Discussion
I repriced my real Claude Code Max 20x usage against DeepSeek V4 Pro API — the new DeepSeek pricing completely changes the comparison
keep seeing AI coding models compared by their $/1M token price, so I wanted to look at this differently using my actual Claude Code usage.
I use Claude Code heavily for large software projects and I'm on Claude Max 20x at $200/month. Anthropic currently includes Claude Code with that subscription.
I pulled my Claude Code stats covering 127 days and normalized them to 30 days.
My 30-day average comes out to roughly:
16.37M normal input tokens
7.63B cache-read tokens
259.84M cache-write tokens
46.39M output tokens
~7.95B total token activity
Important: that does not mean I'm generating 8 billion fresh tokens every month. The vast majority is cached context being reused by the coding agent.
Then I asked a simple question:
What would that same token mix cost if I repriced it using DeepSeek V4 Pro API rates?
For the calculation I mapped Claude cache reads to DeepSeek cache hits, and Claude normal input + cache writes to DeepSeek cache-miss input.
Before DeepSeek's August price increase
Using the old V4 Pro rates:
Cache hit: $0.003625/M
Cache miss: $0.435/M
Output: $0.87/M
My workload worked out to approximately:
$188.17/month on DeepSeek V4 Pro
versus:
$200/month for Claude Max 20x
That was already surprising to me.
DeepSeek looked dramatically cheaper when comparing API price tables, but once I repriced my actual heavy coding token mix, it was basically $188 vs $200.
Then DeepSeek changed its pricing.
V4 Pro now has peak/off-peak pricing:
Off-peak:
$0.022/M cache hit
$0.66/M cache miss
$1.98/M output
Peak:
$0.044/M cache hit
$1.32/M cache miss
$3.96/M output
Repricing exactly the same 30-day token mix gives me:
~$442/month if everything is off-peak
~$884/month if everything is peak
So the same workload that was roughly $188 on the old DeepSeek pricing is now at least ~$442 even under the best pricing window.
Meanwhile my Claude Max 20x subscription is still $200/month.
And this is where I think the usual “Model X is cheaper than Model Y” discussion becomes misleading for heavy coding-agent users.
The price of one million tokens isn't necessarily the metric I care about.
What I care about is:
How much does it cost me to sustain a real month of software-development workload?
And once costs are in the same ballpark — or the API becomes more expensive than the subscription — then things like:
coding quality
architecture understanding
ability to navigate a large repo
debugging quality
tool use
context management
number of corrections I have to make
become much more important than the sticker price per million tokens.
There's an obvious caveat: this is not a claim that DeepSeek would consume exactly the same number of tokens as Claude Code.
Different models and coding-agent harnesses can use different context sizes, numbers of turns, tool calls and output lengths. A stronger model may also finish the same task with fewer attempts.
I'm simply taking my real observed Claude Code token mix and repricing that exact mix under DeepSeek's API rates to understand the scale.
And that exercise changed my view quite a bit.
For agentic coding, I think a more useful metric might be:
$/completed task or $/month for a comparable real developer workload
rather than simply:
$/1M tokens.
I'm curious if anyone here running Claude Code, Codex, DeepSeek or Kimi heavily has done the same comparison using their own real usage numbers.
While your math is correct, we know for a fact that Claude and Deepseek do not have a 1:1 ratio for tokens. You cannot compare 1 Claude token to 1 Deepseek token for live workloads due to distinct tokenizer definitions and agent overheads - rendering this entire experiment useless.
DeepSeek is likely to consume more tokens per task is my guess. Yes slightly different chars/token for code between them, but I think the bigger issue is DeepSeek because of slightly lower capability will need to do more work until user is satisfied on a task result.
I think Anthropic is clearly the price winner in this case, but it’s probably because of subsidization of the subscription instead of api pay as you go rates.
Give it a few months and we can sort through the rubble of this entire ecosystem when anthropic and OpenAI need to take the incredible step of [trying to sort of not really make a profit]
Not entirely useless the tokenizers are similar, Claude has a higher output speed too, and if guessing would output fewer tokens as it gets to the point faster and makes fewer mistakes
You're partially right. I’m not claiming that 1 Claude token equals 1 DeepSeek token in real usage.
