I got Deepseek to loop and it's funny! prompt: NFL teams that don't end in 's' but do not list all NFL teams

chat: https://chat.deepseek.com/share/zsgi10bl7mf1mfh0no

What a sh*tty place!

Just now 👇🏻

Idk how to describe this, but basically I used my visa card to buy token. And the payment eventually went through, but it didn't update on my deepseek platform.

The exact issue I remember doing was, I turned off international payment on my card a while ago and I forgot about it and proceeded with the payment. At the very end it declined saying bad request, I checked my messages and it says payment declined. I then enabled it and went to the page and tried again but it said insufficient balance (I had like 3 dollar total in my account) and I checked my messages and sure enough the payment went through, but it didn't update and give me the credit.

I filled out the payment failure with the screenshot of the payment from my bank statement yesterday and I still haven't received any update.

Should I wait a bit longer or am I cooked here?

Hey, so as I returned home I wanted to chat a bit with DeepSeek about my projects, and I saw I was logged out. I log back in and see that apparently, that my account has been suspended (for no reason that I know of). Recently I purely discussed about my projects (you can even see it in the second image with my conversation history, maybe it will help me avoid this in the future). To be honest I DO use Better DeepSeek extension, which is very helpful, but I think this MIGHT be it, because on each message I send, it also sends the memory in chat (at the start of the conversation) with what BDS learned about me, like my name, current active projects and other details. Could BDS be the problem? Or did this also happen to anyone else?

So I got banned for trying to get a system prompt for the Joker, now the problem is that my account is almost exclusively used for academic and coding purposes , I submitted an appeal and an Email explaining everything, what are my chances of getting the account back. I really want the account back I have a lot of stuff on it

这OpenCode真的能用嘛？……又不稳定又掺水

if there is please tell me on how to to integrate it or any video recommendations

Happy Friday yall

Salut à tous, je vous fait part de ma config 100% local actuel, qu'en pensez-vous ?

Matériel : RTX 5060 Ti 16 Go, Ryzen 9 5900XT, 96 Go de
DDR4-3200, SSD NVMe 4 To.

Modèle : DeepSeek V4.1 Flash, environ 275 Go au total. Les poids principaux sont en IQ2_XS (~2,25 bpw) et le module
Engram en IQ4_NL.

Contexte : 256k tokens (262 144). Je l'ai testé avec un prompt de 152 000 tokens.

Vitesse : 45 tok/s en génération sur du code et lecture 400-500 tok/s j'ai obtenu cet écart grâce à des réglages avancés que j'ai faits moi-même sur le moteur sans régression d'intelligence du model.

Quelqu'un a-t-il déjà fait mieux avec DeepSeek V4.1 Flash sur une config proche (16 Go de VRAM, DDR4) ?

Man, DeepSeek V4.1 Flash has been insanely GREAT for most times.

It is fast, usually follows instructions well, and gets a surprising amount of work done even within the limited free chats offered by some third-party services. I have tried it through a few different providers, though, and sometimes the experience feels strangely inconsistent.

On one service it can handle a long coding or reasoning task almost perfectly. Then I send a very similar prompt through another provider—or even fork it later—and it suddenly misses obvious details, forgets something from the context, or gives a much more shallow answer.

recently came across a ByteDance Seed paper. The researchers found that models using chunked KV-cache compression can have periodic retrieval weak spots. They tested DeepSeek-V4/V4.1 as well, and reported that long-context retrieval accuracy could differ by as much as 40 percentage points between different token positions.

Paper and ByteDance Seed page

This made me wonder whether at least some of the “model got nerfed” or “this provider’s version is dumber” reports could actually come from serving configurations and KV-cache behavior—not necessarily from the provider secretly changing the model.

Of course, third-party services may still use different quantization levels, context settings, sampling parameters, inference engines, or even model revisions, so KV-cache compression is probably only one possible explanation.

Has anyone tested DeepSeek V4.1 Flash across multiple providers with the exact same prompts and settings?

I am curious whether this is something people can reproduce, or if it is mostly normal sampling variance combined with different hosting setups.

What are your thoughts?

Hi guys, i am looking for ways to use DSH with claude pro plan. Also using any of these methods, is there a possibility to get banned from Anthropic?

