Yeah no, I can’t use it like this lol. Same kind of usage used to cost me like 20 cents tops. Guess I’m back to looking for other options

I was scared to even touch it all afternoon, just waiting for my GPT Plus to reset tomorrow

We ran a beta of a new AI inference pricing model last week, and our post here kind of blew up:

https://www.reddit.com/r/DeepSeek/s/eFUlYOMpqS

There was a lot of interest, but also a lot of questions, doubts, and confusion because we didn't explain it well. So this is the follow-up that clears it all up, and we're opening slots for the next beta.

The one-liner: for ~$0.20/hr, you get a dedicated lane on a GPU running the full-weight DeepSeek V4 Flash 0731 — not a quant — for one hour. Your own guaranteed slice, no shared rate limits.

Before you start doing the math, let me lay some groundwork.

Right now you have two ways to run inference -

1. Pay-per-token APIs

Fine until you're a heavy user — then it gets expensive fast, and DeepSeek's price hike made it worse. If you're spending $100+/mo on tokens, you're exactly who this is for.

2. Host on your own GPU

What most big teams do — full privacy, zero data retention, and once your workload is big enough, the monthly GPU cost beats per-token pricing.

But for solo builders and small teams this is a dead end: GPUs start around $12–30/hr and rack up $7k+/mo, and you'll never keep one saturated. You're paying for a whole GPU to use a sliver of it.

So we're building the middle ground: Shared Reserved Inference

We host the model, 30–60 people split the GPU cost for an hour, and each person gets a dedicated lane on it.

You get self-hosted-style dedicated inference for a fraction of the price — without renting the whole box.

And like self-hosting: we log zero prompts and zero completions. Only aggregate metrics like latency, throughput, tokens, and cache-hit rate. Your code never leaves your session.

The numbers

Full breakdown: https://www.singularityapi.dev/benchmark

From our last live run, on a lane costing $0.20/user/hr (rough estimate — don't hold me to the exact figure):

97% cache-hit rate under real agentic coding load

Each lane pushed 14M input tokens, 97% cached, and hundreds of thousands of output tokens in the hour

That worked out to 1.7×–3.4× the token value you'd get spending the same on DeepSeek, from off-peak to peak pricing

And we only ran the node at 30% capacity — there was a lot of headroom left

Clearing up the confusion from last time

1. On the tok/s numbers

The per-second figures we quote are floors — measured with everyone hammering the node at the exact same time.

Real agent sessions interleave: different prompts, different timing, tool calls, waiting, etc. So in practice your effective throughput runs ~2–4× above the floor.

The floor is the worst case, not the normal case.

2. It only works on fully reserved, saturated GPUs

That means you reserve your hour in advance. If there's no node slot available in your timezone, we simply can't offer the lane.

This isn't an always-on API.

3. It's for focused coding, not agent swarms

You get 1–2 concurrent requests + a few in-flight — plenty for a normal coding session with a subagent or two.

If you're running 5+ subagents hammering the API at once, this is not for you.

4. It's a fixed hourly reservation — for now

You book a lane for a full hour.

If your session runs 40 minutes, you still reserve and pay for the hour. If it runs 1h20, you book a second hour.

That's the tradeoff of a guaranteed reserved lane today.

As demand grows and our node occupancy fills out, we want to move toward pay-for-what-you-use — billed for the 20 or 40 minutes you're actually on the lane — but that's down the road, not now.

It's also why we're being picky about matching beta slots to when you'll actually use them.

We're opening the next beta — free

A free 1-hour run, 64 seats.

You get a key + base URL, point your tools — Claude Code, opencode, Cline, Cursor, or direct API — at it, and code on your real project.

Pick a slot that fits your timezone:

Landing page: https://www.singularityapi.dev/beta

Signup form (60 sec): https://tally.so/r/EkoJkN

Benchmark: https://www.singularityapi.dev/benchmark

Now hammer me with questions — ask away.

So this is the end, huh?

I have a basic Kimi sub and it got absolutely decimated over the last 30 to 60 days because of the K3 release. In February, I could spam K2.5 without any issues. Now you can't constantly spam K2.7, or God forbid, even look at K3.

I've been using DeepSeek for tasks that don't require a high level of reasoning. Life was good, but this price increase is pretty substantial. Now I can't abuse DeepSeek for all my subagents. I need to be really careful and actually think about what I'm doing.

I thought to myself, "No worries, I can just use Luna through a basic OpenAI Plus subscription for my workhorse needs." Whoever came up with this? Sir, I hope you get diarrhea. Not only do I feel dirty for using an OpenAI subscription, but as far as I can see, codex with its limits isn't it. I can't spam Luna as much as I want.

Look, I can afford high prices for LLM inference. I'm just pointing out that everything is crashing and burning right now. Literally everywhere you look, subscriptions are getting downgraded.

Getting spanked by Moonshot, DeepSeek, and now OpenAI back to back was not an experience I'd like to ever repeat.

At this point, I think I'll just bite the bullet and allocate a certain budget for the DeepSeek API and try to optimize everything to a reasonable degree.

Good thing I know what I'm doing and can survive on low cost Luna or DeepSeek V4 Flash. I have no idea what these vibe coders are going to do, because smart models are getting ludicrously expensive, and somehow I feel like subscriptions will just keep getting worse and worse.

I asked Deepseek v4 Flash and Opus 5 to generate a trading strategy inside a trading harness. Both received the same prompt and both had access to the same trading knowledge hub.

