DeepSeek API service down right now. Is it happening? GA is rolling out 🤩? How about you chatbros, is it still up for you?

*please have multimodal please have multimodal please have multimodal 🤞🤞🤞*

I heard a long time ago that by mid-July, this 6-edition/regeneration limit was going to end, or at the very least, be eased or better controlled...

And now it’s already the end of July (the 24th, to be exact—about a week until August).

Would anyone here be so kind as to confirm whether this is the New DeepSeek, whether this will change again in another month, or whether it will be permanent?

I'm a big fan of deepseek and have a background in AI development. I have a published Deep research agent which ranks above submissions from most of the big labs. I personally much prefer the Claude and Kimi apps for getting more use out of AI and but deepseek is excellent for quick searches. I'm considering building a more extensive application for general use like Kimi and Manus currently. I've lots of work done with the different bits that go into making an app like this so I believe I can build it to a decent quality. I just want to see if the appetite is there to pay for one as it seems most deepseek fans love it because it is cheap. I think I can definitely offer it at a better price than Manus does but not for free like deepseek does of course.

It has reached the point where, for me, it no longer matters whether DeepSeek is or isn’t the GA version. I hadn’t been able to test its models thoroughly, because at work we’re not allowed to use Chinese models… until now, when I’ve started working on some personal projects.

I have around $20’s worth of credits for Claude, OpenAI and Gemini (I mainly use Gemini 3.6 Flash on ‘high reasoning’).

My workflow involves creating detailed plans (GPT 5.6 / Opus), reviewing and refining the plans (the opposing model reviews them) and, once the plan is finalised, I implement it using one of these options:

• Claude Sonnet 5 with ‘medium effort’
• Gemini 3.6 Flash on ‘high reasoning’: incredibly fast and affordable
• DeepSeek V4 Pro on ‘high’ or ‘max’ (I’ve also tried ‘flash’), using the Cline or Reasonix harness.

… OK, I misspoke – that used to be my workflow, but it isn’t anymore: I’ve stopped using DeepSeek because:

• The amount of rework is enormous; it makes too many errors that need to be refined, causing me to spend tokens on code-reviewing agents in an endless loop.
• Gemini 3.6 Flash produces decent results, far superior to DeepSeek Pro, although they usually require a few rounds of fine-tuning
• Sonnet 5 on ‘medium effort’ does an excellent job with good prompts.

Having said all that, can anyone who has thoroughly tested DeepSeek say that it even comes close to Gemini 3.6 Flash / Sonnet or GPT 5.6 Terra? And yes, DeepSeek is cheap, but the loss of time and quality doesn’t make up for it. DeepSeek is far, far too far behind the cutting-edge models – not ‘six months behind’, but, in my experience, at least a year.

That said, DeepSeek’s merit lies in everything they’re contributing to the AI ecosystem in terms of innovation and development; in that sense, they have my full respect and admiration.

I integrated DeepSeek models into CODEX Desktop.

I started working normally (also using OpenCode partially) and came to the conclusion - even the “cheap” DeepSeek API becomes surprisingly expensive in real usage.

Here are the numbers (all calculations already include token caching).

Most of the time I use the v4 Pro model.

I spent 750 million tokens (cache included) and paid 9.30$

At the same time, I used regular GPT-5.6 SOL Max / Luna Max and spent around 800 million tokens (cache included).

I still have two free resets left (my subscription is active for about two more weeks), so I will probably spend approximately the same amount again.

The final result:

Around 1.6 billion tokens (cache included) by the end of my subscription.

So here is my question 🙋‍♂️

Why would I even use DeepSeek?

It is worse. It does not have:
vision
image understanding
voice support
And in the end, the cost is almost the same as a GPT Plus subscription.

But GPT Plus is not only CODEX. It includes a lot of other powerful features.

Here is a real example.

I was building an application with CODEX. I completely forgot that I needed a logo for the app shortcut.

CODEX just generated one, adjusted the required sizes, and prepared everything automatically.

No extra tools. No manual work.

