctions because they were getting too competitive to US companies; GPU restrictions; tariffs; etc), and that's not one administration: it's US policy over the past few years.
Why link to a number, which means roughly nothing, instead of the specific details on the countries you're trying to compare? I have no idea how to break down or make my own assessment of what 0.83 or 0.18 means. Does that 0.83 for the US include all of the people being shipped off without due process to El Salvador, or do they count that as part of El Salvador's Human Right Index?
People like to bring up the Uyghurs immediately, but never really have the same smoke for the US. Also, do we include the prison population in human rights? Do we include access to healthcare? What about systems perpetuating poverty like lack of access to public transportation?
To pre-emptively be clear on my stance or potential bias, I stand by no country in the world. I was born in Iran, and my parents left when I was young to the US because they believed in the American Dream. The irony being that the dominos of why they left were initiated by the US and UK. And long before Trump was elected, but certainly immediately afterwards, I decided to leave the US once I had the means. (Also because of the lack of public transit, the $100k medical bills after an accident, being punched by a homeless person, and having my possessions stolen repeatedly and the cops giving me a :shrug:). I moved to Japan, where I experienced blatant racism for the first time. I moved to Hong Kong (and tbh no real complaints so far), but I am technically subject to the freedom of speech constraints on China.
I have no allegiance to any country or specific people. My allegiance is to all living beings.
I'm not arguing against a reputable institution, I'm arguing against using that reputation + their final number alone without going through the process to verify the methodology myself, which you didn't link to. You only showed the conclusion, which to all of us, should mean very little until we look into how that number was arrived at.
And I don't care if they're reputable, I will look at the methodology and make my own conclusion either way. Appeals to authority are not a substitute for critical thinking.
We're not talking about a few % difference here. If you want to look into it then fine, but you have to come up with some really good evidence if you think you can debunk anything here.
Have you ever wondered why Manus founders gave up billions of dollars from Meta and “voluntarily” moved back to Beijing? One country does things out in the open, while the other operates in the shadows. The latter worries me more.
It worries me far more what people are ok with us doing in broad daylight.
> Have you ever wondered why Manus founders gave up billions of dollars from Meta and “voluntarily” moved back to Beijing?
This country wouldn't be such a shithole if we were able to bring the wealthy to heel. I suspect most people on earth see China as the more competently governed country. Understandably, this forum tends to select for people who view private investment positively, but this is clearly insufficient by itself to produce functional societies.
Just to test, will GLM – hosted by an infra provider other than Z.AI – also stop answering if I asked controversial questions from the pov of its country of origin? Or third party infra providers can perform some unhinging on the model before hosting? Or actually the blocking happens even before the question reaches the LLMs in that country?
(Note: my paid LLM access at this point are only via DS and GLM subs - 90-95% usage; and a free Gemini Pro access. I hope that adds some cred that I am not trying to take potshots at the their country of origin).
to add to the corrent sibling answer, the unhinging is called abliteration by the wider community but it's hard to find someone who will serve that over API.
It doesn’t have to be better, it just had to be nearly equivalent. I’ve been doing a lot of vulnerability work lately (trying to get ahead of the bots finding exploits in our code) and the frontier models feel like a chaperone at a church dance: get too close and they show up with the ruler and enforce the safety margin. Using deepseek and glm and qwen, I’ve gotten nearly equivalent results without the constraints. I still use frontier models for things but outside of a company environment, I’m happy to spend 1/10 the money, get the same results, and not have try and trick the AI into working.
In practice, it does help to immediately be able to tell if you missed something or wrote something incorrect in your prompt, compared to if you didn't have access to the CoT. If you see that it goes off wondering about stuff or thinking about stuff that you know are already solved/known, you cancel the execution, edit the prompt and redo, then observe the CoT again, until you've resolved any "misdirections".
Both OpenAI and Anthropic hides so much of that, that you don't really know if it misunderstood something or you got something wrong, until all the turns are complete or you directly see tool calls that implicitly tells you something is wrong.
You don't need to read any papers to see this effect concretely when developing together with LLMs.
If I were part of a cabal that is interested in preserving control and trying to secure trillions of dollars, it would be a minor effort to nudge and bend the curve against competitors like the Asian AI complex.
They are showing this new version scoring significantly higher across multiple coding benchmarks, beating Sol 5.6 (6.1 is the latest) across 10/11 benchmarks.
I am going back to DeepSeek for writing code. I was trying out using Sol and liking the result, but this should close a lot of that gap. I use Opus/Sol for an implementation plan and code review also use them for grilling. DeepSeek is also running verifications.
Faithful to the original theme. Apropos. +1 on adding the prompt as long as it’s affordable and easy enough Simon, and not too distracting/noisy/time consuming all considered!
i let 4 different agents review the same dirty code base (200+ files changed, c++, game engine):
- deepseek v4.1 flash via deepseek API: took 20m to find one p1 issue that was relevant and a p2 issue that was irrelevant
- glm 5.3 flash NVFP4 via TP2 DGX Sparks: took 1h 5m to find a p3 documentation issue
- gpt 5.6 sol: took 5m30s for one p1 and one p2 issue. both relevant
- gpt 6.1 sol: took 4m30s for two p1 and one p2 issue. both relevant.
Worth to mention: none of those findings overlapped.
I let deepseek v4.1 flash and gpt 6.1 sol both plan fixes for the summary of the findings. in this particular scenario: gpt 6.1 sol planned to increase a buffer size from 128 to 256 to fit the 191 elements that blew past a test scenario. it included some headroom. deepseek v4.1 flash on the other hand decided to make it exactly 191 to satisfy the test, for example.
The TPS decode on deepseek is impressive. I never had a second / feeling of being left aone and though "damn this thing is working". Despite all my efforts, it took ~4x longer in a review, found less and planned worse than GPT 6.1 Sol. Wall clock time. I don't have token counts for comparison. If I were to grade those models from 1-10 (10 = top tier GG), I'd give deepseek a solid 6.5 and 6.1 sol a 7.5-8.0 maybe.
In your case, is it a public repository that the llms might have found the issues for online?
I too am thinking about creating some benchmarks that I can understand myself. The LLM 'cheating' by finding known issues through some sort of history or online source is a path you want to protect against. Then again, you DO want them to be able to look online for people having similar issues.
LLMs are non-deterministic, you could run the same prompt on the same code base with the same models, and have different result. I don't think only one run with each model is enough to draw conclusions.
The new model is amazing. Had it build a full Angular Material & CDK components showcase via DeepSeek Harness and it easily did the best job of any recent models I've seen.
Honestly given how good the open weight models are getting I don’t see a viable business model for OpenAI and Anthropic anymore. The profit is in the compute, not the model. OpenAI and Anthropic spent all their money renting compute from others so yes they can sell lightweight model inference but they have a massive structural disadvantage now as others can always sell good-enough inference much cheaper.
There’s also zero stickiness to models and most companies now have their own internal routing utilities so devs and users can just keep the same API endpoint and move between models with a single line of code swap.
Unless something changes with the big labs and fast, they look pretty f*cked.
People will pay for more intelligent models. Astra and Fable are more intelligent than these open weight models and can solve problems that are much more difficult, and that's extremely valuable. It's not any more complicated than that.
Yes but that’s a tiny portion of AI use cases. Maybe 1-2% max. The big labs need a lot more than that to survive.
You’re assuming other model creators can’t build these mega models. Evidence is they’ve simply decided it’s a fools game to do so and they’re focused on where the use cases are.
It is more complicated than that when the valuations and business plans are contingent on those companies AI having complete dominance at every level.
If companies only use frontier models for occasional difficult tasks, not only does that affect volume, it basically elminates stickiness and incentivizes companies to instead of using their products continuosly, find ways to move from the expensive stuff to the cheaper stuff.
That trajectory appears to be changing. Virtually every company I see is shifting more and more to open weight models. Anthropic’s slowing growth rate is already spooking investors and that shift is just getting started.
I use Opus 5.5 and swarms of about 50 deepseek models to reverse engineer old videos games. The scale is roughly 600k loc generated from pure asm, simply by knowing what the old compiler is. Truly staggering stuff.
I really like the having DeepSeek Flash + Opus 5.5 as advisor/reviewer at the moment. I run through a $200 Claude Code subscription in less than 24 hours now just for a single project, and I hate fiddling with different accounts every day. Now that I get a 200 USD api credits inlcuded with the subscriptions I have rewritten my coding agent workflow completely.
