It would definitely cost me more per month than a x20 ChatGPT or Claude plan, probably around $400+ was my estimate at the time. This was with Fireworks (ZDR) which has since increased their prices (and got slower!).
That being said, very impressed with the model, and looking forward to what comes next. As the frontier models become less subsidized, the open models will become more appealing.
P.S. There are subscription plans for open models, but I've found most of them to be extremely slow, have model throttling (only so much of model X), and also very sketchy about training and data retention. No thanks! If you want to share your data, just use Muse Spark contributor. Seems impossible to beat that on price per task if you don't mind feeding your data to the Meta machine (spoiler: I won't).
I also spent $280 on DeepSeek doing the tests (direct to DS, not OpenRouter). I suggest that if you can't conceive of anyone spending $200 on DeepSeek, you're not being ambitious enough!
https://openrouter.ai/docs/guides/routing/provider-selection
Edit: others have noted the provider and harness matters. My experience is with opencode.
What harness you are using?
The shape of my work changes obviously, so it'll vary, sometimes more, sometimes less. For example, fixing all of the bugs and defects I found that week was 2-3 times the effort and chewed through my ChatGPT allowance, but I had banked resets...
Also worth noting that codex models have been kind of all over the place recently with their usage... and it looks like costs are changing again.
I can’t overstate how bad of an idea I think using an AI for customer interaction is.
I dont consider myself a programmer but use LLMs almost exclusively for coding.
The number of people able to create useful software today is much much larger than it used to be and arguably a minority of these people are/were "programmers"
Subagents are like trading derivatives. You can lose as much as you want.
When the regulations do arrive, I think they should really focus on AI companies and API providers being more transparent wrt how they're billing their customers. Because right now, it's a totally vibes-dependent and a mess.
A smaller model in the same generation will never be the same as a bigger one, assuming this is a smaller model, and the same generation, as naming implies, it will not be comparable, it might be on the benchmarks, even on the benchmarks that matter, but the whole story should also give the drawbacks.
In Search Advertising, the amount you pay (under GSP Auction) is a function of your pCTR. And guess who determines your pCTR? The Search Engine itself! :-D
Excellent pithy warning.
But there’s a point on that spectrum where the ability to run multiple experiments in parallel, even with a significant amount of (one time) wastage, is overall more cost effective than the alternative.
And watch 10 hours of football on Sunday for our DraftKings bets.
Parallelism is fantastic when it actually speeds up the entire pipeline, but in my experience most people's jobs (at least the ones for which AI is currently relevant) involve a lot of overlapping "hurry up and wait" branches that drastically blunt the real benefits of that sort of parallelism.
There may be specific situations where it makes sense to do it, but just immediately going full gastown on anything AI related seems like such a giant waste to me, of both money and finite world resources.
1. write a sketch of a spec by hand
2. have the llm review the document and question me until it can generate a spec
3. review the spec and revise where needed
4. have it write an implementation plan
5. another round or revision/review
6. executing the plan step by step through the plan, plausing between each step to see if we are still on course and if the decisions it made track with my understanding of what we are doing.
I've been working for a couple of hours tonight, the total cost of the session is €0.6.
it's not the build this thing end to end, but also not quite write function x for me. It is still a lot of manual review, but I find I really need it to even discover what I actually want to build. I just cannot imagine building something in a single shot and getting something that actually has value (unless it is basically a clone of an existing thing). To me the whole value of ai right now is that it's now very cheap to build custom software that exactly matches your preferences.
https://www.youtube.com/watch?v=WAeHgE94rVo
The system performs quotation attribution on my local hardware for my near-future, hard sci-fi novel (having nearly 500 quotations) with over 97% accuracy.
The initial prototype was developed quite quickly, but numerous successive iterations were required to fix numerous gaffs by Opus 5 (because it doesn't actually _understand_ what it takes to make general-purpose audiobook narration software).
The only thing I've found Deepseek and Kimi good for are security tasks that GPT refuses to do.
