DS was serving the pro version at extremely low prices for a long time, and they've had integrations with opencode & other providers, so they likely gathered a lot of data from real developers doing real tasks (on openrouter they were labeled as such). Now they can use those live scenarios to further post-train their models and improve them further.
Can't wait to see if distilling k3 into dsv4 brings additional improvements. Anyway, having fast cheap models getting better is great for the community. Especially since these don't "go away" on a provider's whim. Whatever capabilities they get, can be used "forever" going forward. And, at least flash can be ran "at home" with <10k in hardware, which isn't really possible / feasible with glm/k3 larger models.
> Whatever capabilities they get, can be used "forever" going forward.
You can’t treat Qwen3.6 like its fable, but if you prompt precisely and specifically it’s a great executor.
I actually found it refreshing to use more of my brain for once, and actually have to think deeper about what I’m trying to do, and how to build it.
I try to keep changes under 1000 lines and drive architectural decisions myself, barely notice any difference compared to frontier models. The rest 10% is to spot bugs, security problems and to investigate better architecture, which flash can also do pretty well, I just cross check it.
Faster iterations are way better for me, I hate waiting for 5-10 minutes on small changes. I tried to use recent versions of Kimi and GLM, but they use too much thinking for no reason and are pretty slow because of it. I also often feed a lot of data to it, without worrying about hitting the limits: dependencies (to find bottlenecks in them), logs, performance dumps and so on.
Also, it will never complain about security guards, I've been using it to reverse engineer binaries.
Maybe I'm using too weak language in my prompts, but none of the OpenAI models I've used via codex has refused to reverse engineer binaries, is it supposed to? I'm sitting right now reverse-engineering a 3rd party firmware together with Codex and haven't hit a single guardrail. Meanwhile, I see people complaining about it rejecting non-security related prompts, are things so individual on the platforms right now or what's going on?
Thanks.
The idea was that I could continue to use the application in the future and export the new data, not that I would be able to recover the extent data already in there.
I use OpenCode mostly (uses fewer tokens than Claude Code) and I am looking forward to the release of DeepSeek’s own coding harness.
It's replaced the Kimi models for me though.
Availability and rate limiting can be an issue, though. I’ve found that constraining the providers works well to solve that though.
- Cost: $4.55USD
- API requests: 3,467
- Tokens: 323,183,886
And as an engineer who leads a small team, I have very high standards for quality, and these carry across to my personal projects where I use deepseek. It has not disappointed at all for coding or review tasks. For everything else, use another model.
I'm using them for my micro-saas, and they have made my niche economically profitable where as SOTA models are only slightly better for massively increased expense. Its truly impressive.
Word of advice to anyone, not all your use of LLM tech needs to be code/dev work related.
We are entering 'Web 4.0 era' or whatever you want to call it. Massive transformations of nearly every single business will and are being developed as the cost of intelligence as a commodity is falling through the floor...
I've had worse experiences doing it because the quality of answer has been quite bad, and I'm wondering if my methods are the reason.
Review is a skill, as in, a SKILL.md with a folder full of references:
- SKILL.md: https://gist.github.com/lionkor/161525be858d1d75db4c13c0f093...
- references/output-contract.md: https://gist.github.com/lionkor/8c68e33becef7a21f8408c7dc119...
- references/review-lenses.md: https://gist.github.com/lionkor/0a8b080fe45306213efddf3ebb75...
- references/review-workflow.md: https://gist.github.com/lionkor/d2d374b133ceb7e3660bd530ee72...
- references/section-rules.md: https://gist.github.com/lionkor/8a9e503adc7fd3697410cf021f27...
I'm aware that almost all of this is prompt voodoo, and there's no guarantee for the review to find anything or everything, but making it a dedicated skill and thoroughly observing the output thinking, tool calls, and result, lets me adjust these over time and fill the weak spots with even more prompting.
I use this skill by simply telling the agent something like "Review the changes on the current branch against origin/main, take special care with backwards-incompatible changes to the public API" or something like that.
I use `pi` (pi.dev) with a subagents extension, so that I can ask the agent to invoke a subagent to do the review, on work that the agent did.
For models, I use the highest possible reasoning on whatever model I feel like makes sense, usually this is GPT-5.5 or deepseek flash/pro, depending on the confidentiality of the codebase, on the highest reasoning always (for reviews).
I've also had success with a review checklist, though it doesn't produce an easy to parse (for humans) output: https://gist.github.com/lionkor/054ac2cf241e0765eee2383f0dba...
This is why my review skill mandates a very strict output contract. I need the output to be very easy to parse, and the output contract I've specified there does that.
