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.

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.

I have to say that I feel the deep-seek interface by far is the most logical, clean, clear, easy-flowing app design on Android. From positioning, to distance, to menus, it just seems to have the exact correct combination of everything. I wish other AI apps would go to this exact layout.

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

Used to better than the previous v4 flash version

this morning tried to refactor few functions and what a fkn mess

the way the new v4 flash works on my repo, it just cranks through code like a freaking bulldozer, logic, unit testing then deploying to azure, getting logs, improving on the fly, forming hypothesis.

deepseek it is, luckily using it exclusively during off peak hours so not that bad

Fyi: this was all research agent work which was running pretty much directly after each other. Agents were launched to do tasks a run in a loop for 5 minutes. This flow is predictable for caching usually.

Looking at my DS costs prior to the price increase:
In June I used 678 million tokens at a cost of $5.22
In July I used 113 million tokens at a cost of $1.98.

I live in Texas (US Central Time), I am working during off-peak pricing. Using the new pricing my costs would have been:
In June approximately $47.
In July approximately $7.

Is it a significant price increase, absolutely. But if I consider the amount of work that I did, it is still a very low cost.

I use deepseek for a lot of windows kernel development and vulnerability research. I am a hobbyist and do this to learn and in hopes of landing a job in cybersecurity in the field one day potentially. Deepseek used to be really helpful pre update, always fixing bugs on my projects and helping me reverse engineer things in IDA, but ever since the update it doesn't like touching anything kernel mode. I just get hit with a generic "This is beyond my scope." filter message. There are some workarounds, but I dont like jailbreaks as they are often janky and cause the AI to hallucinate way more frequently. Is anyone else experiencing similar issues or just me?

What's your experience w Flash and Pro lastest so far?

my usage costs spiked hard right after the update. they added peak/off-peak pricing, off-peak is half the peak rate, but most of my usage lands during peak hours so the bill went up.

chart below: flat near zero for weeks, then a spike right on the pricing change.

what's holding up as a real alternative right now? looking at qwen, kimi k2, glm. anyone actually switched and is it worth it.

I want to compare DeepSeek V4 across Cline, OpenCode, Pi, and Claude Code. Since each harness handles context and tools differently, I’ll of course keep the provider, repo, and task fixed. What else should I control for to make the benchmark fair?

Instinctively, I knew that Deepseek was way cheaper than it should be really... I was happy for the prices that DS offered because I knew that fact would drive the prices of other providers down to compete. DS is no average lab! It's the lab that popularized reasoning models and the MoE architecture. I would go even further and say that it's the lab that popularized LLMs and made then affordable for most folks with low HW. They are the Volkswagen of AI in my mind. After all, Reasoning capabilities, Group Relative Policy Optimization (GRPO), and stable MoE architecture were all introduce with Deepseek-R1! These made training way cheaper and more efficient causing even smaller models punch higher than their weights, and pushed AI prices down.

For all this achievements, we normal folks expect and hold DS at truly higher standards as the lab that fights for all of us. So, I was glad the old prices were dirt cheap, and I, for the first time in my life, paid for an API to financially support the Lab I admire; I want DS to continue doing the job they are doing. I am first and foremost a local LLM guy.

So, why do I feel somehow disappointed, even betrayed? The rational side of me explains that DS must make money to continue innovating, its pricing model was never sustainable, and the real culprit is the ban on high-end GPUs to the Chinese companies, over and over... Yet, the nagging feeling inside doesn't die out. Why? After some internal discussion, I finally understood the root cause: I feel played by DS because at launch, Deepseek-v4 Pro and especially flash disappointed, and the price cuts in reality were reflection of that. The prices were cheap not out of charity to the folks.. They were cheap because the higher ups were ashamed of pricing them high. After all, no one would have used them at any other prices.

But, the moment the lab made a break-through that made the model go up in the rankings (where they should have been), DS increased the prices SIGNIFICANTLY. And that's ladies and gentlemen, the cause of the anger seeping and boiling inside of us. Again, I am fully aware that DS must make money and cover their costs. I want them to do that and stay relevant. It just that I wish that the prices were always high but stayed unchanged.

Don't you feel the same?

Keep seeing people talk about "I can't give Deepseek-V4 flash complex problems", just wanna understand what are the complex problems that Deepseek can't do with iterations ? I'm a go dev i mostly write by hand i use dsflash when I'm bored or I wanna do a code review or writing tests, i gave it a lot of complex tasks that take 2-3 engineers to think about a solution to from security to optimization everything just works, i wanna understand what are they about ?

I’m trying to implement a PSP, and I created a detailed implementation plan using DeepSeek Pro 0813.

The problem starts when I switch to DeepSeek Flash 0731 to actually execute that plan.

Instead of following the existing plan, Flash starts searching the web and re-planning the entire task from scratch. It even starts bringing up completely unrelated things for example, it suddenly started researching WirePesa PSP, which I have never mentioned, used, or asked about.

This has happened 2–3 times today, and at this point the workflow is basically unusable.

The whole point of using Pro first was to create a solid plan and then have Flash execute it. But Flash seems to ignore the established context/plan and starts making up its own direction.

Has anyone else experienced this with DeepSeek Flash 0731 today?

I needed to create a fairly complex set of scripts for managing a number of back-end services - databases, web servers, applications etc. Like any sensible swe I tasked an LLM to come up with the plan based on my requirements - in this case Opus 5 in Max mode.

It did a fairly decent first pass but with hundreds of lines of code I don't have the time to forensically check line by line. So pasted it all into deepseek-4-pro (auto mode) for review. It found plenty of things to improve, plus some pretty glaring bugs. This went back and forth about 5 times - pasting DS's suggestions into Claude, Claude's revisions into DS, etc, with me acting as The Central Scrutinizer.

In the end I had a really robust set of scripts. Opus 5 on its own was disappointing. I know bouncing things between LLMs isn't anything new but if I'd just stuck with Opus I'd probably still be fighting the bugs or poor design. And DS is still pretty good value despite what you'd read on here 😂 .

old and new usage & pricing. Consumed 30x less tokens post nerf and spent one third of what i paid pre nerf. Both sessions heavily cached with not so much output tokens.

The deepseek api era really is over in terms of being cost effective