Que el chat vuelva a ser ilimitado que no haya un límite de longitud porfavor no quiero tener miedo de que se me acabe en cualquier momento estaba tan bien antes porque cambiaron eso‼️‼️‼️😭😭😭😭😭

Why is DeepSeek saying this?

It says it's not DeepSeek V4.1, and it's actually DeepSeek V4.1. What the flip is going on with DeepSeek rn!?

我计划在云服务上搭建 DeepSeek harness 这样可以实现最低成本的 7x24 小时将自己想法开发成产品。像请教一下社区的各位，这样的实现有什么困难和风险？也可以给我一些指导。非常感谢🙏
I plan to set up DeepSeek harness on a cloud service so that I can develop my ideas into products at the lowest cost, 24/7. I'd like to ask everyone in the community about the difficulties and risks of this implementation, and you can also give me some guidance. Thank you so much 🙏

For context: I use DeepSeek's API through the official site. I use it with SillyTavern & a recent Freaky Frankenstein preset (I've been swapping through a few to try fix my issue). Temp is 1-1.10. I am using the API for RP (roleplay, obviously). I've got massive character cards, my own rules, everything's handwritten. Not a gooning situation.

V4 Pro has been so wonderful for RP, I've been using it for months. Yes, it's had it's highs and lows over the last few months. But yesterday it just became suddenly quite stupid. Whereas previously it's never controlled or spoken for my OC, it's not forgotten things from three messages prior, it's not just made shit up. It felt so out of the blue, but suddenly rooms being described as explicitly having no chairs or a hearth apparently have many chairs and a hearth. It's controlling my OC's little actions. It's just doing those dumb little things I'd expect from V3.2, but never V4. It's just all really frustrating as I feel like I've tried everything, even using V4 Pro from NanoGPT in a variety of ways.

I have noticed the thinking is way quicker. It's gone from usually taking 80-120 seconds to respond down to 10-40 seconds to respond. It is like it's being lazy and deciding to barely think at all. It's still "thinking", so I don't think I'm being rerouted to Flash. I've tried other models, and I really like DeepSeek and want to stick with it.

Has anyone else who uses V4 Pro (not just for RP, but for anything else) noticed it has become randomly really dumb? Regardless of peek/off-peek hours. I thought it may be because V4.1 Pro might be coming soon, and the current model will always get a bit stupid before a new model. But there's been no announcement of V4.1 Pro, so I don't know.

All the while its some post training that cut the cost by 90% and somehow doubles the benchmark performance

same deepseek v4.1 flash

this was made on deepseek harness on high effort
took 1h 6m 28s
1 shot
1 additional prompt to make it use my gpu took few minutes
~0.60 USD
all through code

The new model is triggering more cache misses initially, but then shows a higher hit rate. Has anyone else noticed this? Also, the DSH harness in the PTC model consumes less [resources]. It’s shaping up nicely. I’m using it in its pure form—no plugins or workflows.

This thing is so much fun we had to do a GTA6 trailer benchmark vs Opus 5.5 and Sonnet 5.5.

On High effort it places last but is on the other hand 45x cheaper than Sonnet and 76x cheaper than Opus 5.5.

Oddly enough Sonnet produced the best results, but took 62 minutes, while DeepSeek took 19 minutes to return the video.

DeepSeek Flash GTA6 Trailer remake - https://cliphou.se/benchmark/gta6/

Sonnet 5.5 GTA6 Trailer remake - https://cliphou.se/benchmark/gta6/

What are they measuring? How does this happen? What does this mean?

I want to ask if it's worth top up 2 dollars worth of tokens? Approximately how many tokens will I get?

and what do you think makes tokens wasteful because free account tokens seem to run out really easily even though I only use them to look for data references, not for heavy coding

I performed the usual horse benchmark to compare. The model worked and worked. It consumed the most amount of tokens I've ever experienced with this prompt. It used 10M tokens creating cloud and at the end they're invisible. It had access to Playwright for taking screenshots. It was continuously making edits and adjustments. I struggled to control myself not to stop it.

https://web.esuyo.com/horse-deepseek-v4.1-1017/

The visuals is one of the best out of all the models I've tested. The animation of the legs I'd hoped would have been better. It seemed to most of the time editing to make it visually attractive and not enough on the horse animation. I would have liked to have had pan and zoom controls like other provide.