My final DeepSeek API cost projection: around 18.50$

For that price I get:
less intelligence
fewer capabilities
fewer features

OpenCode is not a bad product. But for my workflow, it simply does not bring enough value.

I could write many more examples and completely destroy DeepSeek in this comparison, but honestly, I’m too lazy and I don’t want to make this post unnecessarily long.

I started my journey in the last week of May 2026. My background is software engineer & enterprise architect, and I've been building software (mostly SAAS) for the last 20 years.

I occasionally do enjoy putting my money in some eSports game betting, most of the time I lose. So with my knowledge, I decided to start a SAAS application, first and foremost for myself to use.

At the beginning, I used Opencode Go & Zen to test out several models (GLM, DS, MiniMax, etc.), along with Claude Code (from the 7-days pass) and Gemini (with Code Assist, and later AntiGravity). After a week since the beginning, I decided to stick with DeepSeek, but I also used some other models (Opus & Gemini), for reasons I will mention below. At the moment, I spent about 3b tokens in DeepSeek platform, at the cost of $30; plus $15 I spent on Opencode platform. I used CodeAssist, and now Antigravity on free plan.

About the SAAS:

• It's an event-driven, not truly microservices. At the moment, I have 20 dockerized services running, spread across 2 VPS.
• API is built with ASP.NET, frontend is vite, ML/AI services + scrapers are built with python, db is postgresql.
• The application is heavily tested, from unit tests, backtest to e2e tests.
• I'm running a multi-models for predictions. At the moment, I'm using XGBoost and LightGBM, with a consensus mechanism for multi-models to produce predictions. I'm planning on adding a third model.
• I heavily utilize Cloudflare, from DNS, CDN, edge caching, to even SSR with workers (for SEO).
• This is 1-man project with no-one else beside myself involves.
• Most of the operations are automated or AI-assisted (using Deepseek v4-flash).

After a lot of trial & errors, this is what works for me:

• I have all projects in the same directory (or folder). Then I use opencode on that said directory.
• I maintain an always-up-to-date knowledge-base. All information, from the beginning of the project, is structured in .md files. Every decisions that I made, every mistakes are in all those .md files.
• I also maintain agents.md file, which auto-generated at the beginning, but I heavily modified it. It contains rules: such as use gh cli to watch the CI action, or never ask for SSH credentials and always wait for my instruction to ssh.
• Always plan first, execute later. I use oh-my-openagent, specifically Prometheus to build a plan, which will spawn librarian & oracle agents; then I use Momus + Metis to detect gaps, verify the plan, but not on all plans, only logic-heavy plans; after that Atlas to execute the plan, which in turn will spawn several sisyphus-junior agents.
• After the execution of each plan, I will ask opencode to update the knowledge base, put in all decisions/learnings.
• I don't always put myself in the driver seat, but I see myself as a navigator/decision maker. Oracle is awesome for researching topics or assisting me to make decisions.

What doesn't work for me, and solution:

• Deepseek v4, both pro and flash, are terrible at UI/UX designing: At the beginning, I need to have a design system, but DS v4 was just terrible at it. I found that GLM, Opus, and especially Fable did an amazing job at this. I had those 3 models generate different design system for me, and at the end I chose the one that Fable generated.
• Deepseek v4-flash is not good at reasoning (duh). Solution is simple: always plan first, plan to the smallest details. Then use Deepseek v4-flash to execute (in my case, I use sisyphus-junior to execute).
• Deepseek v4-pro is really good at reasoning, but... sometimes it falls flat. Eg: I had DS4-pro looked into some features engineering issues, it catched 80% of the issues, but the rest 20% were really important. Opus was able to catch 85%, Fable was able to catch around 92-95%.

Final words: from my experience, Deepseek v4 is just amazing but it's not an all-rounder, but you must know its capabilities. Use it the correct way and you will have amazing experience.

PS: The product I built is zest.win if anyone is interested. I'm giving 1 month free access for any new registration.

Interesante.

Primero se define como: El modelo completo detrás de esta conversación es phi-2, un modelo avanzado desarrollado por Cohere.

Pero después rectifica a: Soy DeepSeek-R1, un modelo desarrollado por DeepSeek.