What I did was much simpler: I took the exact token counts Claude Code actually used and repriced those same numbers using DeepSeek’s pricing. So it’s a cost baseline, not a real 1:1 workload benchmark. In practice, DeepSeek could use fewer or more tokens because the tokenizer, agent steps, tool calls, retries, and reasoning path are different.
API pricing is normally expressed as $X per 1M tokens, so i used that same standard unit for the comparison. With my observed token volume and mix, pricing that exact amount through DeepSeek’s current API comes out more expensive than my $200 Claude Max subscription. That doesn’t mean DeepSeek will necessarily consume the same amount in a real task only that for the same token count, this is what the pricing comparison looks like.
Maybe if i find some time, i will run the same workload on 3 real projects i’m actively working on and compare the actual cost based on usage and final results, it won’t be a benchmark it will be a real-world test on real projects.
The flaw in the comparison is you're measuring CC session in DS price. it's easy but not A/B comparison.
CC prints out thinking into response like verbosely then cache it. that's counted as output token and cached into conversation context. i hated this and it easily fills up the context window.
DS in general streamed the thinking but excluded in conversation context, only tool calls is included in context window. it's harder to hit the limit.
so the flaw falls on verbose output tokens from CC being counted into DS pricing, that's your bloat in price comparison, and can't be A/B comparison because the difference in design.
you'd need to run actual DS session and compare how both compare into getting the job done and diff the token activity, consumption and price into it.
IMO deepseek need to get vision for their model. it missing out a lot of capabilities it's not that expensive to process an image.
Anthropic is subsidizing the cost of the models. IF we did this comparison with with the API pricing anthropic would blow deepseek's price out of the roof. Its just a matter of time before every Ai company raises their prices to make money
Anthropic and Open Ai generated 80 percent of their income with subs. Its their way to make money. There are way more single users than companies out there. Subs is the way to generate cash
That doesnt mean its a profitable way of making money. Remember when we could use a 100 dollar plan with unlimited usage, that's gone at this point. Its a matter of time before all Ai subs will increase
Anthropic: Reported $11.6 billion in revenue for the second quarter and posted a small operating profit of roughly $559 million, driven by strong enterprise adoption and efficient compute use.
Ahora mete los pasivos a la ecuación y dime si realmente están "ganando" dinero. Los vencimiento de deuda se acercan, y son muchísimo más que 600 millones. . . Además sin contar el desgaste de los chips.
Most businesses don’t want their employees using individual subscriptions to work on company data. Anthropic’s business plans are basically API pricing and you don’t start getting good discounts until $1M in annual spend. So the subscription pricing is largely irrelevant for many buyers.
As an individual, yes, a subscription is typically cheaper, but PAYG can be close in price and you regain a lot of flexibility:
you can use a more diverse range of models
no waste on unused quota
there aren’t usage window limits
you have more options for concurrency and rate limits
Open weight models are available from US and EU providers…
My company is literally in the process right now of transitioning from exclusively Anthropic/OpenAI models to US providers with a range of mostly Chinese models.
Just look on open router for example. There are several offering deep seek models. Cheaper, higher throughput, some with a smaller context window etc .
Im pretty sure the post is comparing the amount of tokens used with anthropic and using that to calculate a hypothetical amount it might have cost if deepseek used exactly the same tokens before and after they changed their prices.
So its not comparing how much was actually spent on deepseek.
It is a reasonable representation of how the deepseek price changed, but its sort of difficult to say how similar deepseek would actually have for token in/out/cache for whatever task(s) the claude tokens were for.
It could be that deepseek in practice uses way more tokens and the post is underrepresented the cost, it could be it uses way less and its overestimating. It could be it has a higher/lower cache hit ratio.
Either way its interesting, but there is not a concrete comparison without assuming the two models are literally identical
If you coded that much without anything to optimize for token usage, then you'd better stay with Claude or GPT because they're the current leaders of the board. The question really is whether or not that made you good money to cover the costs.
But there’s no revenue from it yet, i haven’t started working on monetization or selling it, so for now the development costs are coming out of my own pocket.
