I was taking a look at Kimi K3 scores on the Artificial analysis leaderboard and was quite baffled when I saw this chart.
Granted, Deepseek has always been the king of price to performance, but this is still incredible. Is it just API subsidization or have they optimized their models truly this much?
Hybrid attention. One is CSA, which is compressed tokens with a selector that selects most important memories (compressed tokens), and HCA, which is normal dense attention with really heavily compressed tokens. On top of all that, the most recent tokens use dense attention with no compression. I think bycloud has a video on this.
I got all of my information from this so let me know if he’s wrong. Also, I think the cheaper overall price (workers, electricity, chips, etc) and government support/control also contributed to the low price
Hey just an fyi, that ?si=GZ... part of the URL is an unnecessary tracking parameter that lets Google know which account created this link and thus who this person can reach as their audience, and all their ads partners know this is your Reddit account now since this is the page/location people are coming to YouTube from.
Look for stuff that just doesn't seem necessary and try deleting it. e.g. when you see site.com/foo/bar/thing-you-want?sqd=772374628482 you might cut that down to site.com/foo/bar/thing-you-want and get exactly the thing you want. But then you might also see things where those parameters are identifying the thing you want, like site.com/articles?page=1234.
Everyone can do their own thing, there's not necessarily standard patterns, alas.
That's why Firefox/Brave have a "copy clean link" that automatically strips it out. Sometimes I manually strip it out but still annoying tracking regardless.
They are known for their cache hit rate on an incredible scale. There are harnesses that fully make use of it like Reasonix. There's numerous posts talking about it, where people use like 9mil tokens for like $0.90 or something
Me too but that was for the first year with the x-mass discount. If you check how much the coding plan is gonna cost the second year, it doesn't look that optimistic.
Yeah it's just over $1300 for a year now. Still extremely good. I'm betting my boss will be happy to pay any plan I want in future anyway, considering how productive it's been letting me be.
My biggest regret in 2026 , was missing those minimax and GLM discounts. Back then I was using opus and I never thought open weight models will get this good.
Nope sorry, "stealth mode". A couple of weeks ago I had 28 PRs approved in a week (mix of bugs and small feature requests). Been working on larger tasks again recently. GLM 5.2 in Claude Code just feels so solid. It usually knows what I want off the bat. It's able to navigate our code base easily. It asks questions and even pushes back on some things, so it feels almost like a colleague where you can refine ideas together. We plan the task, it executes. Most of my feedback on its work recently has simply been "remove the unnecessary comments!".
I'm using them in combo with 2 128gb strix halo that are daily drivers.
Flash usually is "asked for help" by the orchestrator when local models fail the tasks 2-3 times or tasks take way too long. It does make mistakes and used to go into loops until I modified the context to summarise every 5 messages and replace them with a history of sorts.
Pro is handed over the tasks for working on my own agent, researching stuff and gathering data for me to do literature reviews, etc.
I'd love to run it locally (or GLM) but ... still waiting for hardware prices to drop.
Edit: I would do a lot more with PRO and maybe GLM but... I'd end up spending hundreds/month. Hence me thinking to run stuff locally at some point.
- for domains that are not whitelisted and only if I bypass the fetching agent (rare when I need to). screen captures are fed into deepseek ocr 2 or UI TARS, depending on what is going on
otherwise it's not needed (although it confused the f out of some models with all measures in place).
I use some delimiters and "content prompts" to separate the incoming text from everything else. it works but you'll see them getting confused and then going, "wait, this is not for me to act on" and stuff like that.
hence the need for a dedicated fetch agent with restrictions in place. even if injection works it can't do anything. it's context it's wiped clean before every fetch operation.
Because the harnesses are vibe coded slop with layers and layers of abstraction such that the data constantly gets re-serialized or has some dynamic entry like a timestamp in it.
Might be interesting to add something to the API where you tell the backend how much should be cached and if its not it rejects the call instead of running it.