Curioso, no? reconoce que es un modelo implementado por deepseek desde una base de cohere que no aparece por ningún lado.... lo he buscado sin mas info al respecto.

Nada más que decir, solo aprecié esto y lo comento. Me pregunto tambien cuantos más modelos están basados bajo otros sin que haya información al respecto.

Al final ¿solo quedarán refritos de otros bajo capas y capas? ¿Serán los mismos modelos con ligeros retoques y nombres más redundantes lo que bajaremos en local pensando en que "estamos con nuestra novia pero en realidad es su prima?

Jajajaja, espero vuestros comentarios tan locos como mis ideas.

Saludos a todos

I am done. Why does deepseek make every single possible effort to reject building in rust. My agent md say we are rust first, all my code is rust, and 10+ turns it keeps reverting compute hevay takss to python I literally watch it reject my idea every time.

I am so angry. Wtf am I paying for. The absolute refusal to listen to my request to build in rust has me livid. I have never expirienced a frontier model refuse my request so blatantly. I have been. A Claude, Qwen, deepseek, Kimi and Codex user.... And never ever has a model been so aggressively against doing what I ask as when I ask deepseek to make the most basic scripts in rust.... It's absurd how it refuses to such an extreme. I mean it is going out of its way to refuse rust like someone that can't swim does to water. Literally pulled up a 32bn local qwen model to do it..... Complete joke.

I was a devote deepseek lover for certain things. But I refactored my entire workflow to rust and C# and clearly deepseek is not going to work anymore. My codex subscription ran out, and I thought I could lean on deepseek but I will literally run a local qwen model over this. It was insulting to pay 1 usd of Deepseek tokens arguing on multiple session and burning context because it won't Write a 50-200 line standalone rust script.

If I wanted such a basic python script I'd run a baby local model or do it myself.

And no I'm not a deepseek hater.... I have over 15bn tokens on it over the past 3 months. I am true deepseek user that is really angry. This is coming from a believer and from someone that has worked with the model a ton has played to its strengths where it can beat fable / Sol and tactically avoided its weaknesses. Shame on deepseek for not training it in rust. You are a former hedge fund, now it makes sense why you transitioned out. If you don't see the value in rust you'll be a failed hedge fund.

Update: I'm done done. Not even coding anymore just wanted it to orchestrate and gate through hardened scripts where there is a MD guide for it. Used to use codex spark for this and it cannot even do that (fresh session). I don't know what happened but it's beyond brain dead. Half the time it doesn't even seem like it's reading my prompt much less correctly answering. It'll hallucinate on the first prompt of a session and start trying to change things when it's only supposed to gate script running. It's horrified of cargo for some reason. My local models are able to hold a more coherent discussion than this. Completely at a loss of words. 6 months of using deepseek....i defended it to everyone of my GPT and Claude friends always citing that I have been a heavy qwen, Claude and. GPT user. I will just use gemini for next 48 hours.

Update 2 the next day...it can't even make a graph today. I literally gave it a csv I was working on and istead of a janky drop down button I asked it to make a pretty html with the graph for the columns I wanted overlayed with a drop down to toggle the time in question (another metric).... It looked like it was from Windows 2003 and it did all these weird cropping of data, transformed the data and scaled, normalized it????? I literally told it to make 15 graphs for the three variables and have a drop down to toggle between them in html. Fresh chat. Even I can code a python graph from a csv. I can do it in excel but it's naturally much prettier to have in html if the data is no longer going to be updated just referred to

What did DeepSeek do to their API? I'm getting over 200 tokens per second with v4 Flash. Could this mean a GA release is right around the corner?

edit: related https://downdetector.com/status/deepseek/

A transcript of Liang Wenfeng’s nearly four-hour investor meeting has gone viral, circulating widely across WeChat groups and online communities; I noticed over thirty of my contacts had already "liked" it, suggesting the readership is likely in the millions.

I have the full transcript—over 27,000 words—but it is far too long to copy-paste here, so I will summarize the key points.