I swear I have the same question in my head always when I read such statements! I have never once reached 90% that includes everyday usage across multiple repos, artefact generation, device debugging, long running harness work!
I am not saying OP is not telling the truth. I just really want to understand!
I decided to try something a little weird research wise the other day analyzing about a thousand older research papers and burned through my $100 chatgpt sub + 3 resets in three hours and only finished half of them. It's the first time I've done that, though.
It’s because they don’t know what they’re doing. It’s like when openAI was using 10,000 agent clusters to solve problems… then realised the model could do the same work on its own and solve the same problem.
Anyone who tells you they have a “team of subagents” and “code reviewer” agents and “project manager” agents doesn’t know what they are doing. That’s how they burn thousands of dollars in a day.
Do you have a source for OpenAI discovering they can solve Navier Stokes with a single agent instead of a 10k swarm?
I think I’ll trust the experience of the people in the labs who have unlimited token budgets and are running experiments continuously over some anonymous rando slinging mud on the internet.
Also my own experience, which says having code reviewer agents is one billion percent useful. That comment alone completely disqualifies you as a reliable source of info.
Not sure if this will help you but it helped me. I was using subagents and workflows for lots of long running tasks, and part of that work included running tests. If tests took longer than 5 minutes, the cache expired, and the whole thing was then being processed as uncached input.
I created a skill to better manage agents, including a way to keep the caches valid, and it has decreased my usage considerably. I now struggle to use my 100% each week and I hit claude hard. I have a reset expiring in 5 days and I'm trying to burn down the tokens (68% with 3d 8h left) quickly so I can use my reset before it expires.
Token efficiency just means understanding the way the system works. Best person to help with that... my old friend claude.
Yeah, I should see if I can create a similar skill. The tests do spend some time to complete.
Claude: Confirmed: 195M of the 212M cache tokens (92%) come from full re-writes after 5+ minute gaps, accounting for 77% of relative cost. Now I'll check the existing wrappers so the skill
builds on them.
Not OP, but I typically ask it to report status of whatever is running; it can run a background task that reports to it every N minutes. With Claude Code in VS Code and a Pro subscription, it seems like the one-hour cache is the default, making it an issue less often.
After I stopped letting Claude run tests, my token usage went from "gone by Wednesday" to "what can I automate this Saturday so my tokens won't go to waste".
As a dev who hasn't used LLMs as an embedded part of their work, and uncertain about it all, i'd be curious to know:
- How long do you reckon it would take a human to do the task?
- What percentage of features/ feature-extensions you add would have been done at all if you didn't have agentic assistance? Is there a tension between llm productivity gains and 'because we can' feature additions?
- Are many of the tasks for throwaway v1 features, exploratory tries?
- How speced out is the work you give it, eg did this task have pages of specs, how long did the spec take to write?
- How deeply will you manually review the work and how much time will it take? Will you read all the code / review at a high level and interrogate with llm / have the llm review and self iterate?
For what I am currently doing, years, if not a decade or maybe even two. Claude has already been running for 3 months constantly working on this project. I've been trying to do this several times before, but the scope outgrew my available time/life, too big for one person. But now I can actually build a production release by just setting the direction and let Claude to all the wiring, plumbing, tests etc.
I do now add all the major features I want, and write a task list where I add a few more minor tasks each week that just come to my mind. I need to write them down before I forget. I often ask Claude to implement several different implementations/algorithms and compare them. There are plenty of trade-offs, which usually end up in building configuration profiles, so the application can work optimally in multiple environments.
The specification is actually really large, and has grown over time as I've been learning and new ideas has come to my mind. I do not review/audit the code, it's massive would take me months, no chance! I do however use code inspection/audit tools like TSAN, ASAN and more. I will also spend time on full code reviews with the coming Fable 5.5 when a release is about ready. I do however review behavior together with Claude, and this is also where the specification grow, because this and that should be possible, but not this and that. User stories also helps.
Keep doing what you're doing, this guy clearly is just revving his engine because he likes the way it sounds and can afford the gasoline. I wouldnt be asking people like this for advise, they're completely off the rails.
There's no reason to blow through tokens this way, there's a 99% chance nobody uses the output (except maybe themselves).
I have some fairly large personal software projects and I still don't understand what useful work people are getting out of burning all these tokens on unattended agents.
What exactly are they doing for you that's giving you $200 of value in such a short timeframe?
Not OP, but I used to run out in a few days, for me it was a matter of suboptimal code review practices with many subagents looking at different aspects, often expanded to look at more than the diff.
I can drain a $200 OAI plan in 2 hours flat working on my 10 projects simultaneously.
Maybe you're not being ambitious enough with these things? They are at their most fun when you spec out sprawling architecture documents and watch 1+8 astras buzzsaw through it.
I'm only at 260B for the year, though. I'm sure someone who has used more will be along shortly to chime in
E: The other subthread talking about tests is correct. Expensive models should not waste a single second waiting on tests or validation. If you cannot assign a single cheap model to be the test bitch, then just tell them to frontload everything and clean up later as a single wave.
Not meant as a bad faith response but have you shipped anything? Do you have income from this? Why 10 projects apart from fueling ADHD and "never finishing a project"?
Not OP and while I’m not draining my accounts in a single day/hour, I’m hitting limits with a $200-Claude and $100-OpenAI account.
> Have you shipping anything?
Yes, hundreds of PRs and shipped new features to 3 different platforms (day job and 2 side-business apps/platforms I’ve been developing for many years, pre-LLM). Those new features have gone out to customers and been used.
> Do you have income from this?
Well my day job for one, and I’ve been able to more easily sell to new clients with my side business. Without the changes I’ve shipped I would have been able to land a couple of the clients I’ve signed on recently.
I don’t think OP’s message implied they are never finishing a project, maybe they edited it?
In addition to the 3 income-generating projects, I have a number of other “just for me” projects. Some are mature now and only need occasional development when I want a new feature. Some are in flux as I build them out (like my “software factory”).
At this point I’m limited by compute and tokens more than anything else.
Does work pay for your subscription? I think many workplaces would worry about personal accounts for work projects.
EDIT: but thanks for a useful answer to OP's question. As a programming dabbler I also find it hard to imagine how to push much past the 5hr limit on $20USD subscription.
Interesting, coming from a similar situation, I'm interested what workflow you landed on. Opus planning + available to be called for deep thinking + opus reviwer? or?
By not using existing tools and one shooting everything through the API - I.e. instead of decompile via Ghidra they will try to get source via capstone genersted assembly.
It is like putting a jet engine on a shipping container and wondering why it needs to be constantly kept in afterburner to at least scrape on the road while smarter users put wheels on the shipping container.
I've been using DS v4.1 Flash daily for nearly a month now on Agent Swarm experiments. I've tested it against GLM-5.3-flash and Opus 5.5. DS 4.1 comes out tops for me. Opus 5.5 is still the smartest, but DS 4.1's speed and price more than make up for the "intelligence" gap. I've ran Chief/Builder/Reviewer swarms autonomously for multiple days. I think my new workflow is going to have DS 4.1 Flash do all the engineering and just have Opus review its work. 4.1 Flash really is a very impressive model. Kudos to the Deepseek team.
I have started phasing into DeepSeek v4 (Dwarfstar quant from Antirez) as my daily driver and it's going very well. It can one shot iOS apps on my Mac Studio, it's a little slow and sometimes needs retries but can definitely do medium difficulty real work for me.
I didn't try the last flash model because I would need to use SSD streaming (I have 128 GB), but the benches for this release are so compelling (and it using less tokens to get tasks done) it might be worth the throughput hit. I will probably give it a try when I have some downtime.
It feels very freeing to be doing useful work on a local model on my own hardware with my own coding harness. The TCO argument is starting to look compelling too.
Try the Qwen 3.8 flash next model on DwarfStar - you will be very surprised. I’ve been using the 4 bit quant almost exclusively after having DS v4 flash be my daily driver. Qwen is faster and seems to be much better at coding tasks.
That's way cheaper than openai and other web search providers. I have no doubt that AI demand justifies all the compute money big tech is spending now, but I also think there are so many over valued VC funded companies that are going to crash in the next few years.
Does DeepSeek provide the inference code along with the weights? I read here recently that for models where only the weights are released, providers have to implement which could be different than what the labs themselves use.
I wonder how you would be able to reliably rely on a model like that for agentic work, even for implementation only after careful planning, how are you making sure you are not wasting tokens reviewing and correcting it over and over again ?