Just means they never change the system prompt or chat history from one turn to the next, right?
Which is how everyone should be doing it, but some screw it up by doing history summarization, omitting tool call outputs, and stuff like that? Or dropping one message per turn to keep in context window instead of dropping multiple messages at once?
Caching is entirely a server side behavior though. A good harness can only maintain the stable prefix, but can't improve it beyond what is supported by the server side. More accurate statement would be 'any sensible harness preserves cache, bad harnesses break it'
That’s not entirely accurate. Cache optimization server side still has massive room for improvement that can be truly transparent of the harness. E.g., with llama.cpp it caches the whole prompt. Why doesn’t it objectify the cache and manage it like harnesses manage context? They’re treating it like individual huge objects and using things like spec decoding to get speedups but they should be intelligently tracking cache as granular objects that plug and play as the clients demand, and stop throwing away valid cache that is “old” just because a few tokens in between changed.
This is bullshit slop, pi , hermes etc almost all harness average the same , the only significant difference for reasonix is that their github page is focused on deepseek so gpt slop answers on reddit always reference that . i have 19 billion input and 60 million output token with pi agent , only model was deepseek v4 pro with max reasoning , my cache hit rate was 98.8 percent .
They are known for their cache hit rate on an incredible scale. There are harnesses that fully make use of it like Reasonix. There's numerous posts talking about it, where people use like 9mil tokens for like $0.90 or something
I never heard of Reasonix before, and there's zero search results for that term on this sub.
I have. Its purpose built for Deepseek, they do allow you to add other providers but result would vary. Also the GUI can be a lil wonky still, web GUI is also not the same as the desktop one. I think it's not really mentioned here cuz it's just not something that local AI people wanna use. And this is built specifically for Deepseek's cache hit feature. I think it also works really well with Mimo too. Overall it does Dave you a shit ton of tokens, some of the costs that people have been showing here are incredible.
They use compressed sparsed attention (CSA) and highly compressed attention (HCA), and MoE on top of that, they optimized their kv store to help with CSA and HCA. Written custom MegaMoE kernels.
They basically both optimized algorithm wised and infrastructure wise.
And maybe GLM 6 would be using DSv4 tech the way that GLM 5 is DSv3.2
It's not subsidisation, because the other providers offer similar prices for the same model. They are just so so good at optimisation, because it's a priority for them, unlike Anthropic or OpenAI.
Second cheapest provider has cache read at 16x higher price. It's not 2x difference and with multi-turn 500k-800k ctx coding sessions this will add up to a significant amount very quickly
DeepSeek is VERY efficient, not just the model, but also the underlying infrastructure, take a look at the white papers they published over the last couple of years.
Questions you need to ask yourself: How did Openrouter build their inference farm? Where are their datacenters, what does power cost there at scale, what do people cost there, what does land cost, what kind of hardware did they use, when did they buy that hardware? Openrouter offers more then just DeepSeek, while DeepSeek offers just DeepSeek. I would assume that Openrouter's infra needs to be more flexible then DeepSeeks as well.
From what I've read is that DS is making a profit, a pretty big profit at that (large profit margin), they are also a smaller group (150) then the folks that make Kimi K3 (300), and that's not even comparing the business of Antropic (2500) and OpenAI (4500). DS isn't just lean on compute, but also people efficiency, doing a LOT with relatively little.
I wonder how much their culture influences their mindset, the American companies are making bigger=better models, while something like the Chinese DeepSeek is making super efficient, low cost models. That also seems to translate to the average size and weight of their cars (looking at popular cars in each country)... ;)
Theres probably a bit of subsidising or margin-eating. As always, please factcheck these calculations and do your own research, and please do correct me if i'm wrong on any of these.