In general I let <whatever the latest model of OpenAI's ChatGPT is> author and review SKILL.md and similar large prompts, usually with a ruleset like this, which is a 1600 line research artifact from a long GPT 5.5 "Pro" research session on prompt engineering: https://gist.github.com/lionkor/71498794d0a7d72173fc58766f25...
Does the review catch all issues? Not at all. Does it catch, usually more than one, important issue, across large changesets? Absolutely, and that's the point! :)
Feel free to ask me any questions, I'm also happy to share more about my setup via email or add you or anyone else to my private repos with more of these.
The main difference is that deepseek is bad at prose, and Codex models are much more eager to use tools provided to them (which is usually fine, they often make like 3 todos via tool calls for a simple one step task which is silly though), and deepseek in general benefits from good instructions more than GPT models maybe do.
Deepseek becomes much better if you give it tools for asking clarifying questions, doing self-review with subagents, and so on. The more tools it uses, the better the signal-to-noise ratio in its context, and the more consistent the output.
You must still review 100% of the code as if it's trying to sell you insurance.
Then you, dakolli - out of generosity and minute-to-minute devotion to enlightenment - sacrifice time from your busy day to sit down (though perhaps that's been painful lately?) or stand up with your phone - and offer a profound, deeply thought-out counterpoint in the following form (and I'll paraphrase):
"Nah mate, it's shit. All LLMs are shit."
Well, I guess having "Two PhDs, Three masters degrees. Expert in everything.." in your bio makes you very smart!
Edit: And like a lot of tools, HOW you use them is just about as important as the quality of the tool itself. Of course the tools can produce massive amounts of bad quality slop, they can also produce fast, focused edits that make sense.
Usually unreviewed code only gets committed if I really don't care, like for one-off scripts, which I sandbox with github.com/lionkor/sbh or run as an unprivileged user.
For any work with protected intellectual property, I use other providers, for the contractual guarantees, but I think it would be silly to think that OpenAI or Anthropic are not training on literally all data they get. How could you ever tell if they did? They can just claim the data was mislabelled, or ignore the accusations. If you have serious IP, use only local models.
(TBH though, in my multi-subagent workflow I do use other, more expensive models for planning, reviewing, oracle-ing)
I haven't used our slow opus subscription for weeks.
(Also set up an OpenWebUi self-hosted chat that works from my phone, has some mcp and skills. fully replaced perplexity. Monthly cost ~$18 for hosting and subscriptions)
https://github.com/gitsense/pi-brains/tree/staging
The README is being worked on but the three videos should give you a good sense of what it can do. Pi is also what makes what I will demo in
https://github.com/gitsense/chat/tree/update-readme
possible. Since Pi exposes so much, it is very easy to build advanced tooling around it to help easily grok hundreds of tool calls to help you understand what they agent knows and what it has tried.
To be more constructive, what I did (as an experiencd SWE but a complete noob to agentic coding): went to pi.dev's extension marketplace and looked into all the new shiny stuff. Subagents, mcps, context and memory optimizers, skills. Using the most popular ones (not necessarily the best ones)
It was like 15years ago learning the new mindset of vim (and spending a ton of time to customize it to my workflow). My understanding is that Claude and opencode doesn't give you this flexibility.
Learning all these stuff drove me to also set up openwebui, and it was such a successful private project that I implemented it at work (with jira/confluence/bazel query access) and management said "we need this by tomorrow".
I believe the models matter not that much anymore. The "harness" does. (unless you just want to vibe code. Thebn, throw crap at fable and call it a day)
Lots of sensible defaults and good tweaks/settings you just don't worry about.
Last night it single handedly implemented a feature after a grilling session, and came back with the red-yellow-green risk assessment points that I mostly saw with anthropic models. I had to check if I'm using the right model, but it was DS4Flash.
So maybe I was using it already?
If you are directly using it through Deepseek, they do mention that the flash slug has migrated over
Crazy.
* DS4 Flash: 82.7
* GPT 5.6 Luna: 75.7
For reference, that puts it on the third spot behind GPT 5.5 and Fable 5. For some reason GPT 5.6 Sol is not showing in the leaderboard. If it did, then DS4 Flash was number four.
The thing is, even if Luna is better in DeepSWE and has the 80% discount. DeepSeek is still cheaper.
--------------
DeepSeek V4 | Flash GPT-5.6 Luna (New)
--------------
Input (Cache Hit) $0.0028 $0.02
Input (Cache Miss) $0.14 $0.20
Output $0.28 $1.20
--------------
Both Luna and Flash are heavy on the reasoning > output. And the cache hitrate + prices also matter.