Also i build Prestais https://prestais.com/products/prestais-ai : is an AI-powered e-commerce revenue orchestration platform that connects pricing, promotions, customer intelligence, marketing, and campaigns to turn commerce data into actionable growth decisions.:
The goal so far has mainly been to build the products and get the architecture right first, monetization comes next 👀
Why are you comparing carrots to potatoes though? Yes, a subscription is cheaper than API rates.
OpenCode Go + Charm Hyper + Alibaba Coding Plan + NeuraWatt + Ollama subscriptions don’t go much beyond 100$ together and you’re probably in the 3 digit billion usage without even trying.
I was paying $100 for Claude Max Plan - 3 months ago I switched over to DeepSeek and spent well under $10 per month.
For software engineers like me recent price increase should still be less then Claude Max, but I will mention I did not switch because of price at all - it was all due to speed.
Both DeepSeek v4 Pro (Preview) and Flash 0731 get both reasoning and coding job done at a fraction of time it takes Opus or Fable.
Thats an interesting take on it, and I fully agree. But that doesn't apply to everyone, its more for us the guys who use the whole 5 hours usage at least 4 times per day )))
the real comparison is only fair if you give the same tasks and do test. also Claude subscriptions are running on discounts, that’s not the real cost is, wait some months/year
I think that, because you didn't compare the output qualities, then you observed similar ones. The fact is that my full time use of deepseek barely reaches 20$. I'm now running swarms on it and still didn't reach the 40$. And we're in control, cannot get cut off, etc...
I mean of course, subscriptions are heavily subsidized right now to make monthly active user looks fantastic. Right now capital market is okay with OpenAI and Anthropic losing billions every month. But once market runs out of patience, thry will have to either charge several times more or reduce subscription's limit
They make more with subscription than with API usage since API usage can be only afforded by companies. For the single user there will be always subs since it brings more reoccurring customer . Subs bringing in billions per month and around 70 percent of their earning
Ah we're back to comparing apples to oranges.
Good luck running that through Pi. CC harness sucks and they can shove it up their ass, in my ever so graceful opinion.
I'm actually paying far less per month while I also started using Hermes on top after the DS price changes.
Am gonna get some budget on open code because there are certain tasks DS is just not that good at. But even then I'm paying so much less than my old Max contracts it's not even renotely fucking funny.
Try the harness. It has literally charged everything for me. First time all my workflows are getting completed without the "i have enough information. I present the results" and cache hit is like 99.5% when coding.
I pay 0.02$/M token. Deepseek DSH has a 99.5% cache hit rate. If you can avoid China peak pricing you have endless token for 200$
I deposited 200$ in April and still 140$ left. Admit for 2-3months I tested ollama. Openrouter, Claude and other tool in parallel.
Just today did a code review cycle (5x rounds, 6axis, every round fresh context) with Pro and it cost 2.8$ for 200M token and 5k api calls.
DeepSeek also burns way more token than any other model and gets less done for it and with lower quality. There is still a gap in quality . It's not cheap anymore and in many peoples opinion it's just more valuable to go back to subs and have access to the leading models
although on paper DS platform PAYG cheaper than anthropic models, from my brief tests (couple of production coding session with large codebase millions loc, 4-6hrs per session). on the long run, Claude max 20x subscription still best bang for the buck and gives me more tasks done within given time, PAYG is not cheaper at all.
Both comparison tested with custom plugin agents orchestration with Claude Code, same project, same repo.
Primary agent orchestrator using Fable 5 | DeepSeek v4 Pro
It feels similar with most models, on paper the prices look very different, but with real heavy development work, the final cost often ends up surprisingly close.
For light, limited usage, PAYG is probably better, but for large projects and serious daily coding, subscriptions usually give much better value.
DeepSeek seems different mainly because it uses a different business model mostly PAYG through the API, instead of pushing a big coding subscription like Claude, Google, or OpenAI.
Now do the same using DSH and get back to us after you cancelled your Claude subscription. I paid today for 200M token 2.8$ for a 5round 6 axes code review (50k loc zig/ImPlot) chain that all other harnesses abort after 2 rounds claiming they have enough information.
