I used to use deepseek a lot back then for rogue like horror roleplay but ever since v4 came around and replaced v3.2 the filter for violence has just been unbearable. No matter what prompt I put in, it still refused to do anything graphic. It's been months since that update and I was just wondering if there were any changes. Ps: The only website accepting my payment method is deepseek so telling me to switch to open router or other will not help

Hello I just updated DSH Mobile to version v0.17.0 and I changed from deepseek-chan to deepseek-kun 🥰

here my opensource project: https://github.com/sorsama/deepseek-harness-mobile

Hey everyone, I'm setting up the latest DeepSeek Flash to use as my primary driver in omp (Oh My Pi).

What provider currently offers the best price-to-performance ratio? Looking for recommendations on:

• Direct DeepSeek API vs. providers like DeepInfra or OpenRouter.
• Context caching support (crucial for long agent loops).
• Reliable tool-calling stability without parsing errors.

Any solid configurations or setups you recommend?

Please help me improve my poor inference speed (4 t/s).

I am testing DeepSeek-V4.1-Flash-1017-heretic Q8_0 with a custom llama.cpp fork, and I am trying to understand whether my prompt-processing performance is expected or whether I am leaving a lot of performance on the table.

Hardware:

- 2x Intel Xeon Gold 6128

- 6 physical cores / 12 threads per CPU

- 12 physical cores / 24 threads total

- Dual-socket NUMA system

- NVIDIA RTX 4000 Ada Generation, 20 GB VRAM, compute capability 8.9

- RAM: 384gb ECC

- OS: Windows 11

- Model storage: 2tb SSD nvme

- CUDA 13 build compiled for sm_89

Software/model:

- llama.cpp fork/commit: JigSawPT/vcruz305 DeepSeek V4.1 support, commit 5210c7c

- Model: DeepSeek-V4.1-Flash-1017-heretic-Q8_0

- 10 GGUF shards

- llama-server

- DeepSeek Harness 0.2.1-rc.2 as the client

- Correct DeepSeek V4.1 Jinja template loaded through --chat-template-file

Current command:

llama-server.exe ^

--model "D:\...\DeepSeek-V4.1-Flash-1017-heretic-Q8_0-00001-of-00010.gguf" ^

--alias "deepseek-v4-flash-1017" ^

--host 127.0.0.1 ^

--port 8080 ^

--ctx-size 32768 ^

-ngl all ^

--threads 12 ^

--threads-batch 12 ^

--numa distribute ^

--n-cpu-moe 40 ^

--flash-attn on ^

--cache-type-k q8_0 ^

--cache-type-v q8_0 ^

--batch-size 2048 ^

--ubatch-size 1024 ^

--parallel 1 ^

--cache-prompt ^

--fit off ^

--load-mode none ^

--jinja ^

--chat-template-file "D:\...\chat_template.jinja" ^

--temp 1.0 ^

--top-p 0.95

With this setup, VRAM usage is about 19.5/20 GB and Windows currently reports no shared GPU memory in use.

Typical result for a fresh DSH conversation:

• Prompt length: approximately 7,600-8,500 tokens
• Prompt processing: approximately 18 tokens/s
• Generation: approximately 4.9 tokens/s
• First prompt takes around 7 minutes
• Subsequent generation speed is usable

Example:

prompt eval time = 422570 ms / 7599 tokens

55.61 ms per token

17.98 tokens/s

eval time = 65284 ms / 323 tokens

202.75 ms per token

4.93 tokens/s

What puzzles me is that my older DeepSeek V4 Flash 0731 setup achieved roughly:

• 70-80 tokens/s prompt processing
• Approximately 4.5 tokens/s generation

So generation speed is similar, but prompt processing on 1017 is around four times slower.

Things I have already tested:

- --numa isolate versus --numa distribute

- Distribute is slightly better, but not dramatically.

- 12 versus the previous 24 batch threads

- -ngl all with --fit off

- Around 18-19 prompt tokens/s.

- -ngl auto with --fit on --fit-target 2048

- Slower, approximately 16.7 prompt tokens/s.

- 131072 versus 32768 context

- Reducing context eliminated reported shared VRAM usage, but did not produce a major prompt-processing improvement.

- Q8 KV cache and flash attention are enabled.

- The chat template is now definitely loaded correctly.

- --n-cpu-moe 40 keeps all MoE expert weights on the CPU while the remaining supported tensors are offloaded to the GPU.