Reality is, you can not go wrong with Luna or Flash at those prices. And remember, DeepSeek V4 Pro is still in the rafters, what is ironically closer to Luna's new price.
In terms of open research, China has absolutely overtaken the US.
US or American, don't trust anyone, these are open weight models. Host them yourself if you feel strongly about privacy (as you should). I honestly don't know of any OSS open weight models from the US labs as good as Kimi K3 or Deepseek V4 though.
They release the models back for free.
Undermining out entire economy by subsidizing the release of DIY versions of our main economic drive sounds like a huge win for China.
Yes this is why I referred to 'months' and was downvoted by people who can't distinguish their politics from reality. You are restating exactly the text you are criticizing.
This is the nature of mechanical parrotlike repetition of propaganda:
a) You can have 'frontier' models with closed weights. Your sentence is basically a contraction within itself, again, the weight of ideology.
b) No one actually knows whether or not the closed weights of Bytedance are beyond all existing frontiers. Again the weight of ideology blinds you to the fact that the real 800 lb gorilla of Chinese Ai is more closed than Anthropic.
The parent made a totally coherent commment that China closing off their open weight models would cede the frontier (and majority of AI users worldwide) to the USA. Nothing you said counters that point.
1. Selling access to the US? 2. Ship more and better software? 3. Talk about it publicly?
If you had a secret AI better than anything currently available the mere mention of this would crater the US tech investment sentiment.
They could even host these Chinese models through AWS and sell at-cost inference on Bedrock and obliterate the US model companies.
In fact the overwhelming weight of AI use in China, the chatgpt so to say, is Bytedance's AI which is absolutely closed and uniquely opaque.
The press treatment of these matters was no good and they are slowly walking it back, e.g. NYT yesterday finally actually read the speech.
> We should put in place laws and regulations, technological monitoring, early warning and emergency response systems in order to strengthen the line of security, prevent abuses and malicious use, and ensure that AI is always under human control. In the meantime, we should jointly oppose overstretching the national security concept in the field of AI and placing one country’s security over that of others.
There was a blog post by a state-linked broadcaster cited in the NYT piece:
> “China supports openness, but this does not mean it advocates for the unconditional proliferation of all capabilities,” the blog said.
The article also quoted an American journalist:
> “If these models do reach those dangerous capabilities, they are not going to let it be a free-for-all in terms of releasing them," Mr. Sheehan said.
But a distinction has to be drawn between real dangers (which many people believe LLMs have not actually shown, to date) versus "dangers" hyped up marketing purposes or domestic regulatory-capture motives. Presumably the Chinese government is less interested in the latter.
It is just a question of being unaffected by the motives of the speakers, which is what adults learn to do.
The person I am replying to actually believes that Xi actually spoke of "open weights", for example. This is the AI information space we live in.
But the speech doesn't say that? I'm looking at the full text. For example:
> We should take seriously the various types of inherent and secondary risks that AI may trigger.
The risk portion of the speech is focused more on application-level risks than model capabilities. Which is a much more sensible regulatory framing than what Silicon Valley has proposed.
The administration blather about banning open weights is characteristically confused. Xi has already stated (what is obvious) that he will ban security-endangering weights and keep them a state secret.
I’m curious to see why you’re so eager to ban open-source LLMs while being okay with Anthropic’s control. Do you think they’ll rebel against you?
You could have said the same of linux - that it extinguished proprietary OSs at least for server usecases.
It's so good that the US government is rushing to ban all Chinese models as fast as they can.
The interest around K3 is a lot more defensible because we actually know what the architecture looks like, how much effort it takes to get it to run, and what kinds of results it gets on cyber evaluations. And no, it's nowhere near "dangerous" enough to where people might honestly want to ban it for real safety reasons. It does a good enough job at fixing cyber issues, but that's hardly a safety concern.
Sarcasm aside - if the community can't get their shit together to continue improving what is currently public, well - we don't deserve free stuff and open weights.
If those numbers translate well to its general capabilities, with the great caching DeepSeek has, I feel like this model will get tons of usage.
Does this make sense?
Should be extending the lead in intelligence/cost index, as deepseek-v4-flash already were the most price efficient model, which now becomes even better. Although, in the deepseek APIs, the cost is leaking all information about codebases to China.
I find the newest OpenAI and Anthropic models to be way better for big tasks that require many decisions but I don't like that anyway because I lose track of what's being done.
Knowing what I want for every prompt makes DeepSeek Pro the best LLM for me. It allows me to work relatively fast at a very low price.
I didn't do much agent coding and had a mix experience.