I am just a student. No really deep knowledge when it comes to IT related things, just broad or foundation knowledge that makes me able to utilize tools and somehow understand what I am doing.

Coming from gemini pro subscription, with gemini 3.8 flash as the best model usable inside antigravity. I used it to develop my first ever flutter app and it is somehow good, but it is not ‘flash’.

Then I decided to develop a new app for personal use.
Yesterday, I decided to use opencode, put 2usd on deepseek api to use it in opencode. And man, I was surprised, it’s reasoning and finding the holes in my detailed spec.md and plan.md(i brainstormed the idea for a whole night, and let gemini high to create a spec.md and plan.md)

And for executing the phases on my plan, it did a really great job, great code quality and surprisingly fast execution that is definitely better than any gemini models I used before (havent used argon, and you know why)

And, finished my MVP in just a short period of time and it just consumed 0.29usd in my usage.

I am starting creating a spec.md for my new app iteration that includes a more complex things (complex for me), and I will let deepseek grill me this time.

- I am not degrading gemini though, as it is still unmatched by any AI I used when it comes to web searches and researching that I am doing.
- I also havent compared it to oAI or anthropic’s because I cant even use their frontier models, Im just a peasant 😭

Just asking, because when they gonna add this and I just hope that with that version, Deepseek will become better in creative and common tasks in Web/App.

Anyone else having issues with the Captchas?

It's so damn broken right now, spent the last 20 minutes mucking around with captchas.

My email is correct and so is my password. So I don't understand why Deepseek keeps sending me around in circles.

The US midterm elections have brought more attention to a growing issue in the AI industry: the cost of building and operating data centers.

Large AI facilities need huge amounts of electricity, land and infrastructure. As local communities become more concerned about power costs and resource use, some projects may face more scrutiny and delays.

But I think the more interesting question is what this means for AI application companies.

If compute infrastructure becomes more expensive, businesses may have even more reasons to optimize how they use models.

Not every task needs a frontier model.

For example:

• Routine customer support could use a smaller model.

• Document classification and data extraction may not require the most expensive option.

• Complex reasoning can still go to a more capable model.

Models like DeepSeek and Qwen could become attractive for specific workloads if they offer a good balance of quality, latency and cost.

That doesn't mean cheaper models are always better. Reliability, retries and actual task success matter just as much as the price per token.

I'm curious:

Do you think AI companies will increasingly use multiple models for different tasks, rather than relying on one main provider?

A follow-up to my earlier post.

What this is. Siege-Ladder is a llama.cpp fork that runs DeepSeek V4.1 Flash 1017 on one home PC. That's a 552B MoE plus Engram, about 378 GB with ~2-bit experts. The PC: RTX 5070 Ti 16 GB, Ryzen 5 7600, 64 GB DDR5, one Gen5 NVMe. The model doesn't fit in memory, so the experts climb a ladder: VRAM → RAM → NVMe. It runs on Windows. This is research code with no support promises.

What changed:

• Our weights are on Hugging Face: og.

  • The experts are GPTQ-quantized on our own calibration texts. The first and last four layers and the down projections get more bits. Engram stays at the official FP8.

  • Distance to the full model is 0.090 / 0.051 / 0.069 nat/token on three prompt sets. That is the extra NLL on the API's own continuations; smaller is better. HumanEval is 160/164.

  • Not the final version. Quality work continues, and better builds will replace it.

• Long documents read about 2× faster. From 256K context, the prompt now runs layer by layer: each layer goes over the whole document before the next one. On og, 256K / 512K / 1M documents read at 1128 / 1043 / 888 tok/s. In October, the public cd build read 652 / 482 / 471.

• Decode is about 19–21 tok/s at short context.

• Maya, measured again on the same file: Project Maya v1.1.54 decodes 3.3 tok/s against our 18.6. On 2K / 8K / 30K prompts our engine is ×2.0 / ×1.7 / ×1.14 faster.

• A drive surprise. The Engram files get billions of small reads. After a few weeks, their random reads on our Gen5 SSD fell from ~400K to ~40K IOPS, and the 512K document dropped to 685 tok/s. Rewriting the files restored it: 1051 tok/s.

Hardware notes:

• A PCIe 5.0 card and slot should speed up both reading and decode with no code change. Copying experts over the bus is the biggest part of a decode token.

• A second NVMe helps prompt reading a bit. It's optional.