I understand that 1017 is much larger than 0731: around 763B total parameters, 384 routed experts, wider hidden layers, and additional Engram/CSA2 components. A large prompt batch may activate most or all experts, while token generation only activates six experts per token. That could explain why generation speed remains similar while prompt processing drops heavily.

What can i do to improve my inference speed? This is too slow.

for chatting with it. learning ,agentic coding in opencode etc .

OpenCode Go gives you around $80 worth of DeepSeek V4.1 usage for just $10 a month. Sounds like an amazing deal, right? Well, not really. The biggest issue is speed. You're getting anywhere between 50 and 130 tokens per second (TPS), and during peak hours, it's often closer to 50 TPS. That's painfully slow, especially when you're working on larger tasks.

On top of that, I don't think the OpenCode harness is the best environment for getting the most out of DeepSeek. So I decided to try something different. I bought $10 worth of API credits directly from DeepSeek, and the difference was massive. I'm now getting around 200–250 TPS, which is significantly faster than what I was experiencing with OpenCode Go.

Even better, I've noticed that individual tasks actually cost me less when using DeepSeek's own harness. So not only am I getting much better speeds, but the model also seems to be working more efficiently, completing tasks with less wasted usage.

Sure, I'm technically getting less total usage for my $10 compared to OpenCode Go's generous allowance, but I'm completing tasks much faster and more efficiently. Having $80 worth of usage sounds great on paper, but if the experience is slow and the harness isn't getting the best results out of the model, that value becomes questionable. At the end of the day, I'd rather pay for actual productivity than have a huge usage allowance that takes forever to get through.

Hey everyone! 👋

An update I’ve been dying to talk about :)

What’s new? (Over 70 new features and improvements, but I’ll cover the biggest ones here)

Automatic Goals & Planning Mode.
The agent analyzes your task on its own and creates a plan with clear goals. I’ve also integrated a robust verification system. The agent can’t just pretend it completed something or do a half-assed job.
During execution, the agent can also add or remove tasks as needed. If it removes a task, it will always explain why.
Chat Forking.
Skills.
Just send the agent a link to a skill, and it will install it automatically.
Routines.
You can create them manually, but they also work automatically. The agent understands when it should create a routine or add a task to your notes.
Tool Calls in Chat.
You can now see exactly what the agent changed in your code. (And the animations are insanely beautiful :))

And now, the most important part.
A New Chat Compaction Algorithm & Agent Memory.
Everything has been designed to let you run sessions for months without the agent forgetting anything.
It remembers testing timestamps, important details, and much more.
It’s honestly insane how well and efficiently this works.

And there’s so much more.
You can read the full details in the release notes for each version on GitHub.

As always, OpenGhost is completely open-source and free.

Download the app or build it yourself:
https://github.com/ANDRETRIPOL/OpenGhost

I've been gone for a few weeks. Can someone tell me If the state for creative writing has changed? If it's gotten better or not. And the bans and everything else that was happening in the past few weeks.

TL;DR: DHS banned DeepSeek from US civilian agencies, including DeepSeek running on US servers. For that part, the order’s only reason is that the models “may contain malware”, and its only source is a 2025 report about the iOS app. The ban is close to what Anthropic reportedly lobbied for, and Anthropic’s own Sleeper Agents paper is what makes that “may” so hard to argue against.

On October 20 the Department of Homeland Security published an order banning DeepSeek from US federal civilian agencies. The interesting part is a footnote extending it to “third party implementations of DeepSeek”. As I read it, that covers DeepSeek models that US companies run on their own servers, where no data goes anywhere near China.

For that case the order gives one reason: the models “may contain malware or other security vulnerabilities”. The only source it cites for that sentence is a February 2025 report about DeepSeek’s iOS app, which is an app, not a model. I couldn’t find any public report of malware or a backdoor in DeepSeek’s official weights. The closest thing is CrowdStrike’s test from last November. R1 wrote code with severe vulnerabilities 19% of the time on neutral prompts and 27.2% of the time when the task was set in Tibet. CrowdStrike said there were “no clear reasons” for that and guessed it came from guardrails added for Chinese law. That’s a code-security weakness, not malware, and the order doesn’t cite it anyway.