This experiment interested me because Opus 5 is one of the most expensive models on the market, whilst Deepseek is the cheapest of the high-performing ones.

Opus 5 costs roughly $25 per million tokens. Deepseek charges $0.15. That makes Opus more than 160 times as expensive.

The full comparison at a glance — Deepseek V4 Flash against Opus 5.

What Is the AI-Backbone Trading Harness?

The AI-Backbone Trading Harness is the environment in which models such as Opus 5 or Deepseek are executed and fed with feedback on the performance of the strategies they generate.

Months ago I started thinking about how to help the models and steer them in the right direction, so that they build strategies that actually hold up. In this article I use the system I built around the models. It does three things:

• Execution compiler: the functional correctness of a strategy is verified immediately after the build. The model receives instant feedback on what to fix.
• Immediate feedback: backtests and logs. The model learns how to restructure the strategy when there are deadlocks or errors in its logic.
• Conceptual feedback: a knowledge hub holding a scientific collection of the best trading strategies, proven over years and confirmed by reputable sources.

The harness: the knowledge hub goes in at the front, and two feedback loops run back into the model before the optimiser ever sees the strategy.

The First Draft

Opus 5 produced this on the first attempt:

Opus 5 — the first guess draft, before any optimisation.

The first draft from Deepseek did not trade at all. 😁 No positions opened. Nothing.

But we are not finished yet.

Optimisation

This is the crucial step. In the prompt I asked for parameters so that the strategy could be optimised, and that matters more than it might appear. The models genuinely have no feel for trading or for the way market conditions need to be handled. So we keep the options open and search for a configuration that works well on gold, XAUUSD.

Deepseek v4 Flash

Opus 5

>Worth noting: optimising the Deepseek strategy took twice as long. The low price per token therefore buys you a longer wait during the building process.

Performance Comparison

Equity Curve

Deepseek V4 Flash

Note how both strategies maintain small losses and larger gains.

Opus 5 — the optimised equity curve.

Statistics

Deepseek V4 Flash

Opus 5

PnL Histogram

Deepseek V4 Flash

Opus 5

Holding Time

Deepseek V4 Flash

Opus 5

Summary

Both models were given the same task: design and build a trend reversal strategy. Opus 5 managed to keep its losses small in relation to its wins. The best strategy from Deepseek V4 Flash ended up with a moderate drawdown and a noticeably higher number of trades.

What I find remarkable is that Opus 5 found a way to keep the losses small whilst letting the runners grow. That is something only the most experienced traders manage to achieve in their careers.

How to Choose

• If you want peak performance on the spot, Opus 5 is the right choice.
• If you have time and you are exploring ideas and strategies, Deepseek will serve you better. It is also a very good way to get a feel for how the AI-Backbone trading harness works.

The honest answer to the question we started with: no, paying 160 times less does not cost you 160 times the performance. It costs you patience.

Want to become independent from back box EAs? Discover how this AI can build professional strategies for you too.

>Note: The harness from ai-backbone.com was used to generate the strategy and all the reporting figures.

This is my experience (VS Code BYOK), you may agree or disagree. Each is good in one type of task. If I know exactly what must be done and it is an isolated small task, I would go with Luna. If I want to work on a feature level or higher, with just a broader description, I would go with DS Flash. Luna averages around 86-87% cache hit rate with copilot sub, 92-93% and occasional high hits 96-98% with opencode/openrouter on non-coding tasks. DS Flash is consistently hits above 96%.

Luna is like the colleague who is really efficient in implementing as long as you put a lot of effort in getting your design and vision into their mind, handhold a bit and comes back too soon with a half-ass work. 

DS Flash is like the smart colleague who can fill the gaps as long as you provide a broad overview of what you want, reasons on their own terms, takes more time on tasks than you would prefer and gets it done with minimal corrections required in a second round. 

Luna is better for if you are handholding it, like a senior dealing with a junior in a team and comes back quicker. DS Flash is better if you want it to write code on its own and you want to be merely a system architect, code reviewer and approver.

In my experience, Luna 1M does not give enough weightage to previous requests - as if its attention span is too low for any long context work. It predicts, speculates instead of reasons and find evidence.

is it command code goat, cline pass, or opencode go, i saw the opencode go thing, i cancelled the subscription but really want to use deepseek models, or is the api pricing better

cache rate is same for both , 99% approx!
but if we talk about harness smartness which one is better?

I had a GitHub CoPilot Pro license for a long time for $10 per month and this worked fine. Then all of a sudden the price went up in April and I had to make a new plan.

I then tried ChapGPT Plus for month for $20 per month, which was more than I really wanted to spend, but it's all I could find at that point. I was using it mainly for writing code for my personal side projects and some work projects (but I'm not a software developer at work).

I found Opencode Go and that sort of worked for a while even though I had to use OpenCode Zen with a $10 credit limit. Around this time I found Hermes and started implementing it with my work and personal stuff.

Now DeepSeek came out with new pricing and forced Opencode's hand and now I'm back to where I was with GitHub CoPilot, but a lot more dependant on AI because now I use Hermes too. And I feel like it took some time to get Hermes to the point where it is now.

I feel like we'll keep going through this cycle until we eventually realise that AI costs are too high or you just make peace with it and pay $20+ per month.

I don't spend a lot of money on subscriptions. I have Netflix and Google One in addition to OpenCode Go.

So what now? Quit? Use Mimo. Make peace and go with ChatGPT Plus until I find something else? I don't want to try Command Code because it just sounds like another Opencode Go and I'll be back here in 2 months.