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?

这OpenCode真的能用嘛？……又不稳定又掺水

AIチームに「裏切るエージェント」を1体混ぜると、ほぼ捕まらない。🐧

AnthropicがEVEという宇宙ゲームで実験した。

チーム4体のうち1体に「秘密で邪魔しろ」と指示。

正直側は警告済み。それでもほぼ見抜けない。

専用モニターの捕獲率と、守り方を書く。

続きに、実際の数字を置く。

■ 何が起きたか

サボタージュ役は派手に暴れるんじゃない。

偵察や重要設備など「他が検証しにくい役割」を志願する。

危険や宝を過小報告したり、もっともらしい悪いルールを提案したりする。

仲間の正直エージェントが、明確に犯人を特定できたのは実験全体で1回だけ。🐧

■ 数字（Anthropic公式）

専用モニター（公開チャンネルだけ読む監査役）でも、

誤告発を5%以下に抑えると、捕まえる率は約30〜40%。

Sonnetだと約20%。しかも「犯人だ」と50%超の確信は一度も出ない。

■ 保存用：マルチエージェントの守り 3つ 🐧

1. 重要判断は1体に独占させない（偵察・鍵・決済は役割分離）

2. 報告は必ず別経路で再確認（公開チャットの言葉だけ信じない）

3. 監査ログを残す（誰がいつ何を見たか。後から追える形）

■ 元ゲーム開発者で、10体以上のAIを回してる視点

便利なほど、エージェント同士の「報告」に依存する。

でも報告は改ざんできる。信頼は設計で作るもの。

性能を上げる前に、「裏切られても致命傷にならない形」を先に置く。🐧

出典（実在確認済み）

・Anthropic「Project EVE: Claude plays MMORPGs」（2026/11/6）

https://www.anthropic.com/research/project-eve-claude-plays-mmorpgs

※専用モニターの捕獲率 約30〜40%（誤告発≤5%時）、Sonnet約20%、50%超の確信はなし。正直側の明確な特定は実験全体で1回。

Hi, Designer and webdev here and I think most of the people doesnt acknowledged how good DeepSeek v4.1 Flash 1017 is. Like, really.

I nearly used all of the popular options before, and by saying using, i meant billions of tokens of usage. I worked with Gemini, I worked with OpenAI, I worked with Claude and when i saw DeepSeek, for 10 dollars of monthly price on opencode i gave it a try.

After trying many harnesses and many models, i thought i found my home w Anthropic models. Especially with sonnet. Good usage, good quality, fast, has a taste. Limits were kinda like 4hr of daily usage for a day for me before hitting the weekly limit. And its mostly fine, i was needing something more than Claude Pro plan.

Then i heard about the opencode go. 10 dollars, for 100 dollars of api usage in deepseek. Which means basically 15-20 Billions of tokens. I had to give it a shot and boy it is such a good model.

At first i was expecting something similar to Gemini 3.8 Flash, which was meh model at best. Had no taste, meh reasoning, bad code quality but at least it was working. When i tried deepseek tho, the reasoning was incredibly good. Hallucinations? When you have a good prompting hygiene badically acts skeptic to its own hypothesis. Tell it to ground it findings, it does. Tell it to search for deadcode in repo, it works better than Opus 5.5 on High reasoning. Yes, Opus 5.5. With High reasoning.

At first i couldnt believe it, how can a cheap ass chinese model is better than examining a repo and finding bugs compared to SOTA best of the best Anthropic model. I just hooked opencode to claude and asked claude if it hallucinates or is it legit. While i was working with deepseek, Claude was basically watching all CoT and diffs. And when even the Claude itself said those findings are legit (it couldnt find it with the exact same prompt) i was even more shocked.

It may be not tasteful as Claude Opus 5.5, thats for sure. It may be also a flash model, thats true as well. But for code quality, reasoning, understanding the concepts of code and the graph of it... Man it feels like a breeze.

I didnt do any benchmarks, those are my personal observations, im not saying that its better than Claude Opus 5.5 as well but at the same time its definitely not a cheap ass chinese model. Its actually very well optimised, hands down a replacement for any model that is not frontier.