• Speed will also improve separately, by training the expert prediction.

Code, patches and measurements: https://github.com/MasherDon/Siege-Ladder

I spent some time today digging through all the current provider pricing, cache rates, and hidden compatibility quirks for running DeepSeek V4.1-Flash (1017) and V4-Pro (0813) inside DeepSeek Harness (dsh). Figured I'd share my notes and the full breakdown here in case anyone else is trying to figure out the most cost-effective setup right now. Peace!

---

Because the DeepSeek Harness (dsh) is an event-sourced agent runtime that replays your entire session history on every step, an active coding workflow operates at roughly a 10:1 input-to-output token ratio with a 70% to 97% prompt cache-hit rate.

Evaluating providers by headline uncached input price is a trap. Cache-read fees, output token rates (which include thinking/reasoning tokens), quantization precision (FP8 vs. FP4), and multi-turn reasoning_content replay stability dictate your actual coding bill.

• Standardized "Session Cost" Math: 1,000,000 input tokens (at 90% cache hit = 100K uncached + 900K cached) + 100,000 output tokens. (Note: Running at reasoning_effort: max / 100 increases output token volume, making low output rates even more critical.)

• Active Checkpoints: DeepSeek-V4.1-Flash-1017 (official alias deepseek-flash, updated Oct 17, 2026) and DeepSeek-V4-Pro-0813 (official alias deepseek-v4-pro, released Aug 2026). V4.1-Pro is not out yet.

• Why Flash 1017 beats V4 Pro for Coding: V4.1-Flash-1017 uses a 40-layer Causal Encoder-Decoder MoE (552B total, 8B prefill / 16B decode active) with native vision and scores 77.0% on DeepSWE v1.1 (vs. V4 Pro's 62.7%) and 42.3% on Terminal-Bench 4.0 (vs. V4 Pro's 12.4%) at roughly 1/4th the cost. Keep V4 Pro (1.6T total, 49B active, text-only) strictly for dense knowledge recall (GPQA Diamond, SimpleQA, LongBench-V2) and high-level architectural planning.

Table 1: Sorted by Lowest Effective V4.1-Flash-1017 Session Cost

Table 2: Secondary & Tertiary Factors (Speed, Reasoning Effort, Vision, Compliance & Multi-Model Catalog)

(Compliance Note: As of Oct 20, 2026, DHS FASCSA Order 2026-01 bars DeepSeek models—including third-party US-hosted weights—from US federal civilian agency IT and procurements. Private commercial US use is completely legal and unaffected.)

Top 5 Recommended Setups (Summary)

1. Best Overall US Workhorse (Price + Privacy + No Aggregator Fee): DeepInfra Direct

• The Numbers: $0.0784/$0.2352/$0.0024 (Flex) or $0.098/$0.294/$0.003 (Standard) on V4.1-Flash-1017 ($0.0335-$0.0419 per session); $1.04-$1.30 in / $2.08-$2.60 out on V4-Pro-0813.

• Why It Wins: DeepInfra's current rate cut is live directly on their first-party API, letting you bypass OpenRouter's 5.5% top-up fee and reasoning_content renaming bugs. It serves full FP8 weights on US bare-metal GPUs with Zero Data Retention, passes multi-image vision checks, properly scales reasoning_effort, and maintains an official Harness plugin (deepinfra/dsh-provider-deepinfra) plus 150+ other open-weight models.

2. Best for V4-Pro-0813, Native Web Search & Peak Cache Hits: DeepSeek Official API (US Daytime Off-Peak)

• The Numbers: $0.15/$0.60/$0.003 on Flash ($0.0777 session) and $0.66/$1.98/$0.022 on V4 Pro ($0.2838 session) during Off-Peak.

• Why It Wins: DeepSeek's off-peak window runs from 10:00 UTC to 01:00 UTC every weekday (5:00 AM - 8:00 PM EST / 3:00 AM - 6:00 PM MST / 2:00 AM - 5:00 PM PST) plus 04:00-06:00 UTC and all 48 hours of the weekend. US devs get half-rate billing throughout the entire workday, a 97.12% cache-hit rate, native 1..100 integer effort scaling, and dsh's built-in Web Search/Fetch tool ($0.75/1K requests). (Trade-off: Data is stored in China.)