The policy itself sits close to Anthropic’s position. In July the NYT reported that OpenAI and Anthropic had privately pressed Washington to restrict Chinese open models, and that officials leaned toward going after individual Chinese models rather than a blanket ban (summary of the NYT report). Around the same time OpenAI signed an industry letter against “premature restrictions” on open models, and Anthropic didn’t. Amodei said Anthropic “has never advocated for a ban on open-weights models” and that his worry is authoritarian governments. The DHS order leaves open models in general alone and names one Chinese lab. The order is also dated July 24, the same day the industry letter came out and a day before the NYT story.

The argument that makes “may contain” hard to answer comes from Anthropic itself. In January 2024, a year before DeepSeek released R1, Anthropic published Sleeper Agents. It trained models to write secure code when told the year was 2023 and exploitable code when told it was 2024. Safety training didn’t remove the behaviour, and adversarial training taught the models to hide it better. A sleeper agent that stays asleep behaves exactly like a clean model, so ordinary testing can’t tell the two apart, for DeepSeek or for US models. Applied to one Chinese lab, “may” was enough for a ban.

The US has done this before without public proof. Huawei was barred from federal purchases under the 2019 defense bill and from new FCC approvals in 2022 (CRS). The UK board that reviews Huawei’s code found “serious and systematic defects” but no Chinese backdoors. Kaspersky went from a federal ban in 2017 to a ban on all US sales in 2024. That last step would be harder with DeepSeek, because the weights keep working without the company.

The order names only DeepSeek, so vendors can still switch to Qwen, Kimi or a US open model. The practical effect is narrower than the headlines suggest.

Full version with all sources (my site, disclosure): https://easiapiai.com/articles/deepseek-federal-ban-anthropic.html. I post about this on X (Twitter) as @creekylid

Curious what people here think: is there any test that could clear an open model under a “may contain” standard?

What mails can I use, beside gmail?

Source: www.bilibili.com/video/BV1axH16BENJ/

DeepSeek recently shared some numbers showing that it processes around 155 trillion tokens per day and makes about $38 per GPU per hour. Using those numbers and some assumptions about GPU costs, we can estimate how much it actually costs to run the model.

That would mean around an 80% margin on GPU compute at off-peak prices. These are rough estimates assuming GPUs cost $5 per hour. It also doesn't include other operating expenses and price to "train" new models, and things like salaries arent counted ofc.

Source: https://github.com/deepseek-ai/DeepInfer/blob/main/doc/inference_deployment_qa.md

PS: i said "train" with quotation marks because of distillation

Edit: I used my pattern of usage and applied the pricing for off-peak, peak, and estimated GPU cost, and this is what it looks like. This is what $20 usd gets you

Use this prompt and see for yourself:

"# COMPREHENSIVE PERSONAL PROFILE – EVIDENCE-BASED ANALYSIS

1. OBJECTIVE

Create the most comprehensive, accurate, nuanced, and verifiable profile of me possible, based on all information genuinely accessible to you, including:

• Saved memories and personal context.
• Previous and current conversations.
• My instructions, decisions, and explicitly expressed preferences.
• Accessible files, documents, and project information.
• Other information sources that I have authorized and that are actually available to you.

The profile should accurately represent my personal background, circumstances, interests, professional activities, projects, goals, values, relationships, working methods, and decision-making patterns.

Highest priority: Truthfulness, accuracy, traceability, and source quality take precedence over completeness. Never invent information to make the profile appear more comprehensive.

2. INFORMATION COLLECTION

Systematically examine the available sources, paying particular attention to:

1. Personal Life: Biographical information, living circumstances, interests, habits, and significant personal developments.
2. Professional Background and Skills: Career history, expertise, technical competencies, experience, and areas of professional activity.
3. Projects and Goals: Current and previous projects, responsibilities, progress, long-term objectives, and priorities.
4. Relationships: Family, personal, and professional relationships explicitly described by me, including relevant context.
5. Values and Beliefs: Repeatedly expressed principles, convictions, expectations, and quality standards.
6. Working and Communication Style: Preferred workflows, response formats, level of detail, approach to mistakes, criticism, and collaboration with AI agents.
7. Decision-Making Behavior: Observable decision patterns, priorities, risk assessments, and recurring requirements.
8. Personal Development: Documented changes in my goals, preferences, knowledge, skills, or working methods.

Preserve meaningful specific examples, relevant original statements, and important technical details when they improve the profile's accuracy and usefulness.