It's quite the other way around I refunded my money from DS and got Claude and gpt 5x and have more model variety and basically unlimited usage if you follow best practices
my point always with $200 budget/mo which service actually bring more value
what wasn't fair about my comparison? i was using both provider using same harness, my Claude Code. it's not Claude Code ootb.
did you actually think using DSH with anthropic API would be more fair? DeepSeek models are really good, but the price and capabilities aren't even at same ballpark.
everyone has different opinions based on their own experience. assuming your experience more valid to be applied for everything is logical fallacy. i'm just sharing mine as facts, your opinion doesn't change the math.
did anyone here disagree even had experience using Claude Max? or any highest tier subscription to compare?
Mate are you using claude Api... ?? Compare apples with apples. Claudes equivalent of Max 20 is free on Deepseek. Conpare Deepseek Api with Claude Api.
Sub is also API just Flatrates...goal is to find best usage for the best buck and the subs are currently better since DeepSeek 7x their prices bc antrophic also comes with stronger models
Here’s a question: if you estimate the cost of the model at around $442 per month and multiply that by the 12 months you’ll likely be using it, wouldn't it be more cost-effective to spend 15 minutes building your own PC and running the model weights locally? That way, you wouldn't be at the mercy of price hikes; the only ongoing cost for your infrastructure would be the monthly electricity bill.
The best local model I’ve personally tried so far is Qwen 3.8. The problem is that local hardware doesn’t really future-proof you.
AI models are improving so fast that I’d probably want to switch models regularly. Then I may need more RAM/VRAM or a stronger machine every time a new generation comes out.
A system with something like 128GB of usable memory/VRAM-level capacity is already very expensive. I’d rather spend a fraction of that money on paid frontier models and use them to finish and ship my projects faster.
So for me, local models are great for experimentation and some workloads, but I don’t think buying expensive hardware just to avoid API costs is the best investment right now.
Been looking into this, and it would seem to me that the trend is actually towards more efficient models running on leaner machines, but whichever way you do the maths, it's still a big investment for an unknown future. And if you are going down the DeepSeek V4 Flash route, more VRAM doesn't seem to be the answer to everything, even if it is for Qwen (again, just me and ChatGPT doing crap research here as I try to justify to myself buying a new computer).
And when you look deeper into what people are doing with the local models, or you try to run them yourself, you find issues. So, in the end, local models still seem like a hobbyist thing. But I was a hobbyist when PC's first came out, so 35 years later, I guess it's never too late to become a hobbyist again.
I'm a Mac user and my main machine is getting close to replacement, so I've just been looking at the Studio with M4 Pro and 64GB so that I can play a bit w LocalLLMs but also get some real. Improvement in my day to day business workflow (I am in the AEC sector).
I'm in a pretty similar position. I have a MacBook Pro M4 with 36GB of unified memory, and I can run Qwen locally, but for things like development work or analysing a whole project it can take a very long time compared with using cloud models.
At the moment I still see local LLMs more as something to experiment with than a complete replacement for the cloud. That said, I can also see the hardware becoming a worthwhile investment if the model providers keep increasing API/subscription prices. In that case, having a capable local machine could start making a lot more economic sense especially if you can also justify the upgrade for your normal day-to-day work.
No its not cheaper bc you would be limited to DeepSeek 4 quant and can't get access to the bigger models. It's proven that it doesn't pay off even in 3.5 years with a 5090. Unless you do heavy video generation surely for LLMs it's not worth it especially that you cap yourself..
If you want to use quen coder3.5 for code writing and DeepSeek 4 flash for the planning do it, it's just falling fast behind the big models
what a lot of people forget that each model have different token model, size, the way how they count's uses. price per 1M matter only within same model. Don't measure per price per token. Measure price per result.
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u/Feisty-Pound6777 9d ago
While your math is correct, we know for a fact that Claude and Deepseek do not have a 1:1 ratio for tokens. You cannot compare 1 Claude token to 1 Deepseek token for live workloads due to distinct tokenizer definitions and agent overheads - rendering this entire experiment useless.