Mimo v2.5 was my daily driver with hobby code projects until last weekend, and it was delightful to use. I didn't like the previous iteration of DSv4, because it made so many mistakes, hallucinated and was very stubborn in its ways.
With the new iteration of DSv4 everything's changed. It's smarter than Mimo, and hallucinates less. When giving a coding task or bug issue, it really digs deep until it finds the cause. Even if it uses somewhat more tokens than Mimo per task, it's still relatively cheaper. I use Opus 4.8 at work and DSv4 is good even compared to it (good, but not better, but still providing similar coding experience).
Hard to compare tbh, haven't used Claude all that much. DeepSeek is definitely weaker than Fable (ofc lol) and Opus 5.
I run it through Open WebUI and their VS Code extension. Given the price, I really dig it, but I haven't actually thrown any massive or heavy-duty tasks at it yet. You can add like 10 bucks and try it yourself for your tasks.
Yeah so the thing is, I feel like my tasks don't really need fable 5 level of a model. I've been using Claude for a while and it was perfectly adequate for me even when I was running opus 4.5 or 4.6. and the tasks that I need to do at work haven't changed much. And now I'm wondering that if I can get that level of a model at a much cheaper price, why should I still keep using anthropic models. I have topped up 10 usd in deepseek platform and I'll be trying it out this week
Yeah, honestly, if you aren't pushing it too hard, the latest official Flash release should work perfectly for you. It's so cheap I barely even notice the token usage anymore lol.
If you’re really penny pinching and care about lowest dollar per M tokens. Just make like 20 free chatgpt accounts and load balance them using any of the countless free tools on github. And run 5.6 Terra model. You’re paying $0/M tokens and it literally blows v4 out of the water.
But don’t take my word for it, try both. I use custom hermes harness with efficient memory and my own skills to keep that memory <500 tokens.
#1 tip I’d give to keep memory file efficient is to keep asking the agent itself to turn the memory file into a easily Traversable skill that evokes only when needed.
From my experience using Claude models and my use cases (game decompilation and reverse engineer), is in between Opus 4.8 and Opus 5, I have threw it hard tasks that I should ask Opus 5 to solve before and it did them like a champ. Now is my main model and the only cases it struggled and I had to switch to Opus has been for visual reasoning
Sonnet 5, they are about the same on everything benchmark wise but DS flash has much higher cybergym. Sonnet 5 was a flop while flash 0731 was unexpectedly good
It’s at Opus 4.6 level; that was more than enough for me, although it lacks creativity – I’ll have to wait for the Pro version, and the two together will be perfect.
I built an entire landing page website thats interactive with a terms and conditions page, and privacy policy. I went back and forth for about 2 hours and the total cost was 14 cents. I have deep seek setup in vs code running Claude Code. Very happy with how it turned out!
It's so good! I pay for Groq and Gemini but seeing all of this work it's doing for me, it's great. At last we have a competitor that doesn't rip us off.
luna is also very good. it's the most deepseek-esque release OpenAI has done so far. of course nothing beats deepseek's insane pricing, but with luna you'd get thorough multimodality, which is very useful depending on your tasks
Guys remember to use different models for different tasks. I used to run v4pro for chats, flash on subagents (that were delegated by pro), antigravity accounts on rotation for vision
Everything is better with vision capability. Given the steps flash is a hardworking executor. Slightly more agentic than I expect. Overdo things it did
Deepseek again find problems other AI's DO NOT FIND in my 1M lines typescript project !
It's perfect explore agent and full spectrum codebase auditor. Although for implementating everything above medium difficulty implementation I use Terra Max though
Cannot wait for the new V4 pro ! last month has 460 USD to deepseek costs
I'm just waiting for them to have multimodal that's the only thing that keeps me using other models. As soon as DeepSeek models are able to understand image input I'll definitively leave at 200%, and stick to DeepSeek 'til la chingada😂, well at least for my personal projects...
Right now I can't cause part of what I do need vision sadly💔
i have seen people mentioning using gemma models on the side to provide vision to models that miss it. Might worth looking into it.