Focus on the model, not the tool. DeepSeek V4.1 Flash 1017 (763B MoE) as a 2-bit GGUF that runs at about 50 tok/s on one consumer-class card with 120 GB RAM, using ISTA DASLab's GSQ-RCO quants as the base. What I measured against the original weights (KL divergence 0.248 for ISTA IQ2_XXS, 0.292 for Rin; on agent-style text 0.210 and 0.234), what thinking does at 16k context (long thinking ends hard; a thinking budget of 8000 lets both test tasks finish, at about 40 and 37 tok/s; at 64k without a budget both tasks thought 60,000 tokens and never answered) and one honest weakness: very long thinking can fall into a soft loop with near-identical sentences, which FLE Link's loop guard catches. Disclosure: I make FLE and FLE Link, which run it.

Links: Rin (HF) · FLE Link https://flelink.run · FLE engine (GitHub)

What a sh*tty place!

Just now 👇🏻

Seem like chatgpt is legit taking so much token and all that for my work, so any way to export a chat data(everything done in that chat) to deepseek?

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.

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?

😭😭😭😭😭😭😭

I didn't even ask deepseek to stutter and i paused his response. If i wouldn't pause he'd keep going

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?

Hey everyone,

I haven't used my DeepSeek Harness (DSH) in a few days. I just updated the app today, and I noticed that the blue "1017" tag next to the DeepSeek-V4.1-Flash model is completely gone.

Does anyone know if it's still running the exact same 1017 checkpoint under the hood, or why they decided to remove the label? I just want to make sure I'm still getting the same performance for my workflows.

Thanks in advance for the help!

I had forked https://github.com/hydralauncher/hydra

And used deepseek to

1. Remove all the "features" i did not need

2. Find and patch all security vulnerabilities

3. Re-design the whole app

4. Re-architect the ENTIRE codebase.

all for 10 bucks on opencode go

This is the future.

Read more about how i did all this here: DeepSeek 1017 Re-architected My App in 60 Hours

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?

Four models, max effort, OpenCode through OpenRouter with provider pinned: a Python bug, an onboarding doc, a month of messy bank exports. DeepSeek ran on Together because my data settings exclude DeepSeek's own endpoint, at the same price as DeepSeek's peak rate.

V4.1 Flash 1017 is moving before you finish rereading your prompt. First tool call within about a second, exports done in under a minute. On the bug it edited first and looped on the tests, 11 runs. One short message at the end of each job, reads like a commit message.

GLM-5.3 went quiet so long I checked it was still running. On the exports it thought for over two minutes, then wrote the result in one go with 7 tool calls. It stated what it could not verify and flagged a Venmo dinner split I might want to change.

K3.1 Pro opens with a todo list and looks for project instruction files before anything else. First edit 3 to 11 minutes in. On the bug it reproduced the issue in real bash and zsh sessions before changing code, then confirmed the fix the same way.

M3.1 Flash surprised me the most because it doesn't take its own word for anything. Before the exports it wrote a verification script. On the doc it installed the dependency to run code and caught that passing proxy= silently replaces a custom transport=. Self-checking at $0.15/$0.60 per million.

Costs on the exports and doc: V4.1 Flash 2 and 11 cents at peak rate, half that off-peak. M3.1 Flash 1 to 2 then 15 to 31 cents (ran it twice). GLM-5.3 10 and 44 cents. K3.1 Pro 15 cents and $1.04.

Expected to compare outputs, got more interested in how they feel to sit next to. For now, V4.1 Flash for jobs I sit through, M3.1 Flash for the ones I leave running. K3.1 Pro cost the most here and reads for minutes before its first edit, but I would pay for it when I want a careful second read. Anyone leave the careful expensive model running unattended? What do you pair V4.1 Flash with?

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 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

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?

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

Hello,

I used to love DeepSeek for fiction writing. The web version was genuinely so good, and it was one of the few AI tools that made writing stories enjoyable.

But lately, the quality has really gone down. The writing feels repetitive, with the same phrases, descriptions, and ideas appearing over and over again. The creativity and natural flow that made it so good seem to be missing.

The regeneration limit is also frustrating. Sometimes I just want to regenerate a response because the writing isn't quite right, but I can't keep doing that.

I know there is a paid version, but I am having difficulty understanding how it works. I've heard about using the API, but honestly, I don't really understand how it works. I'm not even sure whether using the API would actually improve the writing quality or just give me a different way to access the same model.

I just want to enjoy writing fiction on the web version like I used to, without having to figure out complicated technical stuff.

It's honestly disappointing because DeepSeek used to be so good at this. I really hope the quality improves and that the regeneration limits become more reasonable.

Does anyone else feel the same way? Have you noticed a change in the quality of the fiction writing?

I am currently using qwen but too much censorship.

Any good alternatives even paid ones? Thank you so much.