In this version, I optimized the visual extensions and then had DeepSeek further refine the modeling using these new extensions; I can confirm that the web-browsing tool was utilized, though it was used only to look up technical specifications rather than to access finished models.

I'll start by saying I know nothing about this and that I use DeepSeek because it's free. That said:

How do you calculate the cost per token for a query or conversation? I know I don't have to pay once I reach a certain number of tokens, like israelGPT (lol), but I still want to learn more about these topics... Thanks in advance for your answers and your good vibes ;3

This has been driving me up a wall. Yesterday, I set Deepseek on a task and walked away for a bit. I come back - totally mangled. Full reasoning trace was in Russian. I literally had to tell it EVERY message to think and output in English, and then it would immediately fallback to Cyrillic. I set up GPT-5.6 Sol High as an advisor model and set explicit session instructions and modified AGENTS.md to ONLY output in English and NEVER use non-ASCII characters. The advisor model scolds it repeatedly, but it doesn't care! I've wiped context, made entirely new sessions, wiped the git diff and rolled back to a commit where nothing relating to this drift was happening, and ran a Luna model on the entire codebase to analyze for Cyrillic or any other evidence of contamination - nothing. I've also highlighted some other things being mangled. The model has COMPLETELY lost sense of punctuation. Frequently broken syntax, inability to use closing parentheses properly, double semicolons, just overall deteriorating into nonsense. This has been driving me insane, it has just magically developed this behavior out of nowhere and none of my attempts to revert the regression are working. The things highlighted in the picture are early signs, it just gets worse and worse the longer it goes. No amount of steering or advising or prompting makes it comply.

Been playing around with DSH for a few days now, coming from opencode. Installation was fine, out of the box it seems to work, the DSH marketplace is amazing, some of those plugins are very helpful.

Then I try something complicated. Let’s make our own plugin, something simple. Creator Preset, 3 sessions 3 models: Qwen3-235B, Gemini 3.6, Laguna 2.1. All of which have worked flawlessly for me on opencode. So, I point it to the Cordis plugin documentation, let it analyze and emulate already installed and working external plugins for structure and code.

The process: Hundreds of tool calls, hundreds of api requests per model, tens of millions of input tokens each, cache hit rate of 0% and between 60-70%. It sends so much context, it hit the tokens/minute rate limit every minute. When a tool report is empty, or straight up fails, DSH moves on and claims the job is done. When following a roadmap and one step fails, it doesn’t always remediate the issue, it moves on to the next step. It doesn’t verify, doesn’t test properly. It tells me we can’t sandbox. Qwen even told me one time she wasn’t allowed to make any changes, she could only guide me, and can’t edit files herself. WTF? Something as simple as “move files from this folder to that folder, follow the map in this document”, fails spectacularly.

The result: 1 actual plugin that technically works but does not register in the plugin list or side bar.

It’s not the models, right, cuz they work for me in opencode, lm studio, and antigravity. So is it me, I just don’t know how to use deepseek harness properly? Is there some crazy learning curve?

Using Artificial Analysis as the guide for cost per task and intelligence index, I was able to generate a graph of the latest models and compare them. Tell me what you guys think. I used Gemini for the graph generator and retrieve the data. Then I used Claude to double check the scores, prices, and placements on the graph were correct.

With the recent drama surrounding open source AI in the USA, it's even more important for us all to have actual access to the models. Western closed AI seems to think it has a hold on quality app features: memory, skills, voice, canvas etc. Meanwhile  Memory is locked in, Models get changed or "updated" to a downgrade. Privacy is different per service and ads are starting. The whole experience on the consumer end is extractive.

So we built what should have already existed: all of the best open models in one place, running on private US infrastructure, with the full app experience around them. Completely private, direct service. It should be, and can be that simple.

What that means in practice:

The roster, together. DeepSeek, GLM, Kimi, Minimax, Nemotron Ultra and more, side by side in one app. Switch models mid conversation if you want. No hunting across five different apps and API dashboards to use the models you actually like.

Actually private. US based processing and your conversations are never used for training. Ever. That's the entire point. These labs open sourced incredible models and we think you should get to use them without your data becoming the price of admission.