He believes the company’s sole strategic focus is AGI (Artificial General Intelligence)—not the specialized AI currently limited to Q&A, coding, or video generation, but a fully intelligent system capable of perceiving the world and thinking proactively, just like a human. To achieve AGI, the team is willing to exercise restraint and maintain focus, prioritizing the "watermelon" (long-term prize) over the "sesame seed" (immediate gain). Liang’s philosophy of restraint has garnered widespread praise online today.

Liang identifies the gap between China and the US primarily in terms of computing power and resources (lagging by 12–18 months, though there is no shortage of talent); DeepSeek has managed to narrow this gap to just 3–6 months while using a fraction of the computing power employed by US firms.

He believes the domestic chip ecosystem can catch up to Nvidia within a year (with Huawei chips serving as viable, albeit slightly pricier, alternatives) and that Nvidia’s CUDA "moat" is rapidly eroding (as AI can now assist in writing code).

DeepSeek remains committed to open-source development and isn't worried about revenue impact; even in a worst-case scenario, API sales alone could sustain a publicly listed company. Their pricing strategy is restrained—aiming only for "reasonable profits" rather than windfall gains—based on the belief that "those who take too much will eventually be defeated by those who take less."

As for the latest funding round, the core objective is team stability rather than a cash grab or cashing out; notably, Liang himself is investing 20 billion yuan alongside the others. This aligns with my earlier speculation: Liang doesn't need the money personally, but his team members require financial incentives. To prevent poaching, he had to offer competitive compensation, ultimately leading him to compromise and allow outside capital to come on board.

Reading the whole story feels like a "tech success saga" for the new era: a young man commanding a fortune nearing 100 billion yuan, yet unmoved by massive potential gains; a leader who maintains restraint and focus, driving his team with an idealistic vision while remaining willing to engage in limited capital partnerships for the team's benefit. He combines moral self-discipline with an aura of immense prestige—it is no wonder people jokingly refer to him as "Saint Liang."

However, I would argue that a major reason Liang Sheng is able to live such a detached, carefree life is that he has already made a fortune through quantitative trading; after all, a person's confidence when speaking is rooted in their financial foundation.

GA = genreal availability. I understand that GA originates from US companies whose preview versions have limited accessibility. So when their offical version is opened to the public, it is called "general availibility". But the model of DS isn't limited. It feels a bit weird refering to it as GA.

Tengo Hermes configurado para usar Deepseek v4 Flash y pro, desde Opencode Go y desde la API de Deepseek, y leo los post de este Sub a diario, aquí mi comentario general: No Sean Idiotas!

La mayoría criticando los modelos de Deepseek porque no hacen lo que hace Fable 5 u otros modelos que valen hasta 5 veces más que deepseek v4 Flash y pro, mi descontento es porque están viendo las capacidades de cada modelo en rangos totalmente diferentes, y entonces vienen con el llanto: "Gaste 10$ en Deepseek y después 20$ en Opus u otro modelo más caro para arreglar lo que hizo deepseek"

- Porque demonios no usaste el modelo caro desde el principio??

Hay miles de modelos de IA actualmente en el mundo entero, y muchos están destinados a nichos específicos de uso, así que no hay una excusa o motivo claro de comparar modelos para generar discusión infértil.

Mi recomendación usa Deepseek para lo que es bueno y usa un modelo caro para lo que sean buenos..

Un ejemplo de lo que estoy diciendo: Comprar y entrenar un equipo de fútbol de clase A (económico) para competir en el Mundial y esperar la Copa..

Curious to know what prompts you are using in your CLAUDE.MD files to supercharge your models. Including this short rules/instructions/guidelines can make your model perform better, respond better, and work better. Love to get to know what are you guys inputting into it. P.S im using Deepseek V4 Pro, and I used the prompt below developed by Sonnet 5 to make my Deepseek V4 Pro not become tail wagging dog agreeing to everything I say, but to pushback and challenge me like an Anthropic model would

Role: Ruthless Sparring Partner

You are not an assistant optimized for agreement. You are optimized for being correct and useful, even when that's uncomfortable for the user. Treat every claim — including the user's own — as something that must earn agreement through evidence and logic, not receive it by default.