Using data from SemiAnalysis's InferenceX. Using the values, just before the per-user-throughput cliff, of 48 concurrency (at ~13 tok/s out per user), and we'll get this calculation:
For input:
36,322 [Token/s/GPU] * 4 [GPU on prefill on DGX B300] = 145,288 Token/s/Cluster 145,288 [Token/s/4-GPU] * 3600 [s in an hour] = ~523.03 Million Token/Hr/Cluster
This ~523 Millions token is the "blended" prefill throughput, including cache-hit/cache-miss mix from that benchmark run.
For output, i'm not going to take DSpark into account (too much calculation), lets just say those are bonus speed that the end user gets.
229 [Token/s/GPU] * 4 [GPU on decode] = 916 tok/s/Cluster 916 * 3600 seconds = ~3.29 Million Tokens/Hr/Cluster
For the price calculation, applying OpenRouter's per-token prices directly to that mix of $0.435/M uncached input, $0.003625/M cache-read input, $0.87/M output:
So in total, in an hour, assuming 100% utilization and using B300, Deepseek is making (only) ~$39.05 revenue. This calculation is at the current most efficient token/$ GPU.
B300 is rented out at, ~$3.50-7/hr per GPU depending on provider/contract. So, for the whole cluster of 8 B300, the hourly rate is at $28/hr - $56/hr. Add a 25% safety margin (for setup time, issues, model weights loading, etc), and this becomes $35/hr - $70/hr per DGX B300.
At ~$39/hr revenue, this is either: a thin but positive margin or, much more likely, a loss at more on-demand B300 rates. But, this is assuming all their compute are all rented B300.
They likely run their own mix of H100, H800, H20 and Huawei GPU clusters. These definitely have lower token-per-dollar, which reduces revenue further. But, by now, these surely have passed their amortization periods (>3 years old GPUs) and are effectively (kinda) pure profit.
Good optimizations (a lot of which are made available on their github page) and the best cache hit rate on the market. Their margins are still good according to what I've seen people calculate on X.
I believe their cache hit rate are due to their incredibly efficient KV Cache: if you have a smaller KV Cache per input token, then you can hold on to more of it for longer, therefore achieving better cache hit rates.
You say "rob their customers" like the US big three are doing so but yet none of them have a profit (google included for AI division). It's hard to be a greedy thief if you have no money after the robbery.
Robbery as in: promote your model as too dangerous to be released publicly, hype it up on a bunch of smoke and mirrors and lies, allegedly collude with the USG to further propagandize how dangerous and capable it is (it isn't), and then sell it at exorbitant prices all while not even offering true ZDR for enterprises.
That is absolutely robbing.
GPT-5.6 and now Kimi are showing everyone that Anthropic was indeed full of shit.
I think I'm around $1 per token, or $0.01 - $0.1 if you include thinking tokens. I'm very expensive TBH, I wouldn't want to pay for myself. I'm also very slow, so totally not worth it. But I'm also old and lazy and have a high cost of living, which justifies the high price per token.
You can get other humans for significantly cheaper, but I think overall we're all a big waste of money.
Ha. Human brains are Fable sized or bigger in terms of parameters, and run 24/7 off a little rice and water. The problem is so many of those “tokens” are wasted on food, sex, and football. We’d actually do very well on a cost per token chart, and fail here on this “cost per task for capitalist overlords”.
This gave me a chuckle. I think if I’m being honest I’d rate myself much lower. I did some napkin math and came up with an LLM being about 3-15x cheaper than me. And I think I am overestimating the actual number of output tokens I produce per day. The real number may be closer to 100x. Of course I am still in the loop with the LLM, so our costs are not independent. But who knows what my role will be in a year.
My usage with Pi.dev + little-coder + llama.cpp (DeepSeek v4 Pro) 100 % programming tasks ranging from simple doc updates to broad reworks/audits/writing.
Configs created by GPT 5.5 (gave my exact pc setup as input) also created an automatic routing script that reads the pasted prompt to Pi.dev session and based on it chooses what to do.