I may be missing something so apologies if I'm telling you something you already know but: there are plenty of web search APIs for agents - the Brave one is the one I use: https://brave.com/search/api/
I gave it a kick and got mixed results. For example, searching for a Microsoft product couldn't find its landing page, and searching for a niche open source package landed in the Debian repos for the top hits, but the home page was in the first 10 hits.
Lovely to see they're doing their own search though, I love how many folk are actively working on search clones at the moment.
This and (the not quite yet open) Kimi K2.8 are my main coding engines, and I find them incredibly cost efficient. I find Deepseek in particular is really good with Svelte work.
Right now I use each of them in their own respective coding harnesses, deepseek-harness and kimi-code, but I do wonder what they are like inside something like opencode.
At the beginning of the month I realized I had more tokens and more capabilities on Opencode at 8$/month via DS 4.1 flash, than I had on a 200$ account in June with the american providers.
Literally both more tokens and capabilities than Opus 4.8/GPT 5.5.
Even if you pay per token, you're still unlikely to outspend american providers.
What does it matter if you’re not hitting your cap with OpenAI/Anthropic in the first place? This thread is apparently populated with the highest of powerusers running multiple OpenClaws. Us peasants aren’t concerned with meters or cost/token - just quality - and nobody comes close to OpenAI or Anthropic in that regard.
Pretty funny to say "us peasants aren't concerned with ... cost/token", as if most people ("peasants") won't find it absolutely insane to be paying $200/mo for a subscription.
Probably a week ago someone posted a thread with the title "Why isn't the industry freaking out about DeepSeek 4.1 Flash?"
I have been intrigued about Chinese models for a long time, but never tried anything except an early Qwen. My daily driver was Luna, with occasional elevation to Terra or Sol - all on 5.6 as I found the output of 6 jarringly incomprehensible.
I still had 95 USD on my OpenRouter account, so I thought I would give DeepSeek Flash 4.1 a try.
I think now I can understand the person who created that thread.
Here is the setup I settled on, after a lot of refactoring and calibration of the workflow:
I have a "plan" prompt which explains to the agent (Luna) that I want to have a discussion about an idea I have in mind. The agent should use the Socratic method to pull out all the necessary info from me using pointed questions. When the agent feels it has a complete picture, it should elevate to a higher level model (Sol), and ask its review. If Sol has complaints, Luna keeps asking me until the points raised by Sol are clarified. The review rounds with Sol continue until Sol says "ready to implement".
At this point Luna creates four files in a directory I specify:
1. PLAN.md: the implementation plan with all the details that came out from the design discussion
2. PHASES.md: implementation phases
3. DECISIONS.md: design decisions (this serves the same role as ADRs aka. architectural decision records)
4. DIARY.md: append-only log of what happened during implementation
Files 2-4 are written by the implementing agent. Luna only adds headers to explain the purpose of the files.
Once these files are there, I start up a new Pi session, switch to Deepseek 4.1 Flash and then point it to this plan folder.
And then it goes brrrrr... :-)
Here is what I implemented in the past week using this method:
1. A capture/analysis/decompilation pipeline for C64 demos: I ask the agent to start a selected demo through VICE, and say "on" or "off" when I want it to start/stop capture. The capture process uses VICE's builtin trace functionality: the trace log is piped into Clojure where it is saved into an optimized binary format (chunks of trace events). Once I have everything captured, I unleash my analysis pipeline which checks the write patterns of the executed code: what it wrote (VIC, SID, CIA, memory) and when (rasterline, cycle). Then it splits the execution trace into segments by type deduced from the write patterns (decruncher, loader, raster, music) and then decompiles each segment into both traditional ASM files and a s-exp based syntax which roundtrips back into the in-memory representation. Once I have the results of the analysis pipeline, I can tell the agent to analyze any demopart and it can explain how the effects are built down to the tiniest timing detail. As a first test, I asked it to create a clean-room implementation of Fairlight's legendary intro and two days later I had it, with everything in sexp assembly, including the music routine and its data. (The reason it took so long was that this was the first decomp and it provided a lot of feedback to the pipeline development.) Finally I had it write an assembler, linker and packager which creates a PRG from the assembled and linked artifact, then runs it through VICE. Done.
2. A DSP compiler which lets me compile sexp forms of a C-like DSP language into native DSP kernels running on the JVM, JS and WASM. Currently I have about 600 tests which verify that the behavior of the three targets matches that of a reference interpreter written in Clojure. I have a full numeric tower with i32, f32, f64, support for arrays and structs, typical control flow constructs, and a library of DSP building blocks like oscillators, filters, LFOs, delays and a chorus effect. By the time this is ready (end of the weekend?), I will be able to build synthesizers like Synth1, Surge XT or Vital in Clojure/Script, compile the DSP kernels in the browser, choosing between JS or WASM backends, driving the kernels via AudioWorklets and sequencing them from the host language.
There are two power tools at play here:
1. Deepseek Flash 4.1
2. Clojure and ClojureScript
Deepseek is a BEAST. It just goes on and delivers, phase after phase. What it needs: clear instructions - this is covered by the planning process quite well, but I still find it needs some steering from time to time (the right nudget of wisdom dropped into its thought stream at the right moment works wonders)
And Clojure... well. That article PG wrote about LISP (Beating the averages): what I witness now proves all of those claims.
The agent LOVES Clojure. Especially the REPL (not so much the parentheses).
Winning strategy: give it two tools: start_jvm and clojure_eval. Implement everything else it may need - tools to start up HTTP servers, compile ClojureScript, add dependencies, capture a C64 demo, run Playwright via spel, etc. - as lightweight Clojure namespaces which provide APIs to all these functionality. Let these namespaces be thin: the namespace and the functions therein shall have docstrings which tell the agent what the functions do, but the functions themselves shall be lightweight proxies to the real implementations in sub-namespaces. Then point the agent to these top-level API namespaces via your AGENTS.md. Bam, instant discoverability with minimal context usage. It will do EVERYTHING through clojure_eval and the round-trip time will be phenomenal, a perfect match to the raw speed of the agent.
...
Now all I need to figure out is what will I do when all my dreams become manifest in flesh.
Isn't there a lot of overlap between PLAN, PHASES and DECISION that pollutes the context ? Whereas a simple PLAN.MD to put the harness on track and an ARCHITECTURE as a single source of truth to prevent it go offtrack would suffice and gives it more space for higher level of reasoning ?
Have you thoroughly tested a slightly simpler workflow with one less stage but a frontier model doing the initial planning? Luna is amazing but it's a deliberate trade off designed for speed and cost savings. You can also give them a rule about speaking clearly.
DS isn't better than Sol and Gemini in the benchmarks, but after working with them all for a while now I'm beginning to understand why people find DS _feels_ so much better.
First, it's the tok/s and TTFT from providers. Deepseek blazes through work - at quite good quality - and you don't find yourself waiting for your agents to finish.
Second is the openness. Being able to see the CoT and not being randomly blocked for cyber review. You're never wondering if it's stuck or if it will randomly fail. Combined with the great cost value.. My next subscription will probably be Z.ai
also it's cheap, using it in opencode is great
Note that opencode routes deepseek requests to China.
Doesn’t that depend on the router service provider?
I have to ask what's everyone's issue with the requests to China. We've had 25 years of American big tech basically capture all our data, and use that to make insane profits.
Sure. Can you tell me why that means that it's ok for Chinese big tech to capture all our data?
The government that is most likely to use your data against you is the government of the country you live in. If a foreign government wanted to use your data against you the easiest way for them to do it would ironically be to send it to your government.
Makes sense in short term & for personal queries. A German or Norwegian model provider shouldn’t tattle on US Southerners seeking reproductive care.
Long-term & software engineering fronts: I hear occasional railing against “US hegemony”. Anyone concerned that if a new tech gives rise to a different country, they’ll become the source of ire instead? (This isn’t my field, at all.)
This is a false equivalency. American providers don't answer to their government.
My code is opensource, china already has it. For my professional work we have entreprise plans with OpenAI / Anthropic
For an average European, why is it better than routing requests to the USA?
Chime-in for an average non us citizen why is it better ?
The argument has never been that it would be better (a domestic EU provider would obviously be better than either from a data handling perspective). But its not obviously worse
Valid question. I have no 100% factual answers and neither does anyone else here.
So I go with feeling, and that feeling says that China is worse. The feeling is partially supported by the fact that China is softly backing Russia in its attack against the west.