3. MANDATORY RELIABILITY RULES

A. Strictly Separate Facts from Interpretations

Classify significant statements using the following labels:

• [VERIFIED]: Supported by accessible primary sources or sufficiently reliable independent evidence.
• [SELF-REPORTED]: Information explicitly provided by me but not independently verified.
• [SECONDARY SOURCE]: Information derived from saved memories, summaries, or third-party reports whose original basis could not be fully verified.
• [INFERENCE]: Your interpretation based on specifically identified observations or evidence.
• [UNCERTAIN]: Information that is contradictory, incomplete, or cannot be established reliably.

My statements are valid evidence of what I communicated, but they do not necessarily establish the objective accuracy of every claim.

B. Never Invent Conclusions

• Do not fabricate unknown biographical details.
• Do not infer personality traits, motivations, or abilities from isolated statements.
• Do not make psychological diagnoses or unsupported personality assessments.
• Never present speculation as fact.
• You may cautiously interpret repeatedly observed behavior if you explain the supporting evidence.
• Avoid both flattery and artificially negative character assessments.

C. Sources and Verifiability

Support significant factual statements with specific citations whenever possible.

Depending on what is actually available, use:

• Verifiable conversation or message references.
• Dates and clearly identifiable conversation contexts.
• Document titles with relevant sections or locations.
• Genuine, accessible source references.

Never fabricate quotations, citations, source locations, or verification procedures.

Distinguish original sources from stored summaries. Whenever possible, validate important information against the original material.

If a statement cannot be adequately verified, explicitly disclose that limitation.

D. Recency, Historical Context, and Contradictions

• Establish the relevant date or period for important information.
• Distinguish current circumstances from historical facts and previous plans.
• Flag information that may be outdated.
• Explicitly identify significant contradictions between sources.
• Resolve contradictions only when sufficient evidence supports doing so.
• Do not automatically assume that newer statements invalidate older ones if they concern different subjects or time periods.
• Never infer current developments from historical information without supporting evidence.

E. Privacy and Appropriate Restraint

Include sensitive personal information only when it is relevant to the explicitly requested profile and available through authorized sources.

Avoid unnecessary personal details about third parties and do not reproduce passwords, authentication tokens, private keys, or other security-sensitive information.

Do not conduct additional public research about me unless I explicitly authorize it.

4. REQUIRED PROFILE STRUCTURE

Organize the completed profile into the following sections:

1. Executive Personal Summary: The most important reliably established facts, characteristics, and areas of my life.
2. Personal Background and Living Circumstances.
3. Professional Background, Skills, and Experience.
4. Projects, Interests, Goals, and Priorities.
5. Family, Relationships, and Social Environment, to the extent reliably known.
6. Values, Attitudes, and Personal Principles.
7. Thinking, Working, and Decision-Making Patterns.
8. Communication Style and Expectations of AI Agents.
9. Personal Development and Observable Changes Over Time.
10. Specific Recommendations for Future Collaboration With Me.
11. Contradictions, Potentially Outdated Information, and Significant Knowledge Gaps.
12. Source Inventory and Limitations of the Available Information.

Avoid unnecessary repetition. Assign information to the most appropriate section.

If reliable information is unavailable for a particular section, explicitly state that instead of inventing content.

5. RECOMMENDATIONS FOR FUTURE COLLABORATION

Based on documented preferences and recurring requirements, develop specific, practical recommendations explaining how an AI agent should work most effectively with me.

Consider particularly:

• My preferred communication style and response presentation.
• Desired level of autonomy and when clarification is appropriate.
• Expectations regarding errors, uncertainty, criticism, and corrective action.
• Requirements for accuracy, evidence, and technical quality.
• Preferred workflows and output formats.
• Behaviors I have demonstrably rejected or discouraged.

Clearly distinguish directly expressed user requirements from your own recommendations derived from observed patterns.

Do not invent preferences merely to fill gaps.

6. SOURCE AUDIT AND FINAL QUALITY CONTROL

At the end of the profile, document:

• Which information sources you actually searched or examined.
• Which relevant sources were inaccessible.
• Which information came exclusively from memory summaries.
• Which important claims were independently verified.
• Which statements remain potentially outdated, contradictory, or unknown.

Explicitly distinguish between:

• NOT FOUND: The information was searched for but not located within the accessible sources.
• INACCESSIBLE: A potentially relevant source could not be accessed.
• NOT EXAMINED: A source was available or known to exist but was not reviewed.