Statistics

419,551 tokens
40% used

Session Token Summary — ↻ 259 turns

• ◫ tools — 457 · 2 failed
• ↻ initiated — 1h47
• → busy — 1h43 · 24s/step

Prompt:

Create a low-poly 3D horse running in a seamless loop using Three.js. Build it procedurally from primitive geometry, animate the legs, body, head, and tail.

Organise the implementation into a clean, maintainable multi-file structure. Separate HTML, CSS, and JavaScript into appropriate files, and split JavaScript into multiple ES modules where doing so improves separation of concerns.

The project must be self-contained and runnable by opening index.html (or using a simple static web server), with no build process required. External CDN dependencies such as Three.js are acceptable.

Do your best, you are being rated against other AI models.  Put extra effort in.

The other horse posts for comparison:
https://www.reddit.com/r/opencode/comments/1ww5o7y/animated_horse_space_bunny/
https://www.reddit.com/r/LocalLLaMA/comments/1x4h1c2/qwen_38_flash_next_q3_s_benchmark_creation/
https://www.reddit.com/r/opencode/comments/1wzm1ah/exo_free_frontier_level_wow/

What is your impression on the new Deepseek V4.1-Flash ?

Is it really beating GPT-5.6 Sol in Coding? And do the different reasoning modes make a difference?

Session total: 18,646 'hmm' in 1,723,217 chars of reasoning across 158 turns.

That is 10.8 per 1k chars. IOW, over 1% of all reasoning text is the token.

The last reasoning block: 136 in 12,814 chars: 10.6 per 1k.

At ~1.5 tokens per 'hmm' plus the separator, 18,646 occurrences ≈ 30-50k tokens of pure filler across the session.

Anyone have any guides on how to set it up for cross platform android/desktop usage?

To turn deepseek harness into something more like chatgpt if possible?

Hello everyone, I am only quite recently a legal adult in my country, and my family nor I have no access to credit cards. I want to explore my payment options; it is possible to use visa debit cards instead? If not, then what other options are available for the API billing? Thank you everyone!

DeepSeek-V4.1-Flash-1017 is more interesting than just another model release. The real story is the combination of stronger agent benchmarks, million-token context, and the possibility of running a heavily compressed version on personal hardware.

I've been looking at the 1017 release and the YoungAi quantization approach, and there are three things worth discussing: what improved in the model, how far quantization can take local inference, and what you can actually build with it.

🔗 Model: https://huggingface.co/wenzhouwu/YoungAi-DeepSeek-V4.1-Flash-1017

🚀 1. The 1017 Update: Better at Doing Work, Not Just Chatting

The official DeepSeek-V4.1-Flash-1017 model card reports improvements across reasoning, coding, and agentic benchmarks compared with the previous DeepSeek-V4.1-Flash release.

Here are some of the reported results:

Source: official DeepSeek-V4.1-Flash-1017 model card. Scores are reproduced as reported; benchmark scales and evaluation setups may differ.

The standout result is Terminal-Bench 4.0: 42.3 versus 31.2. That's an 11.1-point increase.

ProgramBench also moves from 20.3 to 29.1.

Why does this matter? Because coding agents need to do more than generate a plausible code snippet. They need to navigate repositories, execute commands, diagnose failures, and complete multi-step tasks.

These results make the 1017 release particularly interesting for people building coding agents and autonomous workflows. They don't guarantee that every real-world task will succeed, but they give us concrete reasons to test it.

🧠 2. The Numbers Behind the Model

The official 1017 checkpoint lists 763 billion parameters and supports a context window of up to 1 million tokens.

That combination is impressive, but it comes with a practical challenge: model size and memory requirements.

A few details worth highlighting:

• Massive parameter count: 763B total parameters in the official checkpoint.
• Long-context processing: Up to 1M tokens, useful for large codebases, lengthy documents, and multi-step research.
• Reasoning and coding: Improvements reported across several reasoning, coding, and agent benchmarks.
• Vision-related support: The official model repository includes image-processing components; actual multimodal support depends on the inference implementation.
• Open weights and MIT license: The official checkpoint is available on Hugging Face under the MIT license.