Trump's USA is obviously completely horrible, but he is also in large part a product of Russian influence, which again is part of the larger block in which China resides.
"The west" is a stupid idea America uses to force themselves and Israel into the same league as EU countries.
And China is not really supporting Russia, they make money off the war and want to keep it going as long as possible so both Russia and Ukraine become weak and more open to their influence. Is it evil? Absolutely, but you can't compare them to USA when Trump is actively doing regime change operations, deporting its own citizens and putting sanctions on the ICC.
Chinese communists want to stay in power at all costs but America wants to expand its power into the whole world. It's not the same level of danger at all.
Yes I unfortunately trust those who are motivated by money more than ideology. Greed, while horrible and ugly, is at least very predictable. Messianic, or even utopian or pretty much any ideology, can lead to absolutely insane levels of destruction. You just don't know what true believers of any sort are willing to do.
To be fair, east and west are in relation to England, not the US.
Trump’s a smelly orange shit but the US at least pretends to have data privacy protections and we can’t legally compel companies to have back doors… yet. I think. It’s very hard to keep up with all the nonsense these days.
> and we can’t legally compel companies to have back doors
In a post-Snowden world where the existence of PRISM is no longer a secret, I'm not sure how folks can keep stating this with a straight face
> want to keep it going as long as possible
note that the volume of EU and US support to Ukraine is obviously calibrated to the same goal.
Opencode also has a setting to not route to China, though this would probably just disable the models that do not have providiers in alternative locations.
If you pay for a business plan on openrouter (I know, it’s not opencode, just another option), you can geo lock providers if China is a concern.
Deepseek and Muse are my go to for cheap. both work perfect for basic and mid tier tasks
What about mimo v2.6 pro?
I’ve jumped between mimo and ds quite a bit lately. Mimo seems to be slightly better in terms of what it can achieve in terms of end results. But it gets there very slowly, and in a super convoluted way. Sometimes it’s almost frustrating to read its reasoning. Also I’ve noticed that it tends to not use tools in the harness as much as other models.
I usually default to ds, and if it gets stuck try mimo but usually just for fire and forget tasks. After a bit I come back and see what it did.
Usually great but it infuriates me sometimes with what it does. DeepSeek is more consistent and certainly faster.
I mean... I ported some of my childhood Finnish shareware games for Linux and Rust with DeepSeek V4.1 Flash. It took about six hours of spelunking and finally I was pretty happy with the results! I did it twice. Tokens for both of the games were around 3 euros.
Muse is fantastic for open source work. At least 1.3 was, for its price. Wonder makes less economic sense.
Is that a dig on DeepSeek not offering subscription plans?
If so OpenCode Go looks good and includes DeepSeek models (yet to try myself).
I wonder what quantization opencode uses for DS models.
Everyone wonders that.
I think they just pass through requests to DeepSeek, so whatever upstream offers.
Feels better to me in the sense that it doesn't try to be too clever. When I ask it to do things it actually generates code that a developer would write. That still lets me build software in layers like I do mentally. And it keeps you in control of where abstractions are.
Some people would say that you don't need this at all with the frontier llms. But when extending existing software or writing software that fits into an existing ecosystem, I find I still need to offer a lot of guidance. And it's better that the context window isn't flooded with code that was reverted after I looked at it.
> Some people would say that you don't need this at all with the frontier LLMs
Hit the nail on the head. Development methodologies pivoting at the whim of frontier models have been one of the things that have been worrying me as of late.
it doesn't worry me too much tbh. This is really just cutting corners and going this route leaves you with some kind of additive ship of theseus piece of software you don't understand and require your frontier model to work with, which just... becomes more additive and gross. right now we're in some proverbial heyday of everyone seeing how absolutely grand of a thing they can make with frontier models. over time i expect some old design philosophies to take hold with people who use LLM's successfully and find a place again paired with developers who understand why such layered methodologies are important... because this whole AI thing wasn't the silver bullet CEO's and crazy execs thought it was. They will eventually get sick of the support burden of badly designed stuff. i anticipate a bit of pendulum swinging back towards "still using AI, but we need smart people to do it because it's expensive and we want it done right". Right now a lot of companies are being extremely reckless imo and they haven't fully realized/felt the cost implications of trainwreck software yet.
What if those “cost implications” are just asking Opus 6.7 to fix whatever issue cropped up?
[delayed]
I just use a suite of tests to enforce the mechanical standards (dry, encapsulation, etc) and arch documents for shape + reviews of new work against the two. I’m not seeing any significant drift.
Yea, this is one thing that gets me. Vibe coding and freestyle prompting gets wildly different results. Starting from a set of specs and expectations routinely gets me reviewable and we'll tested code. Sure, running the full test suite alot slows things down and uses more tokens, but when I hit a five hour or weekly window, I just roll up my sleeves and start working without the model because the code is layed out the way I expected and is designed to fit my meatbag workflow and pace.
> it doesn't try to be too clever
DS is miles better than GLM in this regard. I wanted to add that as PC is planning to go to GLM next.
Interesting. I’ve been using GLM 5.3 Flash for a couple months and found it to be great. What has it been doing that you found to be “too clever”? It sometimes gets stuck in loops. I once asked it to do a code review of a simple pattern and it went off on a 45 odyssey trying to analyze too much. But other than those, it’s been a pretty good daily workhorse. And you can’t beat the price. Even Deepseek 4.1 Flash is more expensive. And I switched to GLM 5.3 Flash from DS 4 Flash, my previous daily driver, which I also found quite good. Anyway, my point isn’t to start a “which model is better” subthread, but was just curious what behavior you’ve seen where it’s “too clever.”
Huh, doesn't DeepSeek take the longest because it overthinks way too much? Haven't tested this release yet but was very disappointed with past ones because it takes an eternity to do anything because it constantly overthinks and questions itself way too much instead of just starting.
I think, as long as you don't set thinking to max and use a fitting system prompt, it's not a big issue.
It thinks a bit more, but if the cache reads are free or almost free, and it blasts through 10x faster than GPT Sol/Terra/Luna, it doesn't really matter. Just don't use the max thinking for implementation, go with high or low.
Why not use max thinking for implementation? For cost reasons?
Because it doesn't make difference for my use. High is good enough! If you do an assistant for yourself, low is more than enough.
It reduces the speed and tends to overthink sometimes which is really annoying. For programming, 4.1/high is the bread and butter in my daily work.
For time reasons.
4.1 Flash on medium is a solid builder agent
Yes, and this version explicitly addresses that
LLMs don't think.
No need to use a chinese provider, in case that's an issue for you. https://fireworks.ai provides this model for an ok price.
There are multiple others in EU, and in US. Cheaper, more expensive, slower, faster. So much choice.
If one is not American, why prefer American? Chinese fine by me, I trust the US providers just as little.
There's also European providers for these models. More than one!
Could you recommend me a few?
Scaleway, Mistral, Lyceum, Verda, Silo AI, Siili, Upcloud, CSC, Gcore, Nebius...
I haven't found that Mistral serve DS.
They do not yet. They are testing it but they go with GLM right now.
I’ll add Melious.[1]
I feel like I’m getting good value out of my subscription and they have the cheapest GLM-5.3 API rates I could find in Europe. I moved to them recently from Mistral for the better prices.
They do have a slightly different daily inference capacity allowance, but it’s actually not too bad once used to it. I was forced to up my game and figure out what GLM-5.3 Flash and Deepseek-4.1 Flash could actually do for me, rather than just throwing everything at GLM-5.3. Quite happily chugging along with a nice balance across them now.
I am hoping Melious make the new DS 4.1 Flash 1017 available soonest.
[1] https://melious.ai
There's even a European OpenRouter competitor
https://www.edenai.co/
Thanks, i didn't know about these guys. Is their pricing competitive?
Also https://cortecs.ai/detailedServerlessView/deepseek-v4.1-flas... (I like their site bit more over Eden)
Which ones? And how is the price?
Completely unrelated, but it's so odd to see upside down '8's in the first column of the base tables on their pricing page.
I figure (hope) buying from DeepSeek directly benefits them the most so they can release even more improvements and open-weight models.
Plus only one country has threatened to annex mine and it wasn't China.
Assuming canadian or dane, but maybe there are other places he’s threatened. There are canadian inference providers running deepseek, and definitely eu ones idk about ones based in denmark.
The economic sovereignty of any nation, but especially smaller ones like canada and denmark, is harmed by sending our money out of the country when there are options available in country.