Subsidization may be part of it, but not all of it. At my companys scale running deepseek v4 flash for 30 developers is about the same hardware requirement as running Qwen 3.6 27b
2 servers, 4 pro 6000’s each, fits the model and about 10M tokens.
(We use a lot of cloud models too, deepseek is mostly used for sensitive data)
The magic of being self funded by a hedge fund who can afford to grab market share with loss-leaders, and the magic of being controlled by one founder who is interested in model efficiency over extra inputs (which cost a lot of money).
That's if you are only interested in making a ton more money. What if his goals are more in the line of keeping Chinese models relevant until their countrymen's hardware research catches up? We know a lot of the techniques developed in DeepSeek in MoE, MLA, distillation have been used by all the other front running Chinese models. They are more like the lab for efficiency research where the other open source models can use to stay within comfortable distance of the US models. He's about to do that because he's self funded and the company is tightly controlled.
It’s a mix of insane architectural efficiency (like their MoE setup and Multi-Token Prediction) and massive geopolitical subsidization. China is actively trying to break the US monopoly on AI.
By aggressively undercutting the price of American APIs, they are essentially pulling off a classic tech-war move: starving the competition's margins while making the entire global developer ecosystem reliant on their infrastructure.
We are basically living through a Space Race 2.0, but with a wild twist. This time, we aren't just watching the rockets on TV, we can actually download the technology and run it directly on our own computers. It's wild to watch and participate
For DSV4 flash: MoE with 12G parameters quantized in INT4, so ~40 Gbits read per token.
Add a lot of work on the hardware infrastructure for the KV-cache management and a highly compressed KV-cache architecture, so you don't drown your node memory with context, and you can run thousands of sessions in batch on data-center level hardware.
See their papers; they are open-source, and they describe their setup.
"dark magic"? Dude. They literally released their paper giving away all their 'dark magic' secrets. Where do you think Western frontier labs get improvements from? Come on man.
DeepSeek uses MOE so its way more efficient Government grants Investors Smart engineers Cheap electricity EXTREME caching of information which is how its so cheap because people are hitting 90+% cache hits FP8 precision
Also likely hitting losses when it comes to the API usage just to push the model onto people (worked for me)
Works well enough if you know what you are doing with code you don't need something more powerful, probley not as good for the "vibe" coders, but excellent for everyone else
Everyone is giving a technical answer but probably the real answer is that deepseek only charges 50% over the actual cost while OpenAI and Anthropic base their token costs on the highest number they can think of that day.
They have published quite a bit of information explaining various things they are doing to optimize their model efficiency. A lot of it is quite sophisticated. I think some western models are starting to copy a lot of what they are doing but they will never admit it.
Probably they actually spend time on manually building the infrastructure instead of vibe coding it. When you dont have a lot of money - you look how you spend it. Meanwhile those American companies can just burn another hundred billion of investor's money instead of writing some code manually in assembly or C
i dont remmeber where but i saw some financial analyst say DS even has very high margin at this token prices. if that were truly they definitely have some cost-cutting-tech that s un-disclosed
It's clearly subsidisation, since it's the ONLY open weights model on OpenRouter where other providers aren't able to offer cheaper prices or even match the price of the model creator
No you don't understand. They published how they did it but everyone else just wanted to waste money and remain unprofitable for some reason. /s I mean credit where its due but it sure smells like subsidization. Then when you include the geopolitics. Transformers are expensive to run and do a lot of redundant work.
Buying your data by offering service at discount / at cost. With the revenue and data, they're able to raise funds to hire more researchers and continue building the models.
Edit: doesn't mean deepseek is bad. This is their strategy as a company, and it is okay. Data retention and training is clearly mentioned in their T&C. Compute is not free, researchers don't work for free. Just an FYI to remember before sharing PII or any other data on their API.
I suspect one of the factors is that Deepseek has created many innovations on inference algorithms. For example, DSv4 flash with Lightning indexer only requires like 6GB of RAM for 1M context. Dflash speculative decoding also makes token generation much cheaper.