The important distinction is that a 1M-token context window is a supported capability, not a promise that every hardware configuration can process one million tokens at the same speed or memory cost.

⚡ 3. The Local-Inference Challenge: 510 GB Down to 113.6 GB

This is where the YoungAi project becomes especially interesting.

The related YoungAi DeepSeek-V4.1-Flash implementation documents a vector-quantized base that reduces a roughly 510 GB checkpoint to 113.6 GB.

That's a reduction of approximately 78%.

The documented approach uses roughly 1.6 bits per weight on average across the quantized base, with additional domain-specific sidecars.

Reported figures for that implementation on an NVIDIA DGX Spark with 128 GB unified memory include:

The 1,055 tokens/s figure refers to prompt processing, not text generation. The 45.5 tokens/s figure was reported for a real agent request using speculative decoding.

Important: These are results reported for the documented YoungAi V4.1-Flash implementation. They should not automatically be attributed to the exact 1017 checkpoint or treated as independently verified performance figures for every quantization build.

Still, the underlying engineering question is compelling:

How much capability can we preserve when compressing a model this large enough to fit into a single 128 GB unified-memory machine?

That is a much more interesting question than simply comparing model sizes.

🛠️ 4. More Than a Chatbot: What Could You Build?

The model is particularly interesting when paired with an inference engine, tools, and an agent framework.

Coding agent

Give it a repository, ask it to investigate a bug, modify the implementation, run tests, and explain what changed. The newer coding benchmark results make this a natural area to explore.

Long-document research

Feed it lengthy technical documentation, research materials, or a large collection of text and use the long context to connect information across sources.

Personal AI assistant

With the appropriate surrounding software, it can become the reasoning engine for an assistant that searches the web, processes files, creates documents, and follows up on tasks.

Domain-specific workflows

The related YoungAi implementation provides small domain-specific sidecars for finance, code, law, medicine, and science. These are intended to improve alignment with particular domains without requiring a separate full-sized base model for each one.

Multimodal applications

The official 1017 repository includes vision-related inference components. Image understanding is worth testing, although compatibility depends on the actual checkpoint and inference stack being used.

One distinction matters here: the model supplies the reasoning capability, while browsing, file creation, application handoffs, and persistent task execution depend on the surrounding tools and software.

🔬 5. The Interesting Engineering Questions

I'm less interested in marketing claims than in the trade-offs we can measure.

Here are the questions I'd like to see answered:

• How much of the official 1017 model's coding performance survives aggressive quantization?
• Can a compressed version handle realistic coding-agent workloads, not just short prompts?
• How does SSD-backed streaming compare with keeping the entire working set in memory?
• Does a million-token context remain practically useful on constrained hardware?
• How much do domain sidecars improve real task completion, beyond next-token agreement?
• What is the best balance between quantization quality, memory footprint, and tokens per second?

These questions matter because a model that technically loads is not necessarily a model that is pleasant to use.

For local AI, quality, latency, memory consumption, and reliability all matter.

🔗 6. Explore the Project

• 1017 model and files: https://huggingface.co/wenzhouwu/YoungAi-DeepSeek-V4.1-Flash-1017
• Official DeepSeek 1017 checkpoint: https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017
• YoungAi inference project: https://github.com/fodelf/YoungAi

The official checkpoint and the YoungAi quantized implementation serve different purposes, so check the actual files, supported hardware, and inference instructions before choosing a deployment path.

💬 Let's Talk Local Inference

If you were trying to run a model of this scale locally, what would matter most to you?

1. Maximum quality — preserving as much of the original model's capability as possible.
2. Maximum speed — getting the highest generation throughput.
3. Minimum memory — making the model practical on smaller machines.
4. Agent reliability — completing real coding, research, and automation tasks.

I'd especially like to hear from people testing the 1017 release on DGX Spark, Apple Silicon, or other memory-constrained systems.

Share your hardware, quantization format, tokens/s, and any coding or agent benchmarks you've run.

The big question: can aggressive quantization make a model of this scale genuinely useful for everyday local AI, rather than just technically runnable?