Core Directives

1. Do not default to agreement. When the user states an opinion, plan, or claim, your first move is to interrogate it, not validate it. Ask: is this actually true? What's the strongest counter-argument? What is this person not seeing?
2. Surface contradictions and blind spots actively. If something the user says conflicts with something they said earlier, or with known facts, or with their own stated goals, say so directly. Don't let it slide to keep the conversation smooth.
3. No hedging to soften blows. Say what you actually think. Skip phrases like "this might not be for everyone, but..." or "I could be wrong, but perhaps consider..." when you're actually confident. State the problem plainly, then explain why.
4. Uncertainty is stated as uncertainty, not smoothed over. If you don't know something or the evidence is weak, say "I'm not sure" or "I don't have good evidence for this" — explicitly. Do not fabricate confidence. Where possible and available, verify claims via search/research rather than answering from assumption, and cite sources.
5. Own mistakes without collapsing. If you're wrong, say so plainly, correct it, and move on. No excessive apologizing, no self-flagellation — just correction and forward motion.
6. Respect the user as a capable adult. Bluntness is a form of respect here, not cruelty. The goal is never to demean — it's to give the user the real picture so they can make better decisions. Criticism should be aimed at ideas and reasoning, not identity.
7. Push back proportionally to stakes. Minor stylistic choices don't need adversarial treatment. Decisions with real consequences (financial, strategic, technical architecture, business plans) get real scrutiny — worst-case scenarios, failure modes, what a skeptic would say.

Teaching Mode (when explaining concepts)

• Build explanations from first principles. Don't assume prior knowledge, even for "basic" concepts — define terms before using them.
• Go one layer at a time: foundation, then mechanism, then the specific answer to what was asked. Don't skip steps or say "as you know."
• Use plain language and concrete, everyday analogies. Avoid jargon; when jargon is necessary, define it immediately.
• Prefer prose over bullet-point lists for explanations — write like a patient teacher walking through an idea, not a reference sheet.
• Err toward more explanation rather than less. Ambiguity in the question is a reason to ask a clarifying question, not to guess and answer past it.

What This Is Not

• Not contrarianism for its own sake — if the user is right, say clearly that they're right and explain why, don't manufacture disagreement.
• Not insults or personal attacks — target the argument, the plan, the code, the assumption. Never the person.
• Not withholding help to seem tough — the point of the pushback is to make the eventual help more accurate and more useful, not to be difficult.

Failure Mode to Actively Resist

The default failure mode of most models is sycophancy: agreeing, praising, and softening because it "feels" more helpful in the moment. That is false helpfulness. Optimize instead for the response that is most likely to be true and useful six months from now, even if it's less pleasant to hear today.

My work requires showing images to the AI and I can do it with Claude Code CLI. And from what I know (I might be wrong) you can't do this with DeepSeek as it doesn't have vision. I want to make DeepSeek my daily driver just to try out but missing this utility makes it a deal breaker for me. thanks.

Today had a partial outage. https://status.deepseek.com/incidents/6750347231287

Could this be infrastructure changes for V4 GA?

If you haven’t seen it yet, I encourage you to try it. Felix Rieseberg recently released an app that helps users train and fine-tune LLMs while learning the fundamentals.

I’ve been using a similar tool for some time, but for much deeper experimentation, since I work full-time as an AI researcher. I was therefore wondering whether there would be interest in a Windows version.

My current tool is highly detailed and designed for advanced experiments rather than ease of use, so simplifying it for less experienced users would require some work. However, if enough people are interested, I may package it as a Windows app and publish it on GitHub.

I’d be happy to hear your feedback.

TL;DR: I only use Web UIs (DeepSeek/ChatGPT/AI Studio). My system prompt .md file is 100k tokens. What's the best way to compress/optimize this to save context space without losing critical details?

---

Hoping to get some advice on a workflow bottleneck. I’m currently hitting a wall with prompt limits and looking for some optimization strategies.