1. It would build something that was in the spirit of what I wanted, but unusable in practice.
2. It would build something quite useful, but only the public APIs were nice, the deeper code layers would get more and more convoluted.
3. It would built what I wanted and it would have okay-ish code.
However, for 3. I also had to add a custom AGENTS.md, many more code example, extra repos as subtrees, and review any code that had new concepts.
Much more work, but still much less than typing it all by hand.
I admire DeepSeek's openness, but even they have been raising prices after their discounts.
As for vision yeah it sucks but Luna is also 2x input and 1.5x output for 1M context...
That's around 0.4 in/1.8 out
DSv4 is wayyy cheaper.
And it's open now you have Luna at home if you have a decent set of GPUs you can run this on 2Sparks or one very expensive Mac or just like 6-8 5090s..
I guess using a ZDR provider is good enough for now.
V4 flash cache read is $0.0028 per mtok
That's not "a bit cheaper", just saying
The leaked interview has him saying it doesn't matter... as much as open source doesn't matter. There's enough in it for everyone right now and they aren't after everything.
Perspective: DeepSeek doesn't have enough infrastructure to serve their target customers already.
And maybe that's not a bad thing tbh because look at the state of the US right now
That's partly. I'd say the other part is it's an open secret they have "illegal" Nvidia GPUs and other "secrets". There's a common understanding not talk about these things because it'd hurt everyone collectively.
- OpenCode for codebase editing: python scientific computing and LLM projects
- Open Interpreter Classic (python version) for Swiss army knife terminal replacement one-off task type stuff.
Rust works perfectly fine, but when I use Rust, I usually pay closer attention to performance, so I have to guide it a bit, to improve cache locality, use simd, avoid unnecessary allocations and so on. Terra has the same issues with Rust. If you always prompt models to achieve the best performance, the code is usually no the one that I want, they optimize unnecessary/cold parts or blindly optimize stuff where compiler takes care of the optimizations already.
See my other comment for more information on how I work with it. In short, keep the changes under 1k lines, context under 120k (ask it to use subagents), drive the architecture yourself.
I tried using it to generate some rust code yesterday, and it generated much code but only ever came within 1 error of a testable build. The 4th or 5th full rewrite is sitting in the buffer right now.
I'm currently looking to up my game with Bottlecap AI's return of qwen3.6, 'thinking cap'.
Alleged to be twice as fast and superior at coding over extended sessions (vs. 3.6).
We'll soon see.
c#, TypeScript, PHP, SQL, CSS, HTML.
also features, tests, fixes, refactoring and planning.
it's super fast, smart and dirt cheap.
I got super great results working this way. Maybe there is a better way to integrate the two modes though. But my first real project was end-to-end 100% working correctly from the first run - about 10,000 lines (including tests) of greenfield code. I did have it work in manageable chunks that I could easily review - not one-shotting the whole thing.
Now I'm thinking about plugging Deepseek into Codex to be the coding model and see how it goes.
I use Opus/Sol with for /brainstorming, deepseek (on Pi) for /subagent-driven-development
I love it, the docs are easily editable and when I'm ready Deepseek is faster/cheaper then everything on my coding plans.
Excuse
"Too simple to test"
Reality
Simple code breaks. Test takes 30 seconds.Although it's funny that I am thinking about that at all because I have a 2060 :P . My local inference is playing with Gemma 4 E2B and MiniCPM 5 1B.
No mention of weights, just API. When will the updated weights be released?
https://openrouter.ai/rankings?view=day#leaderboard-table
These days cost per task is more important, and SOTA models have become expensive.
These massive jumps in cheap models, is really great times!
I'm still considering pulling the trigger on the annual subscription of Kimi for K3 but it's sometimes slower than I'd like (at least when compared to Anthropic) even on their Vivace plan, and the token limits on the GLM Coding subscription for GLM 5.2 were too easy to hit.
Most of the times, the total cost, including search API's, is less than $0.05 for full deeply researched output, and the research is actually good.
At the speed Flash has on most providers, it doesn't really turn into a latency concern.
QA analysis of voice transcriptions. Napkin math: we operate at 2-5% of the cost of running on Equiv Frontier, though this changes near-weekly because pricing is so volatile.
It took us about a month to get the inference configured to achieve these numbers. But if you can get your hands on a pair of B300 GPUs and the context works, it's untouchable for price/performance.