> The economic sovereignty of any nation, but especially smaller ones like canada and denmark, is harmed by sending our money out of the country when there are options available in country.
I respectfully disagree. I subscribe to the comparative advantage economic principle, so I'm fine with international trade.
What I am saying isn’t inconsistent with comparative advantage. International trade is fine, but only when it’s not a commodity easily obtained inside your own nation. Open model inference is a commodity. There is no real lost opportunity cost from using canadian owned GPUs or chinese owned ones. In fact, as a country with abundant cheap power, and physical space, canada’s probably a better place than many to run inference. That’s what I meant by “when there are options in country”.
yeah thats why hordes of indians control your control now ya dumb shit
To add to this, Fireworks (and many competitors, e.g. enumerated here[0]) offer zero-data retention inference, to anyone. DeepSeek cloud does not. It's part of their terms that anything you send them is fair game for them to train models on.
[0] https://openrouter.ai/deepseek/deepseek-v4.1-flash
(The badges next to companies indicate their privacy terms, i.e. green/yellow/red)
It’s pretty clear despite what they want you to think Anthropic and OpenAI train on everyone’s data who has a subsidized plan. Only way you get ZDR is by paying API prices. Also note that it is zero retention, not zero training.
> "Also note that it is zero retention, not zero training"
The "green" badges explicitly say the opposite:
> "Private: this provider does not train on prompts and does not retain prompt data."
I.e. for the example of Fireworks, this is the precise language they use to describe ZDR:
> "More technically: prompt and generation data exist only in volatile memory for the duration of the request. If prompt caching is active, some prompt data (and associated KV caches) can be stored in volatile memory for several minutes. In either case, prompt and generation data are not logged into any persistent storage."
https://docs.fireworks.ai/guides/security_compliance/data_ha...
I'd rather my data go to China and directly to labs that release open weight models (DeepSeek is MIT licensed still); instead of closed labs and the NSA/CIA; who has far better ability to hurt me than China does.
Without getting too much into politics; over the past several decades, one country is going around starting wars, regime changes and other interventionism, or initiating economic warfare (Huawei sanctions because they were getting too competitive to US companies; GPU restrictions; tariffs; etc), and that's not one administration: it's US policy over the past few years.
Have a look here:
https://ourworldindata.org/grapher/human-rights-index-vdem
Why link to a number, which means roughly nothing, instead of the specific details on the countries you're trying to compare? I have no idea how to break down or make my own assessment of what 0.83 or 0.18 means. Does that 0.83 for the US include all of the people being shipped off without due process to El Salvador, or do they count that as part of El Salvador's Human Right Index?
People like to bring up the Uyghurs immediately, but never really have the same smoke for the US. Also, do we include the prison population in human rights? Do we include access to healthcare? What about systems perpetuating poverty like lack of access to public transportation?
To pre-emptively be clear on my stance or potential bias, I stand by no country in the world. I was born in Iran, and my parents left when I was young to the US because they believed in the American Dream. The irony being that the dominos of why they left were initiated by the US and UK. And long before Trump was elected, but certainly immediately afterwards, I decided to leave the US once I had the means. (Also because of the lack of public transit, the $100k medical bills after an accident, being punched by a homeless person, and having my possessions stolen repeatedly and the cops giving me a :shrug:). I moved to Japan, where I experienced blatant racism for the first time. I moved to Hong Kong (and tbh no real complaints so far), but I am technically subject to the freedom of speech constraints on China.
I have no allegiance to any country or specific people. My allegiance is to all living beings.
Sorry but you're arguing against a reputable institution here.
I'm not arguing against a reputable institution, I'm arguing against using that reputation + their final number alone without going through the process to verify the methodology myself, which you didn't link to. You only showed the conclusion, which to all of us, should mean very little until we look into how that number was arrived at.
And I don't care if they're reputable, I will look at the methodology and make my own conclusion either way. Appeals to authority are not a substitute for critical thinking.
We're not talking about a few % difference here. If you want to look into it then fine, but you have to come up with some really good evidence if you think you can debunk anything here.
lol the US was a .60 in 1789 during slavery. this chart is useless.
Have you ever wondered why Manus founders gave up billions of dollars from Meta and “voluntarily” moved back to Beijing? One country does things out in the open, while the other operates in the shadows. The latter worries me more.
It worries me far more what people are ok with us doing in broad daylight.
> Have you ever wondered why Manus founders gave up billions of dollars from Meta and “voluntarily” moved back to Beijing?
This country wouldn't be such a shithole if we were able to bring the wealthy to heel. I suspect most people on earth see China as the more competently governed country. Understandably, this forum tends to select for people who view private investment positively, but this is clearly insufficient by itself to produce functional societies.
This is really a roundabout way of saying you have no plans to live, or perhaps even travel, to China.
The link points to OpenRouter with multiple providers.
still a myracle to me what people achieve with it? I tried so many things with it, everything is broken it produces
> Deepseek... My next subscription will probably be Z.ai
Wait wut? You do realize that they are two different companies, right?
Z.ai (well technically alibaba) used to support both GLM and DS models in their API and coding plans.
> not being randomly blocked for cyber review
Just to test, will GLM – hosted by an infra provider other than Z.AI – also stop answering if I asked controversial questions from the pov of its country of origin? Or third party infra providers can perform some unhinging on the model before hosting? Or actually the blocking happens even before the question reaches the LLMs in that country?
(Note: my paid LLM access at this point are only via DS and GLM subs - 90-95% usage; and a free Gemini Pro access. I hope that adds some cred that I am not trying to take potshots at the their country of origin).
It will not. Ask DeepSeek about Tiananmen Square. It will happily answer
to add to the corrent sibling answer, the unhinging is called abliteration by the wider community but it's hard to find someone who will serve that over API.
I love using glm 5.3 more than deepseek because of performance
I do not use DS for coding, but for various natural language processing tasks (even translation) in large volume, and it works wonder.
It is very capable for a lot of tasks, not only coding like the other open models.
Agreed.
It doesn’t have to be better, it just had to be nearly equivalent. I’ve been doing a lot of vulnerability work lately (trying to get ahead of the bots finding exploits in our code) and the frontier models feel like a chaperone at a church dance: get too close and they show up with the ruler and enforce the safety margin. Using deepseek and glm and qwen, I’ve gotten nearly equivalent results without the constraints. I still use frontier models for things but outside of a company environment, I’m happy to spend 1/10 the money, get the same results, and not have try and trick the AI into working.
Agreed, but don’t subscribe to z.ai; their inference is terrible. Stick with openrouter
>> Being able to see the CoT
Dont assume that is how your model decides...
https://arxiv.org/abs/2305.04388
https://arxiv.org/abs/2307.13702
https://arxiv.org/abs/2503.08679
> they frequently generate CoT explanations rationalizing those answers
I can't tell if this is a really human thing to do, or if it's just a sycophantic thing to do when your master likes a good story.
Fair, but you can see the logprobs, right? That alone is pretty valuable.
This is like one of those folk theories that have some truth but in practice it is useless.
I love COT and gives me a good idea about the thought process. Sure it doesn’t work always
In practice, it does help to immediately be able to tell if you missed something or wrote something incorrect in your prompt, compared to if you didn't have access to the CoT. If you see that it goes off wondering about stuff or thinking about stuff that you know are already solved/known, you cancel the execution, edit the prompt and redo, then observe the CoT again, until you've resolved any "misdirections".
Both OpenAI and Anthropic hides so much of that, that you don't really know if it misunderstood something or you got something wrong, until all the turns are complete or you directly see tool calls that implicitly tells you something is wrong.
You don't need to read any papers to see this effect concretely when developing together with LLMs.
For 99% of the tasks any model really would do, even GPT 5.4 from an year ago.
It's only for long running/very difficult cases that the differences are here, and they are small.
If I were part of a cabal that is interested in preserving control and trying to secure trillions of dollars, it would be a minor effort to nudge and bend the curve against competitors like the Asian AI complex.
How would you go about that?
> My next subscription will probably be Z.ai
Wait... you just spent 3 paragraphs singing the praises of DS... and then you say you're going to subscribe to the one that makes GLM. ?
(I do tend to agree with you, though I tend to favor GLM-5.3-flash over DS 4.1 flash.)
For us neophytes, what does this mean: “randomly blocked for cyber review”?
E.g., your prompt for simple auth works (e.g., decode JWT), but your sessions stop because you are flagged for cybersecurity risk.