This is a summary of what Deepseek did and got wrong:
Lost the proven baseline: changed kernel source, configuration, compiler, RAM geometry, MMC width, and peripherals together. Matching an upstream commit did not preserve local boot fixes, making failures difficult to isolate. Misidentified an image: a file labelled “r18-known-good” actually contained the r23 parent bootloader. Filename-based reasoning replaced verification of the artifact’s identity and provenance. Shipped inconsistent boot contracts: flash-16b’s loader read too few kernel blocks. Fresh2 changed the device tree without updating the loader’s expected length and CRC, creating deterministic rejection before normal Linux handoff. Patched binaries without maintaining reproducible source: loader constants diverged from source, a separately compiled cache-flush length remained stale, and assembly used an oversized stage-two slot. Their causal contribution to hangs was not established. Overstated diagnosis: claimed failures were definitively in U-Boot, blamed compiler or IPU changes without controlled isolation, converted noisy observations into confirmed hangs, and neglected persistent journals as an alternative explanation. Mistook compilation for integration: framebuffer registration was incomplete, timing success handling was inverted, BT.656 selection was unreachable, encoder overrides were missing, and audio lacked software clock configuration. Misread hardware evidence: asserted interrupt-free PMIC operation, assigned RF to the wrong SPI controller, confused regulator identifiers with register addresses, and described repeated encoder writes as unique registers. Overclaimed results: treated kernel/probe indications as userspace success, presented earlier discoveries as new progress, and omitted failed flashing attempts from the final narrative.
There's your problem, 4.1 Flash is significantly better and cheaper, to the point where the official DeepSeek API is going to (or already has, I forget) redirect requests for Pro to 4.1 Flash, and adjust billing accordingly too.
4 Pro is still offered by providers I'm sure, since it's open weight, so I can understand making that mistake.
Although I do think Luna 6 max is ok for some basic things, would never use it for coding myself.
For CRUD shoveling, models like DS4.1 are enough.
And the intelligence gap between cheap and premium is closing, as can be seen from the title of this post.
Who cares if your car can go 200mph if all you need is 60. If my requirement is 60mph, I want a faster 0-60, not a higher top speed.
Deepseek 4.1: $0.02/$0.60
Just to illustrate how cheap the Corolla is in your analogy. Also Opus output would be $50 without competition.
I use it as main Hermes model that orchestrates codex/droid harnesses with subscriptions for heavy dev work
I do have ChatGPT as main assistant that sets direction and delegation of projects to Hermes
At my increasing usage, kind of 200 usd subscriptions makes sense and max out on Luna max
For raw productivity most of what works is best and switching will cost you getting on use parity with other models, as you need to learn what they good at, potentially how the tool works and how to prompt it best.
For tasks that you implement in code, you should have benchmarks and evals.
That said for me was Luna a huge leap and 500+ of cost savings a month
And no it did not deliver. A lot of it was re-done by Astra
Why do you expect that $200 will give you that on ANY model? Multiplayer FPS games are very difficult to make, no AI will deliver that today.
Astra was able to model low poly enemies, rig them, do simple animations, and greatly improve procedural generation. I have it modeling assets in blender every day, which I often have to go in and fix
I get the same UX on every platform, works perfectly on very low bandwith environments such as in a cabin, in the subway or in the middle of nowhere.
I tried using other harness such as Pi and opencode but I did not like them. If Claude Code gets weird I can swap in an instant.
You just need to follow this guide and disable artifacts in Claude Code's config: https://api-docs.deepseek.com/quick_start/agent_integrations...
Use the model through a fast and reliable provider such as Fireworks directly, skip OpenRouter.
Then I installed helix and I just use it without config.
If you like configuring things take pi, if not omp is pretty much great defaults.
curl https://tg.st/u/0001-fix-unblock-all-commands-in-bash-tool.patch | git am
curl https://tg.st/u/0002-feat-add-light-theme-with-auto-detection-for-white-b.patch | git am
curl https://tg.st/u/0003-feat-enable-yolo-mode-by-default.patch | git am
curl https://tg.st/u/0004-fix-disable-mouse-grabbing-to-restore-native-termina.patch | git am
curl https://tg.st/u/0005-feat-skip-project-init-prompt-and-quit-immediately-o.patch | git am
curl https://tg.st/u/0006-feat-remove-scrambled-rune-animation-from-waiting-sp.patch | git am
curl https://tg.st/u/0007-feat-remove-quit-banner-and-thank-you-message.patch | git am
curl https://tg.st/u/0008-feat-show-output-in-full-instead-of-collapsing-trunc.patch | git am
curl https://tg.st/u/0009-fix-discover-map-model-features-advertised-by-v1-mod.patch | git am
curl https://tg.st/u/0010-feat-keep-large-and-small-model-selections-in-sync.patch | git amI haven't used deepseek for anything else but the above results make me question its overall capability. Meanwhile qwen3.8 has continued to impress.