(B200 would work, but you don't have the B300's memory, which lets you run it on 2xGPU instead of 4xGPU... with Dspark, it's like magic)
On a side note, for tasks that don't require the intelligence of DS v4 flash, we're using Nemotron-3-super with incredible success. I'm shocked we're not seeing more adoption of this model, given how easy it is to fine-tune and how blisteringly fast the nvfp4 version is. (A single B200 GPU can produce an insane amount of throughput with Nemotron 3 Super.)
Probably not.
But Opencode-Go is a great solution for those who don't want to pay DeepSeek directly (or can't due to reasons)
Selfish referral code: https://opencode.ai/go?ref=R1AJZT4VBX
Otherwise, if you're trying to run it locally, even really low quantizations like DeepSeek-V4-Flash-IQ2XXS-w2Q2K-AProjQ8-SExpQ8-OutQ8-chat-v2-imatrix seem to actually not be so dumb compared to smaller models with same quantization, might be worth a try if you're sitting on a lot of RAM/VRAM yet not industry-scale amount :)
3rd party providers on OpenRouter can be cheaper but it's already so cheap.
https://opencode.ai/docs/go/#usage-limits
Previously, OpenCode Go had higher API prices for some models, but now they lowered the API price and simultaneously reduced the allowance.
GPT 5.6 Luna is a new model in Go since I last checked, for example.
I still find this today:
> The plan is designed primarily for international users and provides stable global access. Your data will not be used for model training.
> because we added the new deepseek which we do not yet have a ZDR with we cannot blanket say we offer ZDR
I wonder how the website can make the statement that data will not be used for training.
Played around for a few hours and used up 80 cents of tokens.
Providers' cache hit stats are available to consult, and only 1-2 of them behave properly if I remember correctly, zero if you request providers that don't store and train on sessions.
Why not call it V4.1?
edit: nope, at least deepseek kept "deepseek-v4-flash" and just updated model underneath. I guess preview is no longer worth serving with that release and you'd have to look through inference provider docs to see if they've updated, yeah..
The `-0731` style suffix is worse compared to a proper version bump like V4.1.
https://news.ycombinator.com/item?id=49082022#49087112
How is that important? Maybe it does, so what?
Some providers like OpenRouter now call it `deepseek-v4-flash-0731`, but even in places like here on HackerNews people say things like "Sonnet is better than DeepSeek" without specifying a version or a reasoning effort, certainly no one will mention that `-0731` suffix when talking about DeepSeek V4 Flash.
The only issue i see, is 3th party providers that have not yet updated. But that is going to be a short time periode. There is no reason to not update.
Dual Strix Halo is much slower and current Macs with 256GB are both slower and more expensive (Mac Studio M3 Ultra 256GB around 12000€).
To get something faster than the two Sparks you'd need to spend more than $22000 for a server with 2x RTX Pro 6000 at $10000 each.
Beyond that you could get 2x AMD MI350P.
The max version I could order now with 128 GB?
If so, the price for local inference would be 12 000 € vs 500 000 € for a B300.
You can also do 2x 6kPRO in a workstation, for ~20k.
But still, even for mid level projects API is orders of magnitude cheaper, since you don't need to set it up and maintain it.
The 4 bit quant technically fits (there's a 127 GB version) but ... obviously that's not going to work. It is so close though, surely someone will a way to do it.
> Trains: this provider may use prompts for training and may retain prompt data.
https://github.com/antirez/ds4 is often mentioned for DS4F on Mac, but 64GB is likely not enough to achieve reasonable speeds (official weights should be ~160GB).
https://x.com/SourceCodeplz/status/2083099712760987746
i prefer GPT-5.6 Luna honestly
• Terminal Bench: 56.9 → 82.7 (+25.8)
• Toolathlon: 51.8 → 70.3 (+18.5)
Compared to GPT-5.6 Terra:
• Terminal Bench: Flash 82.7 vs Terra 78.4
• Toolathlon: Flash 70.3 vs Terra 53.1
• DeepSWE: Flash 54.4 vs Terra 69.6
• Agents' Last Exam: Flash 25.2 vs Terra 50.4
Trading blows with Terra, which is pretty interesting. No clear winner on these benchmarks, and wildy differeing scores. Very interesting!
Terra 87.4
> 78.4
The real score is always the official benchmark.
We need to see later if DS4 flash 0731 is going to maintain the score but we need to look at the official benchmarks.
Already seen a PR for DeepSWE to update the benchmark with 0731, so we can verify claimed vs official.
They're literally comparing the previous version of the same model with the new one. It's based on the same architecture, same pre-trained model, just different post-training. It doesn't get more apples to apples than this.
The performance changes are so big with the right harness that is makes sense to engineer the harness and fine-tune the model to one another from the start.
Wow that's crazy
Good times for those that don't need strict data protection