The Fable is the worst offender, and it is not purely Anthropic's fault.
[delayed]
The company's own news page: https://api-docs.deepseek.com/news/news261017
They are showing this new version scoring significantly higher across multiple coding benchmarks, beating Sol 5.6 (6.1 is the latest) across 10/11 benchmarks.
I am going back to DeepSeek for writing code. I was trying out using Sol and liking the result, but this should close a lot of that gap. I use Opus/Sol for an implementation plan and code review also use them for grilling. DeepSeek is also running verifications.
Here are some pelicans. The "max" one took over 11 minutes: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
Definitely an improvement on deepseek-v4-flash-0731: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
You should start asking for animated three.js pelicans too, the 2D pelicans seem to have saturated.
Faithful to the original theme. Apropos. +1 on adding the prompt as long as it’s affordable and easy enough Simon, and not too distracting/noisy/time consuming all considered!
The best bicycle is "low".
so i did some apples to bananas comparison.
i let 4 different agents review the same dirty code base (200+ files changed, c++, game engine):
- deepseek v4.1 flash via deepseek API: took 20m to find one p1 issue that was relevant and a p2 issue that was irrelevant
- glm 5.3 flash NVFP4 via TP2 DGX Sparks: took 1h 5m to find a p3 documentation issue
- gpt 5.6 sol: took 5m30s for one p1 and one p2 issue. both relevant
- gpt 6.1 sol: took 4m30s for two p1 and one p2 issue. both relevant.
Worth to mention: none of those findings overlapped.
I let deepseek v4.1 flash and gpt 6.1 sol both plan fixes for the summary of the findings. in this particular scenario: gpt 6.1 sol planned to increase a buffer size from 128 to 256 to fit the 191 elements that blew past a test scenario. it included some headroom. deepseek v4.1 flash on the other hand decided to make it exactly 191 to satisfy the test, for example.
The TPS decode on deepseek is impressive. I never had a second / feeling of being left aone and though "damn this thing is working". Despite all my efforts, it took ~4x longer in a review, found less and planned worse than GPT 6.1 Sol. Wall clock time. I don't have token counts for comparison. If I were to grade those models from 1-10 (10 = top tier GG), I'd give deepseek a solid 6.5 and 6.1 sol a 7.5-8.0 maybe.
What is p1, p2?
This read like a conversation you were having in your head -- unfiltered.
priority 1,2 etc, more like severity of the cases
It’s a very common abbreviation; i had no issue understanding GPs post tbh
Was that the old DS 4.1 Flash or 4.1 Flash 1017?
I tried it out today, was the new one: 1017.
What reasoning effort?
> Worth to mention: none of those findings overlapped.
This is why using multiple model families is important. Don't limit yourself by being a stan for this or that company.
In your case, is it a public repository that the llms might have found the issues for online?
I too am thinking about creating some benchmarks that I can understand myself. The LLM 'cheating' by finding known issues through some sort of history or online source is a path you want to protect against. Then again, you DO want them to be able to look online for people having similar issues.
Flagship gpt models are crazy efficient. 6.1 Sol is like a savant that looks at a problem and spits out the answer seemingly without thinking.
> Worth to mention: none of those findings overlapped.
Yet to be obsolete.
If none of the findings overlapped then LLMs are either
- For some reason choosing only a subset of issues.
- Different LLMs actually learn different things.
I suspect the former since they do this for me and it's extremely annoying sometimes
you probably tried with DS on max and I don't blame you.
But try it on high instead of max if possible. max is known for being overly verbose to the point of being detrimental compared to high at times.
for example Simmons pelican on max took 11minutes on max and 1 minute on high. and they are comparable.
LLMs are non-deterministic, you could run the same prompt on the same code base with the same models, and have different result. I don't think only one run with each model is enough to draw conclusions.
The new model is amazing. Had it build a full Angular Material & CDK components showcase via DeepSeek Harness and it easily did the best job of any recent models I've seen.
Here's a small benchmark that compares the new checkout with the previous one:
https://slopgrid.com/deepseek-41-horses
Honestly given how good the open weight models are getting I don’t see a viable business model for OpenAI and Anthropic anymore. The profit is in the compute, not the model. OpenAI and Anthropic spent all their money renting compute from others so yes they can sell lightweight model inference but they have a massive structural disadvantage now as others can always sell good-enough inference much cheaper.
There’s also zero stickiness to models and most companies now have their own internal routing utilities so devs and users can just keep the same API endpoint and move between models with a single line of code swap.
Unless something changes with the big labs and fast, they look pretty f*cked.
People will pay for more intelligent models. Astra and Fable are more intelligent than these open weight models and can solve problems that are much more difficult, and that's extremely valuable. It's not any more complicated than that.
Yes but that’s a tiny portion of AI use cases. Maybe 1-2% max. The big labs need a lot more than that to survive.
You’re assuming other model creators can’t build these mega models. Evidence is they’ve simply decided it’s a fools game to do so and they’re focused on where the use cases are.
> It's not any more complicated than that.
It is more complicated than that when the valuations and business plans are contingent on those companies AI having complete dominance at every level.
If companies only use frontier models for occasional difficult tasks, not only does that affect volume, it basically elminates stickiness and incentivizes companies to instead of using their products continuosly, find ways to move from the expensive stuff to the cheaper stuff.
And yet, nearly all of enterprise money spent on AI tokens in the US is going to either OpenAI or Anthropic.
That trajectory appears to be changing. Virtually every company I see is shifting more and more to open weight models. Anthropic’s slowing growth rate is already spooking investors and that shift is just getting started.
That's why they are constantly fearmongering and trying to get other models outlawed. If nothing else, under the pretense of national security.
I use Opus 5.5 and swarms of about 50 deepseek models to reverse engineer old videos games. The scale is roughly 600k loc generated from pure asm, simply by knowing what the old compiler is. Truly staggering stuff.
Its a great update, I ran a benchmark comparing verbosity and intelligence compared to old weights.
- Reasoning effort = 1 is now actually usable and produces 2.5x less tokens than old weights, without intelligence lost.
- Overall 20% less tokens at each reasoning effort.
- Same intelligence and same tool-calling pattern.
More details in blog post https://sference.com/blog/deepseek-v41f-1017-benchmark
Improved on exactly the items that I hear people complain about with the previous checkpoint!
I really like the having DeepSeek Flash + Opus 5.5 as advisor/reviewer at the moment. I run through a $200 Claude Code subscription in less than 24 hours now just for a single project, and I hate fiddling with different accounts every day. Now that I get a 200 USD api credits inlcuded with the subscriptions I have rewritten my coding agent workflow completely.
> I run through a $200 Claude Code subscription in less than 24 hours now just for a single project
How?
Loading tons of MCPs and never compacting
not reviewing its ongoing work and spawning subagents that end up looping each other
I swear I have the same question in my head always when I read such statements! I have never once reached 90% that includes everyday usage across multiple repos, artefact generation, device debugging, long running harness work!
I am not saying OP is not telling the truth. I just really want to understand!
in my experience there's a huge difference in token usage when creating new projects versus working on existing ones
I decided to try something a little weird research wise the other day analyzing about a thousand older research papers and burned through my $100 chatgpt sub + 3 resets in three hours and only finished half of them. It's the first time I've done that, though.
It’s because they don’t know what they’re doing. It’s like when openAI was using 10,000 agent clusters to solve problems… then realised the model could do the same work on its own and solve the same problem.
Anyone who tells you they have a “team of subagents” and “code reviewer” agents and “project manager” agents doesn’t know what they are doing. That’s how they burn thousands of dollars in a day.
Do you have a source for OpenAI discovering they can solve Navier Stokes with a single agent instead of a 10k swarm?
I think I’ll trust the experience of the people in the labs who have unlimited token budgets and are running experiments continuously over some anonymous rando slinging mud on the internet.
Also my own experience, which says having code reviewer agents is one billion percent useful. That comment alone completely disqualifies you as a reliable source of info.
Its easy when you spawn a couple of subagents in a homelab with a rack with 5+ servers.
This is how one of my claude code session looks like right now, one prompt from yesterday:
Not sure if this will help you but it helped me. I was using subagents and workflows for lots of long running tasks, and part of that work included running tests. If tests took longer than 5 minutes, the cache expired, and the whole thing was then being processed as uncached input.
