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
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.
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 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.
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.”
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.
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 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.
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.
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."
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.
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.
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.
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.
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 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.)
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.
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.
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.
Honestly given how good the open weight models are getting I don’t see a viable business model for OpenAI and Anthropic anymore. The revenue 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.
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 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.
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.
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".
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.
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?
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?
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).
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.
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!
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.
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.
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.
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.
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?
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.
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.
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.
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 ?
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
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.
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.
DS is miles better than GLM in this regard. I wanted to add that as PC is planning to go to GLM next.
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
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.
I love COT and gives me a good idea about the thought process. Sure it doesn’t work always
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.
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.
https://ourworldindata.org/grapher/human-rights-index-vdem
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.
[0] https://openrouter.ai/deepseek/deepseek-v4.1-flash
(The badges next to companies indicate their privacy terms, i.e. green/yellow/red)
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...
Plus only one country has threatened to annex mine and it wasn't China.
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.
https://www.edenai.co/
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
If so OpenCode Go looks good and includes DeepSeek models (yet to try myself).
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 is very capable for a lot of tasks, not only coding like the other open 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.
Wait wut? You do realize that they are two different companies, right?
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.
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.)
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.
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.
note that the volume of EU and US support to Ukraine is obviously calibrated to the same goal.
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.
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.
Definitely an improvement on deepseek-v4-flash-0731: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
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.
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.
This is why using multiple model families is important. Don't limit yourself by being a stan for this or that company.
This read like a conversation you were having in your head -- unfiltered.
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.
How?
This is how one of my claude code session looks like right now, one prompt from yesterday:
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.
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.
I think the tests problem is really having an impact for me. Hadn't considered it before so thanks for flagging.
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”.
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
Awfully new old friend.
What exactly are they doing for you that's giving you $200 of value in such a short timeframe?
- 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?
There's no reason to blow through tokens this way, there's a 99% chance nobody uses the output (except maybe themselves).
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.
That’s the “how” we’re wondering about.
I am not saying OP is not telling the truth. I just really want to understand!
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.
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.
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.
> 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.
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 $20USD subscription.
It’s paid for itself though, at least from my side business income.
It is like putting a jet engine on a shipping container and wondering why it needs to be constantly kept in afterburner to at least scrape on the road while smarter users put wheels on the shipping container.
- Reasoning effort = 1 is now actually usable and produces 2.5x less tokens than old weights, without intelligence lost.
- Overall 20% less tokens at each reasoning effort.
- Same intelligence and same tool-calling pattern.
More details in blog post https://sference.com/blog/deepseek-v41f-1017-benchmark
I didn't try the last flash model because I would need to use SSD streaming (I have 128 GB), but the benches for this release are so compelling (and it using less tokens to get tasks done) it might be worth the throughput hit. I will probably give it a try when I have some downtime.
It feels very freeing to be doing useful work on a local model on my own hardware with my own coding harness. The TCO argument is starting to look compelling too.
Lovely to see they're doing their own search though, I love how many folk are actively working on search clones at the moment.
Right now I use each of them in their own respective coding harnesses, deepseek-harness and kimi-code, but I do wonder what they are like inside something like opencode.
The speed and cost/intelligence ratio is just outrageous.
At the beginning of the month I realized I had more tokens and more capabilities on Opencode at 8$/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.
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.
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
https://www.reddit.com/r/DeepSeek/comments/1x84wke/deepseek_...