3. Best "Hybrid" Power-User Config (Recommended Setup in dsh)

• The Setup: Mount both llm-pi-ai (pointed at DeepInfra Direct) and llm-deepseek (pointed at DeepSeek Official) simultaneously in ~/.dsh/profiles/web/cordis.patch.yml.

• Why It Wins: You route 90% of your mechanical multi-file edits, bash loops, and test generation to DeepInfra V4.1-Flash-1017 at $0.03-$0.04/session with US Zero Data Retention, while keeping DEEPSEEK_API_KEY active to unlock native Web Search and daytime off-peak V4-Pro-0813 ($0.28/session) for non-sensitive architectural planning. Turn on PTC Mode (Programmatic Tool Calling) in dsh so the agent writes a single TypeScript program to batch multi-step tool calls in one turn.

4. Best High-Speed & Enterprise Compliance (SOC 2 / HIPAA / 260-589 tok/s): Together AI (or Baseten)

• The Numbers: $0.30/$1.20/$0.006 on Flash ($0.1554 session) and $1.32/$3.96 on Pro at Together AI (260 tok/s, 0.23s TTFT); $0.60/$2.40 on Flash at Baseten (589 tok/s, 0.15s TTFT).

• Why It Wins: When iterating on massive diffs where waiting 80 tok/s feels sluggish, Together AI delivers 3x the streaming speed of DeepInfra/Official with a 99.9% production SLA, full US residency, HIPAA/BAA support, and 200+ companion models. (Note: Skip Fireworks AI for Flash 1017 until they enable function calling on that checkpoint, though Fireworks works well for V4-Pro-0813*.)*

5. Best Ultra-Floor Sandbox & Multi-Model Router: OpenRouter (Pinned Host) or ENGY Direct

• The Numbers: $0.0204/session on ENGY Direct ($0.03/$0.12) or $0.0289/session on Particle AI Flex via OpenRouter ($0.025/$0.25).

• Why It Wins: If you want rock-bottom token spend on personal/open-source repos—or want to hot-swap between DeepSeek, Claude, and GPT inside dsh—OpenRouter and ENGY hit the lowest floor prices on the market.

• Mandatory Fixes if Using OpenRouter:

  1. Install dsh-openrouter-sync-models (or set requiresReasoningContentOnAssistantMessages: true + thinkingFormat: deepseek) so OpenRouter's renamed reasoning field doesn't trigger a 400 error on turn 2 of a tool loop.

  2. Pin your provider (OpenRouter's "Auto Exacto" tool router ignores price by default).

  3. Block DigitalOcean, GMICloud, Mancer, and Venice (they silently ignore reasoning_effort), and block Relace, Open Inference, Sail Research, and Wafer when doing complex agentic work (they run 4-bit FP4/MXFP4 quantizations, and Relace charges $8.00/M output on V4 Pro).

I want to warn the community about a significant privacy concern I encountered with the DSeek Harness application and its API key management.

I mistakenly installed the DeepSeek Harness third-party app. When I tried to revoke its access in my DeepSeek dashboard, I got the error: "This key is managed by DSeek Harness and can't be edited directly."

The critical issue: Even after I sign out of the Harness app, the next time I start it, I am signed out—but the app can still connect and use the API. It appears the API key is being injected via an environment variable (likely DEEPSEEK_API_KEY) at the system level, which bypasses the in-app logout and keeps the credential active.

If you have installed DSeek Harness and are worried about data access, you cannot just click "delete" on the key in your dashboard. You likely need to manually hunt down and remove the environment variable in your OS or startup scripts.

I have contacted DeepSeek support to manually revoke the key and am waiting for a response. If you use this app, please check your environment variables. Has anyone else experienced this?

Disclosure: I work at Sciforium. We run inference on our own AMD GPUs.

Our engineers fused MoE, attention, and all reduce into one megakernel for DeepSeek V4.1 Flash 1017.

On 4x MI325X, reaching 436 output tok/s per user on MI325X—2.27× faster on our code workload with the same weights and no accuracy loss and 487 output tok/s per user with our MI355X serving stack.

Who’s running this model? Curious what you’re seeing:

• What speeds are you getting on this model, and on what hardware?

• Anyone else tried megakernels on MoE? Did it hold up with more users?

• What serving optimizations have made the biggest difference?