I created a skill to better manage agents, including a way to keep the caches valid, and it has decreased my usage considerably. I now struggle to use my 100% each week and I hit claude hard. I have a reset expiring in 5 days and I'm trying to burn down the tokens (68% with 3d 8h left) quickly so I can use my reset before it expires.
Token efficiency just means understanding the way the system works. Best person to help with that... my old friend claude.
Yeah, I should see if I can create a similar skill. The tests do spend some time to complete.
Claude: Confirmed: 195M of the 212M cache tokens (92%) come from full re-writes after 5+ minute gaps, accounting for 77% of relative cost. Now I'll check the existing wrappers so the skill builds on them.
How do you keep the caches valid?
Ask Claude, it's pretty wild to see.
Not OP, but I typically ask it to report status of whatever is running; it can run a background task that reports to it every N minutes. With Claude Code in VS Code and a Pro subscription, it seems like the one-hour cache is the default, making it an issue less often.
Some to think about with both Claude and skills.
Can you invert things. What’s deterministic and what needs “thinking”?
Can you create a script that runs things chaining together the deterministic parts with calling out to Claude for glue that needs “thinking”.
> including a way to keep the caches valid
Maybe doing bogus small requests to prevent the cache from expiring?
I mean in the API you can set the cache expiration time right? But I guess claude code can't do this
> my old friend claude.
Awfully new old friend.
Google is the ex, Altavista is the high school sweetheart who tragically passed at a young age.
After I stopped letting Claude run tests, my token usage went from "gone by Wednesday" to "what can I automate this Saturday so my tokens won't go to waste".
How do you do that?
Don't suppose you would be happy to share the skill? Or is it fairly unique to your work flow?
I think the tests problem is really having an impact for me. Hadn't considered it before so thanks for flagging.
Yesterday in my CC there was an update that added a feature to auto compact immediately prior to the cache being invalidated.
As a dev who hasn't used LLMs as an embedded part of their work, and uncertain about it all, i'd be curious to know:
- How long do you reckon it would take a human to do the task?
- What percentage of features/ feature-extensions you add would have been done at all if you didn't have agentic assistance? Is there a tension between llm productivity gains and 'because we can' feature additions?
- Are many of the tasks for throwaway v1 features, exploratory tries?
- How speced out is the work you give it, eg did this task have pages of specs, how long did the spec take to write?
- How deeply will you manually review the work and how much time will it take? Will you read all the code / review at a high level and interrogate with llm / have the llm review and self iterate?
For what I am currently doing, years, if not a decade or maybe even two. Claude has already been running for 3 months constantly working on this project. I've been trying to do this several times before, but the scope outgrew my available time/life, too big for one person. But now I can actually build a production release by just setting the direction and let Claude to all the wiring, plumbing, tests etc.
I do now add all the major features I want, and write a task list where I add a few more minor tasks each week that just come to my mind. I need to write them down before I forget. I often ask Claude to implement several different implementations/algorithms and compare them. There are plenty of trade-offs, which usually end up in building configuration profiles, so the application can work optimally in multiple environments.
The specification is actually really large, and has grown over time as I've been learning and new ideas has come to my mind. I do not review/audit the code, it's massive would take me months, no chance! I do however use code inspection/audit tools like TSAN, ASAN and more. I will also spend time on full code reviews with the coming Fable 5.5 when a release is about ready. I do however review behavior together with Claude, and this is also where the specification grow, because this and that should be possible, but not this and that. User stories also helps.
Keep doing what you're doing, this guy clearly is just revving his engine because he likes the way it sounds and can afford the gasoline. I wouldnt be asking people like this for advise, they're completely off the rails.
There's no reason to blow through tokens this way, there's a 99% chance nobody uses the output (except maybe themselves).
Yes, but what was the task that they were working on? This tells nothing.
That’s the “how” we’re wondering about.
I have some fairly large personal software projects and I still don't understand what useful work people are getting out of burning all these tokens on unattended agents.
What exactly are they doing for you that's giving you $200 of value in such a short timeframe?
You're doing this wrong.
Not OP, but I used to run out in a few days, for me it was a matter of suboptimal code review practices with many subagents looking at different aspects, often expanded to look at more than the diff.
I can drain a $200 OAI plan in 2 hours flat working on my 10 projects simultaneously.
Maybe you're not being ambitious enough with these things? They are at their most fun when you spec out sprawling architecture documents and watch 1+8 astras buzzsaw through it.
I'm only at 260B for the year, though. I'm sure someone who has used more will be along shortly to chime in
E: The other subthread talking about tests is correct. Expensive models should not waste a single second waiting on tests or validation. If you cannot assign a single cheap model to be the test bitch, then just tell them to frontload everything and clean up later as a single wave.
Not meant as a bad faith response but have you shipped anything? Do you have income from this? Why 10 projects apart from fueling ADHD and "never finishing a project"?
Some of us want to build and don't care that it doesn't monetise.
Surely such people does not have 100usd/hour budget?
Not OP and while I’m not draining my accounts in a single day/hour, I’m hitting limits with a $200-Claude and $100-OpenAI account.
> Have you shipping anything?
Yes, hundreds of PRs and shipped new features to 3 different platforms (day job and 2 side-business apps/platforms I’ve been developing for many years, pre-LLM). Those new features have gone out to customers and been used.
> Do you have income from this?
Well my day job for one, and I’ve been able to more easily sell to new clients with my side business. Without the changes I’ve shipped I would have been able to land a couple of the clients I’ve signed on recently.
I don’t think OP’s message implied they are never finishing a project, maybe they edited it?
In addition to the 3 income-generating projects, I have a number of other “just for me” projects. Some are mature now and only need occasional development when I want a new feature. Some are in flux as I build them out (like my “software factory”).
At this point I’m limited by compute and tokens more than anything else.
Does work pay for your subscription? I think many workplaces would worry about personal accounts for work projects.
EDIT: but thanks for a useful answer to OP's question. As a programming dabbler I also find it hard to imagine how to push much past the 5hr limit on $20USD subscription.
Work provides a stipend/reimbursement of $100, the rest I pay for.
It’s paid for itself though, at least from my side business income.
Would love to see these 10 projects that are being worked on simultaneously
Interesting, coming from a similar situation, I'm interested what workflow you landed on. Opus planning + available to be called for deep thinking + opus reviwer? or?
After opus 5.5 release I find it very hard to run out on a 100 sub...
I'll bite. Wtf are you working on that takes $200 a day and you're willing to pay for?
By not using existing tools and one shooting everything through the API - I.e. instead of decompile via Ghidra they will try to get source via capstone genersted assembly.
It is like putting a jet engine on a shipping container and wondering why it needs to be constantly kept in afterburner to at least scrape on the road while smarter users put wheels on the shipping container.
This is a new model.
I've been using DS v4.1 Flash daily for nearly a month now on Agent Swarm experiments. I've tested it against GLM-5.3-flash and Opus 5.5. DS 4.1 comes out tops for me. Opus 5.5 is still the smartest, but DS 4.1's speed and price more than make up for the "intelligence" gap. I've ran Chief/Builder/Reviewer swarms autonomously for multiple days. I think my new workflow is going to have DS 4.1 Flash do all the engineering and just have Opus review its work. 4.1 Flash really is a very impressive model. Kudos to the Deepseek team.
Ok but this is a new model with significant architectural differences and new training run.
What would be the best local harness to run this? It feels there are 1000s now.
I have started phasing into DeepSeek v4 (Dwarfstar quant from Antirez) as my daily driver and it's going very well. It can one shot iOS apps on my Mac Studio, it's a little slow and sometimes needs retries but can definitely do medium difficulty real work for me.
I didn't try the last flash model because I would need to use SSD streaming (I have 128 GB), but the benches for this release are so compelling (and it using less tokens to get tasks done) it might be worth the throughput hit. I will probably give it a try when I have some downtime.
It feels very freeing to be doing useful work on a local model on my own hardware with my own coding harness. The TCO argument is starting to look compelling too.
Try the Qwen 3.8 flash next model on DwarfStar - you will be very surprised. I’ve been using the 4 bit quant almost exclusively after having DS v4 flash be my daily driver. Qwen is faster and seems to be much better at coding tasks.
This is a new model, not the same as a different one.
Other discussion: https://news.ycombinator.com/item?id=50129045
at this point there are 100s of benchmarks.
so AI labs can just pick which to show
Hugging Face link: https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017
Web search: $0.75 per 1,000 calls
That's way cheaper than openai and other web search providers. I have no doubt that AI demand justifies all the compute money big tech is spending now, but I also think there are so many over valued VC funded companies that are going to crash in the next few years.