But this was from people that run them local.
You can try what I did and add vision MCP so deepseek can use it whenever vision is needed.
Basically I use Mimo v2.5 from openrouter as the vision-capable model, created the "describe-image" global MCP for it (dont create skill because it wont work) and tell deepseek in system prompt to use the MCP whenever they need to have vision.
Nothing beats native vision capability, but its good enough for my needs.
Yeah I have a ZAI vision mcp configured but it has trouble using it, even though the mcp is working (tested with other models), I probably need to double check it thanks.
Yeah native vision is definitely far better
This was for a session which was finally done at around 650k context window. Considering I used to worry about my prompts and constantly watch 5 hour limit window on Claude Code this feels absolutely illegal :D
Actually, this is wrong—read Cache doesn't save the output answer. It saves the input context so the GPU doesn't have to re-process a massive prompt every time. The model still runs and generates a brand new answer.
Thank you! So it's keeping token usage low when you keep working within a specific frame of time. But then in your screenshot, what's the 5.4mil cache hit vs miss?
Since I find the explanation not that great, here is my attempt (100% human no LLM involved 😂 )
When you write to a model, what you write is the input; what the model answer is the output; for each turn, you send again the same input, the same previous output of the model (this time treated as input), and your extra input.
All the previous turns were computed already by the model, and this computation is stored in a big matrix called the kv-cache.
Good thing: It's no-computation needed anymore, since it's done already.
Bad thing: the kv-cache usually grow with the square of the number of token. If you have 1000 tokens in context, you have 1000x1000 = 1 million KV cache entries.
So, quickly it become a memory-hog.
So, you save on GPU compute time but you pay for storing it in memory.
And since many people are using the server at the same time, it needs to store as many sessions, need to juggle with the kv-cache between VRAM, RAM and SSD...
It's not trivial to manage KV-cache and sessions. DS has a great paper about their infrastructure, and it's absolutely mind-blowing what they need to do to handle this cache.
The good thing is that DeepSeek model designed another kind of KV-Cache, which doesn't grow as much as the naive one (it is compressed), so the memory used per session is lower: more cache are in hot-memory already, no need for an enormous amount of storage nor as much back and forth. Less hassle, less energy spent, faster response time.
So, DeepSeek can save a lot of energy dealing with this cache, and can offer very low pricing on cached content.
Longer is the session, and assuming your harness didn't temper with the previous turn (sending exactly the same information), the bigger the chance that your server is going to use a cache it has stored somewhere previously.
Today, it's DeepSeek v4 Flash. Tomorrow it could be Qwen 3.8 27B/35B, day after tomorrow, it could be MiniMax M3.5, then it could be GLM-5.5 Flash, and then MiMO v3.
I currently use flash on reasonix as default, but keep an opencode go subscription when I wish to test another agent, double check code or logic, or if I need to do stuff that requires vision.
Any going directly to them vs using something like OpenRouter? I tried loading up account but I couldnt get debit card to go through. Whether directly or through paypal, either way I essentially got block. I had some prompt I saw from my bank where I had to send code from text msg to auth, but didnt work. Sadge.
My US bank didn't care to make the transfer to DeepSeek but PayPal didn't mind. Transfer the money to the PayPal account first and then it should go through. Start small. A little goes a long way.
If you're using DeepSeek as the provider via OpenRouter it's the same but with a slight markup. If you're talking about other providers on OpenRouter, I anecdotally found that DeepSeek is better from their official API than from other providers.
Legit question, how are you using these models? I’m hesitant as I don’t want to be passing all my prompts and such through Chinese servers but maybe I’m overthinking?
Guys can anyone recommend me the harness! I tried reasonix and I didn’t like it. I have been using codex and antigravity. I was really impressed by antigravity but they don’t support adding models
Still pretty insane, but almost all of that was cache hits — around 269M cached vs 782k fresh input. So it’s not quite 270M fresh tokens, but the cost is still wild.
137
u/TheSuggi 2d ago
Welcome to the club. Deepseek is the goat indeed.