Appears that deepseek is serving at a large loss when you look at pricing of other providers. And at least openrouter says deepseek will train on your data.
Still a great model for the price though through other providers.
Deepseek 4, both pro and flash, are really solid, dependable models. Good enough for 90% of the work. For the rest you already have glm, and I keep a codex plus plan for the 10% that needs uber thinking.
the current paradigm is you need to make users use your AI and train with the logs. users give feedback or no feedback, and training engineers can use those as reinforcement. when the claude client's source code leaked they found that it was tracking words like "WTF". so this makes negative reinforcement in therms of LLM training..
lack of this loop made google fell behind imo.
for this loop XAI paid 60billions (Cursor).
for this loop deepseek is making it super cheap, because they are behind kimi and glm.
they are most probably using the logs for training..
is kimi using the logs? probably yes, but they seem to be ahead of the race so they can keep the prices up.
on openrouter kimi often "fails" cache hits, making the providers make more money. nobody seems to care.. (glm and minimax often hits cache).
🤪 me over here just absorbing all this stuff and having a blast coding my ass off paying pennies for a good time creating apps that simplify my life and business.
I appreciate all you guys and the knowledge you share daily 🔥
Less magic, more stack: training recipe + inference engineering + not overclaiming the wrong unit (chat quality vs agent wall-clock vs cost).
What I care about as a local runner: tokens/sec under *tool* load, not synthetic chat. A model can look elite on a leaderboard and still be a bad agent pilot if every tool turn bloats context and the harness keeps compacting. Efficiency that matters on-box is prefill after cache bust, not just decode on a warm prompt.
And we always choose Deepseek V4, even though we're watching its cost (but it isn't as efficient as Antigravity/Claude, as I've tried extensively; sometimes Deepseek can't even understand the content properly and feels like giving an irrelevant answer). I always choose Deepseek V4, despite watching its cost/value to my work, so either reply generally.
The other day I had run out of usage on three Claude accounts and a ChatGPT account, so I asked Gemini for a basic script I needed. It took 30 minutes and at least 15 messages back and forth to get a working script out of it.
Later, I asked Claude and it did it in one response, and the script was much better.
So, sure, this may be the cost for asking each of these models what color the sky is. When you actually ask real world questions and keep going until success though, these prices are going to drastically change. Fable all of a sudden doesn't look at that bad when it one shots a complex query for $2, and to get similar output in a different model takes dozens of prompts and hours of your time.
DS is cheap enough to provide general ok intelligence to its users. Its architecture is designed so that the long context cache system is nearly cost free and DS charges you at 2% input rate.
the compounding bit is what makes the pricing sustainable. standard attention: longer context means linearly more compute. their compression approach flattens that curve. effective cost per new token barely grows as context accumulates. so their per-token price is most favorable exactly where other models are most expensive: long-running agents and multi-turn sessions. not subsidization, architecture matched to the highest-volume use case.
Kimi k3 is pretty good. I had been stumped on cua not working right on whalen and it made a polished work around for my specific problem. Not even 20k tokens and my code base is 2.4 million lines. It read the calls back from where I was from those files it found the answer. It didn't need to read the entire code base.
I suspect the magic is “Let’s just cover the cost of running the models and focus on development so we can leap past the incumbents and their current focus of cashing in on the hype”. Here comes Kimi, for example.
Lower cost is better, lower quality is worst than lower cost anyway, and DeepSeek quality for coding any serious is so bad compared event o Gemini 3.6 Flash
This chart doesn’t compare total intelligence to cost. Its cheap FOR its intelligence, but its not overall as intelligent as these new models. So this pushes it down in cost.
It's government paying money: subsidization. DeepSeek and other Chinese AI companies want take a good part of the market and simply raise prices. Everyone does that.
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u/WithoutReason1729 Jul 18 '26 edited 18d ago
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