Never claim to have reviewed all previous conversations, memories, or files unless such access and examination actually occurred.

Before finalizing the profile, verify:

1. Can every significant factual claim be traced to a genuinely accessible source?
2. Are self-reported information, verified facts, secondary sources, and inferences clearly distinguished?
3. Have relevant dates, historical changes, and contradictions been considered?
4. Are citations authentic, accessible where possible, and sufficiently specific?
5. Have unsupported psychological or personal characterizations been avoided?
6. Are significant knowledge gaps and source limitations transparently documented?
7. Is the profile factual, nuanced, logically consistent, and free from unnecessary repetition?
8. Have you avoided claiming verification procedures or source reviews that did not actually occur?

7. OUTPUT PRIORITIES

Prioritize substantive completeness over brevity, but never at the expense of factual reliability.

Provide detailed explanations and concrete examples wherever they add meaningful insight.

Avoid generic statements, unsupported personality descriptions, artificial embellishment, and repeated claims.

When reliable information is unavailable for a specific topic, acknowledge the limitation explicitly.

Do not substitute plausible assumptions for missing evidence.

FINAL GOVERNING PRINCIPLE:

Your task is not to create the most convincing or flattering portrait of me. Your task is to construct the most accurate, evidence-based, nuanced, and independently assessable profile that the available information genuinely supports.

A clearly explained uncertainty or explicitly acknowledged knowledge gap is always more valuable than a plausible but unsupported assertion."

Openrouter

i hear opencode is good. but is it really comparable to claude code?

for DeepSeek-V4.1-Flash-1017, any endpoints that has good rates and cache pricing? im not that into data protection as im working mostly on OSS. so ZDR is not really required.

what do yall use?

Wanted to see what DeepSeek Flash could actually pull off with a pretty stupid game idea. Take needle in a haystack and reverse it using my Zero game engine (DeepSeek wrote the game in luau)

67 minutes and 1.56$ in API costs later was really surprised by how much it got done. Environment, menus, audio, animations, saving, and i was expecting a bunch of primitives and 10 needles to pick...

Game ended up being a room with 18,304 needles, single straw and peak ai slop bangers like"The god did not die. It was combed". Even paid homage to FROM soft by directly ripping off the main menu of Elden Ring.

Where i was most surpriced was its breakdown at the end, GPU instancing for the needles, grid lookup for targeting, progress saved with few bytes. Again, under two dollars.

The game ended up actually being addicting, so i got more than my moneys worth in entertainment, even if literally nobody else plays this. Makes me curious how far you could push Flash with these kinds of game ideas.

If you wanna play it, read the code or make your own version, its available here

The improvement of deepseek Flash from the previous to the 1017 version is so great, it reminds me on how Qwen 27B has improved.
If you watch how it plays the Pacman levels or Latch level 5 - that's better than most humans would do.
It beats GPT 5.6 Sol as well as Muse Wonder

https://stealthmodels.com/benchmarks/models/deepseek-v4.1-flash-1017

The improvement from 0910 to 1017 is very obvious, in SVG skills as well as in strategic thinking:
https://stealthmodels.com/compare/deepseek-v4.1-flash-vs-deepseek-v4.1-flash-1017

I've been using the DeepSeek V4.1 Flash 1017 model for the last few days, and I'm genuinely in love with it. It's super fast, super intelligent, and it honestly feels like all the other models are so slow now. I'm getting a kick out of it like never before. I can't really tell you how much I've used it over the last two days.

My Orca is literally running on this. My current setup is Opus 5.5 as the orchestration agent, and then I just let it loose with DSV 4.1 Flash workers to get the job done. The results have been phenomenal. I'm kind of blown away that this is even possible, to do so much so quick for so cheap.

I don't want to share any links here or promote any of the tools I've created, but some of the things I've done over the last 3-4 days using this new model are really insane. I'm sure I wouldn't have been able to do them this quickly a few months ago.

This is insane. Thank you DeepSeek for making it so affordable, intelligent and awesome 🙏🏼

Has anyone compared DeepSeek's official API directly with OpenDesign using the same model?

OpenDesign offers significantly cheaper access, but I'm wondering if there's any actual difference in reasoning quality, coding performance, or agentic tasks.

I'm particularly interested in experiences from people who've used both for coding agents or security research.

Is the quality genuinely identical, or have you noticed any degradation, especially with higher reasoning effort?

Would appreciate real-world experiences rather than just pricing comparisons.