Does DeepSeek provide the inference code along with the weights? I read here recently that for models where only the weights are released, providers have to implement which could be different than what the labs themselves use.
I wonder how you would be able to reliably rely on a model like that for agentic work, even for implementation only after careful planning, how are you making sure you are not wasting tokens reviewing and correcting it over and over again ?
The native web search api is 10x cheaper than OpenAIs. I hope they will release the web search api as a standalone API.
I may be missing something so apologies if I'm telling you something you already know but: there are plenty of web search APIs for agents - the Brave one is the one I use: https://brave.com/search/api/
Tavily one is free, too. Easy to use cli with a skill.
I gave it a kick and got mixed results. For example, searching for a Microsoft product couldn't find its landing page, and searching for a niche open source package landed in the Debian repos for the top hits, but the home page was in the first 10 hits.
Lovely to see they're doing their own search though, I love how many folk are actively working on search clones at the moment.
Already released https://api-docs.deepseek.com/guides/search/
At this point I am way more excited by DeepSeek releases than anything from OpenAI or Anthropic.
The speed and cost/intelligence ratio is just outrageous.
This and (the not quite yet open) Kimi K2.8 are my main coding engines, and I find them incredibly cost efficient. I find Deepseek in particular is really good with Svelte work.
Right now I use each of them in their own respective coding harnesses, deepseek-harness and kimi-code, but I do wonder what they are like inside something like opencode.
Is it already updated to the newest version on Opencode?
Yes. It’s actually free to use at the moment. Of course with a quota.
oh is “Flash” a cool sounding word again since Adobe abandoned it?
Anyone using DS as CLI coding agent which harness do you recommend? Is there anything as good as CC/Codex?
I use deepseek via fireworks using Claude code.
When I type deepseek on my CLI it launches Claude with my fireworks API key. Works very well and feels almost free.
By far my favorite model.
At the beginning of the month I realized I had more tokens and more capabilities on Opencode at 8$/month via DS 4.1 flash, than I had on a 200$ account in June with the american providers.
Literally both more tokens and capabilities than Opus 4.8/GPT 5.5.
Even if you pay per token, you're still unlikely to outspend american providers.
What does it matter if you’re not hitting your cap with OpenAI/Anthropic in the first place? This thread is apparently populated with the highest of powerusers running multiple OpenClaws. Us peasants aren’t concerned with meters or cost/token - just quality - and nobody comes close to OpenAI or Anthropic in that regard.
Pretty funny to say "us peasants aren't concerned with ... cost/token", as if most people ("peasants") won't find it absolutely insane to be paying $200/mo for a subscription.
How could that be considered insane for a professional tool?
I've been telling people this for a couple of months now, I'm actually struggling to use up all my tokens.
But this is a new, different model that just came out.
Try adding a new comment
My favorite model just got better. Gotta love what deepseek is doing
Oh mince, mon immense meurtre des humains congois dans le fruit
Probably a week ago someone posted a thread with the title "Why isn't the industry freaking out about DeepSeek 4.1 Flash?"
I have been intrigued about Chinese models for a long time, but never tried anything except an early Qwen. My daily driver was Luna, with occasional elevation to Terra or Sol - all on 5.6 as I found the output of 6 jarringly incomprehensible.
I still had 95 USD on my OpenRouter account, so I thought I would give DeepSeek Flash 4.1 a try.
I think now I can understand the person who created that thread.
Here is the setup I settled on, after a lot of refactoring and calibration of the workflow:
I have a "plan" prompt which explains to the agent (Luna) that I want to have a discussion about an idea I have in mind. The agent should use the Socratic method to pull out all the necessary info from me using pointed questions. When the agent feels it has a complete picture, it should elevate to a higher level model (Sol), and ask its review. If Sol has complaints, Luna keeps asking me until the points raised by Sol are clarified. The review rounds with Sol continue until Sol says "ready to implement".
At this point Luna creates four files in a directory I specify:
1. PLAN.md: the implementation plan with all the details that came out from the design discussion 2. PHASES.md: implementation phases 3. DECISIONS.md: design decisions (this serves the same role as ADRs aka. architectural decision records) 4. DIARY.md: append-only log of what happened during implementation
Files 2-4 are written by the implementing agent. Luna only adds headers to explain the purpose of the files.
Once these files are there, I start up a new Pi session, switch to Deepseek 4.1 Flash and then point it to this plan folder.
And then it goes brrrrr... :-)
Here is what I implemented in the past week using this method:
1. A capture/analysis/decompilation pipeline for C64 demos: I ask the agent to start a selected demo through VICE, and say "on" or "off" when I want it to start/stop capture. The capture process uses VICE's builtin trace functionality: the trace log is piped into Clojure where it is saved into an optimized binary format (chunks of trace events). Once I have everything captured, I unleash my analysis pipeline which checks the write patterns of the executed code: what it wrote (VIC, SID, CIA, memory) and when (rasterline, cycle). Then it splits the execution trace into segments by type deduced from the write patterns (decruncher, loader, raster, music) and then decompiles each segment into both traditional ASM files and a s-exp based syntax which roundtrips back into the in-memory representation. Once I have the results of the analysis pipeline, I can tell the agent to analyze any demopart and it can explain how the effects are built down to the tiniest timing detail. As a first test, I asked it to create a clean-room implementation of Fairlight's legendary intro and two days later I had it, with everything in sexp assembly, including the music routine and its data. (The reason it took so long was that this was the first decomp and it provided a lot of feedback to the pipeline development.) Finally I had it write an assembler, linker and packager which creates a PRG from the assembled and linked artifact, then runs it through VICE. Done.
2. A DSP compiler which lets me compile sexp forms of a C-like DSP language into native DSP kernels running on the JVM, JS and WASM. Currently I have about 600 tests which verify that the behavior of the three targets matches that of a reference interpreter written in Clojure. I have a full numeric tower with i32, f32, f64, support for arrays and structs, typical control flow constructs, and a library of DSP building blocks like oscillators, filters, LFOs, delays and a chorus effect. By the time this is ready (end of the weekend?), I will be able to build synthesizers like Synth1, Surge XT or Vital in Clojure/Script, compile the DSP kernels in the browser, choosing between JS or WASM backends, driving the kernels via AudioWorklets and sequencing them from the host language.
There are two power tools at play here:
1. Deepseek Flash 4.1 2. Clojure and ClojureScript
Deepseek is a BEAST. It just goes on and delivers, phase after phase. What it needs: clear instructions - this is covered by the planning process quite well, but I still find it needs some steering from time to time (the right nudget of wisdom dropped into its thought stream at the right moment works wonders)
And Clojure... well. That article PG wrote about LISP (Beating the averages): what I witness now proves all of those claims.
The agent LOVES Clojure. Especially the REPL (not so much the parentheses).
Winning strategy: give it two tools: start_jvm and clojure_eval. Implement everything else it may need - tools to start up HTTP servers, compile ClojureScript, add dependencies, capture a C64 demo, run Playwright via spel, etc. - as lightweight Clojure namespaces which provide APIs to all these functionality. Let these namespaces be thin: the namespace and the functions therein shall have docstrings which tell the agent what the functions do, but the functions themselves shall be lightweight proxies to the real implementations in sub-namespaces. Then point the agent to these top-level API namespaces via your AGENTS.md. Bam, instant discoverability with minimal context usage. It will do EVERYTHING through clojure_eval and the round-trip time will be phenomenal, a perfect match to the raw speed of the agent.
...
Now all I need to figure out is what will I do when all my dreams become manifest in flesh.
Pretty cool, thanks for sharing.
Isn't there a lot of overlap between PLAN, PHASES and DECISION that pollutes the context ? Whereas a simple PLAN.MD to put the harness on track and an ARCHITECTURE as a single source of truth to prevent it go offtrack would suffice and gives it more space for higher level of reasoning ?
These files came to be after I've seen Deepseek create them for itself to track progress.
Then I turned what Deepseek did into an upfront policy.
This seems to work quite well generally: watch the agent as it works, and think what could be provided to make its life as easy as possible
Have you thoroughly tested a slightly simpler workflow with one less stage but a frontier model doing the initial planning? Luna is amazing but it's a deliberate trade off designed for speed and cost savings. You can also give them a rule about speaking clearly.
Performance charts and benchmarks are here:
https://www.reddit.com/r/DeepSeek/comments/1x84wke/deepseek_...