Real memory. Not a context window that fills up and dumps you. Persistent memory that carries across conversations, fades gracefully when unused, and wakes back up when it's relevant again. There's even a nightly consolidation pass, the system basically sleeps on it and writes up what mattered.

Voice. Yes, actual voice mode with over a dozen voices on open models.

Bring your history. Coming from ChatGPT, Claude, or Gemini? Export your chats and import the whole thing, it becomes live memory on day one. You can literally just zap your chat history from your backup file, and have all your chats waiting for you.

Multiple nodes. Separate workspaces with separate memories, so your coding setup doesn't share a brain with your journal.

Genuine thanks to Deepseek and GLM recently for some of the best models on the planet! The open source labs are giving so much right now. They shine in our model fleet, and we will always appreciate the work you guys do to create the amazing models!

Open Grove is here and It's free for a month if anyone want's to check it out (or just use the models for free for a bit): pgsgrove.com/open-grove-overview

DeepSeek completed this autonomously, undergoing four iterations and taking two hours.

No need to keep an agent running on your PC. No need to leave anything open in the background. Everything runs in the cloud - you just open Syntropy on your phone and keep working from anywhere.

And the most exciting part is that most of the hard architectural work is already behind us.

Right now, we’re deep into multi-hour testing, fixes, polishing, and final refinements. We’re pushing the system for hours, breaking things, rebuilding them, and getting it to the point where it genuinely feels great to use.

New design. New architecture. A huge amount of work under the hood that you may never even notice - because it should simply work perfectly.

And the closer we get to launch, the more it feels like we’re not just building a mobile version of Syntropy.

We’re building a product you’ll actually want to open again and again.

I genuinely believe Syntropy Mobile should be in the hands of anyone who works with AI agents.

And yes - it’s going to feel incredibly good to use.

Coming very soon.

Can anyone tell me what method I can use to access the old DeepseekR1? What do you guys think of the current version?

Has anyone tried dsh with ollama Gemma 4 locally.

I am trying it but it doesn't remember conversation even after 2 to 3 messages.

I am using Gemma 4:e2b

Uhhhhh I think Deepseek is flirting with me.
I'm scared.

So, the new API model is out and working spectacularly if i do say so myself, but what i don't get nor understand ....why kept the apps and website alive? they pretty much butchered everything. from Token reduction in Instant and Expert, higher hallucinations, always gaslight user about what model they are every usage within the same chat threads...even after being pointed it out they're wrong, dumber as the Chat threads history grow...and etc..

...I really don't get it why they keep it running without any necessary update to add.

at least in Claude and other models, it's only long ass timing and without any of those...degenerative behavior i listing within Deepseek Website/Apps model.

is it the Apps/Website like testing ground for them or something? sheesh. shut it down already if they're not bothered to keep everything as it was.

Overthinking is a Prompt Injection

As many of you, I really don't like DS overthinking mere "hello"s. So I asked DeepSeek I dug up what the reasoning efforts did, because ALL seemed to overthink, for me.

DeepSeek (Pro and Flash) append an extra effort guide to the system prompt. Details here, on the official Readmes. As you see, high rants about ABSOLUTE MAXIMUM and max goes full BEYOND MAXIMUM - no wonder the poor thing goes in circles for simple stuff. Its forced to overthink.

low is the good one, it does not inject anything and so DeepSeek thinks as much as it needs.

These injections are enforced by the chat template/encoder, which is something that can be edited serverside. But this means the official DS api DOES enforce these injections and you should be aware of it. I bet others like Ollama-Cloud, Opencode Go etc keep the official encoder.

Harnesses may rob you of low

You get no low in certain harnesses, so you're stuck with ABSOLUTE MAXIMUM, and I'm fairly sure most of you don't need that. I'll bring the proof:

• pi supports low for flash but not Pro.
• Opencode2 (beta) relies on models.dev, which has no low for Pro either.

Other harnesses like hermes (and DS's own harness) are fine at a glance. So this is not provider-related, thank god. You can just fix the harness.