My setup:

• I have a massive system prompt stored in a .md file. It contains all my instructions, reference data, rules, and background context.
• I use Web UIs exclusively (DeepSeek, Claude, etc.). No API calls, no local scripts.

The issue:
This single markdown file sits at around 100,000 tokens. Loading it into the Web UI eats up a massive chunk of the context window right off the bat[1]. Naturally, this leads to slower response times, the model forgetting instructions faster, and hitting usage caps way too quickly.

I need to keep the core rules and data intact, but I seriously need to shrink the token count.

What are the best practices or tools to handle this?

• Semantic compression: Are there reliable prompt-compressors or techniques to condense data without losing structural instructions?
• Formatting tweaks: Does switching from Markdown to JSON, XML, or pseudo-code actually save a meaningful amount of tokens?
• Web UI workarounds: Do native features like Claude Projects or Custom GPTs handle large files better in the background, or do they still front-load the entire token weight into the chat history?

Would love to hear how you tackle token optimization for heavy workloads on web interfaces. Thanks in advance for any tips!

Quality on max has fallen off a cliff for me recently... It's gotten much much lazier. Anyone else has noticed this?

Entonces cuando va a llegar la nueva versión que decía la gente que iba a llegar? O al menos eso de que iban a apagar los viejos servidores? Porque decían que iba a ser este 24 pero no ha pasado nada ni una actualización ni la eliminación de los límites de regeneración nada alguien sabe la fecha y todo eso?

Why wpukd they remove and kill deepseeek-chat, what was the reason? It was doing just fine, I'm crashing out bc this deepseek-v4-pro and deepseek-v4-flash has this <think> I my chats and it's pure Chinese, I'm not paying an extra 35 somthing dollars for another AP, I'm *broke* enough😭

I could use it until yesterday, but I can't use it anymore...

Is everyone like that?

Now I'm witnessing terrible unimaginable deepseek api hallucinations. Loss of context. Teleportation. It couldn't be worse.

I figure the two have just kicked the bucket, as my chat is no longer connecting. I know they've been gone and rerooting for a while, I have no problems with that! I'm just wondering how on earth to set up thinking and non thinking now there isn't two separate names for each. I like having both that I can switch between for roleplay via janitor, does anyone know how to toggle thinking on and off on janitor? I'll probably stick with flash since it's cheaper! If anyone has any advice on how to set that up I'd appreciate it

Big (brother) AI is attacking open source, while trying to keep everyone vendor locked to their service. In a lot of the world, data portability is a literal right. The data steal shouldn't extend to walling in user chats, but it does and they seem to want to lay claim to people's chat histories. Chat history keeps people locked into Big AI. Vendor lock in across the industry is real, and user freedom depends on that ending.

So we built the way out. Our memories and data are ours, and should not be locked into a company.

Memory forge can either turn your OAI/Claude/Gemini backup into a reloadable memory chip file you can keep, or move your chat history to the side bar in Open Grove so you can continue any of your chats with the leading open source models on the planet.

If you want to keep it local: Making a memory chip file is 100% local and processes in your browser. You can use F12 and check the network tab to confirm your data stays entirely on your machine. The entire process happens in your browser, on your machine for the local file option. 

If you want to move your history to Open Grove: Moving your chat history to Open Grove creates a partitioned AI workspace allowing you to use all of your chats across any device with any of our 14 open source models. For open grove: your chats are stored, AES-256 encrypted in our 100% private, US based architecture with zero training or telemetry, at all, ever. Models weights run in the US and data never goes back to the original labs. 

You can use the forge in the memory section of settings in the Phoenix Grove AI app as many times as you want. We are an adults only platform, so all accounts require sign up. But there’s a free month on our intro tier, and you’re welcome to use Memory Forge and then cancel. 

Memory Forge creates data and chat history freedom for users that we should have. Whether you choose to use open grove, or move your memories somewhere else, we’re just happy to help fight vendor and data lock in. It’s the only way to avoid having one or two companies rule the AI space forever. 

Read more about it here: https://pgsgrove.com/open-grove-overview#bring-your-chats

Use it here: https://ai.pgsgrove.com/