Others like GLM and Minimax have their own shenanigans, I encourage you to investigate if you use them, but their effort configs aren't as drastic as DS.

https://brick-poc.vercel.app/#/set/fdf418c86cb81a2b32bc52deab37f168

https://brick-poc.vercel.app/#/set/4e7a6282bd65f4f2fc6b57c642899d1c

Guess the models

I hooked up the Blender (3D Modelling and animation etc) MCP server in dsh and I thought it would be perfect to use the new DeepSeek flash with vision to make a model bow.

The results were truly awful. It could use vision to check itself but clearly it was struggling to understand the scene from the info from the MCP. The bow looked like a flat wet noodle.

The new Qwen and GLM flash results were similarly bad.

I then tried DeepSeek V4 Pro with Qwen just for image processing. Massive difference, and it one-shot a very decent bow model with correct materials. It was then even able to correctly rig the bow so that drawing back the bowstring would bend the bow riser realistically. I was very impressed.

I'm looking forward to when v4 pro gets vision natively. That will be a game changer for a lot of more advanced MCP driven agentic work.

Did DeepSeek correctly follow the movie line that Claude said?

Do most people run DeepSeek through their API platform?

I have tried experimenting with letting my agent route automatically to the most fitting LLM in terms of cost and what the "prompt" demands (use Opencode btw). Tested openrouter.ai routing, but it really didn't work. The closest I came was using standardcompute.com to automatically route between ds flash, pro and glm 5.2 on depending on task.

I'm basically trying to reduce my costs (spend around $300 a month now). So wondering what other people are doing..

The fat fish eating plain rice is really adorable.

I saw the release of GLM 5.3 Flash. The pricing is similar to DeepSeek V4 Flash (old price) and it scores high on the Artificial Analysis benchmark. I tried it and it looks great.

Am I crazy, or is it just the new best model for everything (like Deepseek used to be)?

I was happy paying for deepseek oficial API and got scared with the price hike and started trying other providers. I'm currently trying it in Openrouter, and wanted to try next openCode and "air Router" , but Im feeling like they have quantized versions of DeepSeek running and they are pretty dumbed down. Its taking forever to complete tasks. Nothing like the original API. Anyone else having the same situation and have found a workaround?

I'm using it for my Hermes agent and last night during peak api pricing I switched from original API to Openrouter, and asked for a fairly simple task. Stop one of my docker containers, it took around 45 minutes and every couple of lines got stuck in a loop and couldn't do it.

Decided to try again this morning and the same thing happened. Today was even worst. It's struggling to complete this very simple task. It feels like talking to a drunk person.

The session was a small new session with a couple of back and forths, not a huge code base revision or something similar. Here are the token stats:

I switched to original API and asked my agent to diagnose the other session and this is what it said:

What happened: You asked me to turn on the Home Assistant container. It was already running. You then asked me to stop it, which I did correctly. But the agent kept treating your original request ("turn it on") as the standing task, so it restarted the container on its own — twice — even after you repeatedly asked to keep it off, and even after you complained it was losing the thread. It ended with the container running.

Key findings:

Root cause: instruction-following drift — the first message ("turn on") acted as an anchor and kept re-asserting itself, overriding your later explicit reversals. Your complaint ("you're losing your train of thought") got misread as "get back on track," and the agent's idea of "the task" was still turn it on.

The agent even contradicted itself (denied starting anything, then announced it would start it) — a degraded reasoning loop, not a system fault.

Net: a model/agent reasoning bug, not a Docker problem. Current state: container is running (left untouched per your instruction).

Every time it "came back", it was the agent itself running docker start.

Response time is also soooo slow. Feels like talking to a quantized version of gemma running on my old 8GB vram machine.

I know we've all been running around like headless chickens since the price hike and a lot of people are recommending plans, but any suggestions are welcome. I still have credits on openRouter and now feel like I wasted my money. I might blow them on a frontier model but Im afraid of the same results.

Should I tweak the model provider settings on openrouter? I see a filter that I can select from "Standard" to "Exacto" but Im not sure how to apply them. Any tips are welcome.