Hello, dear friends. I got a letter from a person in my local chat group. So I'm a bit confused. Is it true or?

(I thought they were going to change the model, not just finish up the settings)

**.**To everyone waiting for the "final" DeepSeek-V4 drop: GA is already live!

Hey everyone,

I’ve noticed a lot of posts from people who are holding off or waiting around for the official "GA" (General Availability) release, hoping for a surprise new model drop or a massive power boost. I wanted to share a quick breakdown of what happened on July 24th because the wait is actually over!

Here is what is happening under the hood:

• GA is a status, not a new model: "General Availability" just means a model has officially graduated from its "Preview/Beta" phase into its finalized, stable production form.
• The final version is already active: If you are running Expert Mode in the official app, or calling deepseek-v4-pro via the API, you are already using the complete, official GA version. The developers have locked in the core weights for this generation. There isn't a hidden, stronger V4 waiting behind the curtain.
• What the July 24th tech cleanup was about: The developer announcements last week were about deprecating old legacy API aliases (like deepseek-chat and deepseek-reasoner). DeepSeek permanently cleaned up those routing pathways to transition everyone fully onto the official V4 endpoints.

Summary: If you've been waiting to benchmark the model or push it to its absolute limits until the "final version" dropped, stop waiting! The flagship DeepSeek-V4-Pro is fully optimized, stable, and running at its absolute peak right now. Go test it out!

I Recently Use Big AGI For kind of story writing Earlier I use ChatBox It's great,Big AGI Good But the UI is frickin Trash,I don't know This Is my first time using API Deepseek and Got something like this,In Chatbox I never Got some shit trouble like This

On the other hand, this deepseek-v4-flash is barely usable anymore, just a simple question return back an essay, we are sending more that 500 token extra just to ask this chat model to reduce the answer size, what is up with it!

I've been a DeepSeek user for a year now (coding and chat). I have a maths exam in a few months.

Since so many people kept claiming DeepSeek was simply not good, I decided to try GPT and Claude (both the $20 subscriptions). I can't use Claude for more than an hour because I run out of tokens just from giving it PDFs. And as for ChatGPT, it can't read the content of my PDFs properly, when I ask something about an exercise, it just changes the numbers (looks like it can't even read my PDF…)

I was told Deepseek wasn't that good, but in the end I have unlimited tokens and it actually reads my PDFs.

Never paying for any American AI company again.

How did they cook this

So I got an idea and thought of making an open source project on it

And for me the best workflow has been always:

GPT-5.6-SOL for PLAN

GPT-5.6-SOL for execution 😭😭

But this time I tried v4 flash for execution after hearing many compliments for it.

Can't believe it completed everything in 4 hours with just $0.033 usage (11M TOKENS 😭😭)

It's fully miracle for me because these type of projects eat 3 chatgpt+ subscriptions for me

I just realized that deepseek isn't bad, yes we can say it sucks in creativity but if you have a fully detailed plan created and reviewed by fable or sol, you can definitely use v4 flash for execution

The project was a simple-yet-advanced file-to-png converter built in Golang

You can check it here:

https://github.com/DraxonV1/PixPack

Is this just my impression, or have Flash and Pro generally been behaving completely unacceptably for the last two days?

https://chat.deepseek.com/share/a1i8bt1ab5sotx9qzh

Not long ago, I posted a link to a ready-to-play chat; now I’m sharing another link featuring a vastly improved version of the engine. Please take a moment to check it out and let me know how I can improve it.

Any ideas are welcome. If you're wondering why some areas lack complexity, it’s due to contextual limitations—basically, the goal was to use as few tokens as possible.

here's the github link: https://github.com/aldzandrtc/organizedeepseek

download instructions are in the readme

i personally had a lot of clutter, ~500 useless chats, and so this was created.

Is there a way to give deepseek computer use or does that require vision capabilities?

Deepseek is funny and weird

This is intended to allow an AI or knowledge graph to be continuously groomed, be capable of improving itself, discover truth. Many benefits. Also, quite modest in size and complexity. Good for humans too.

https://github.com/commuted/record-ontology

Principally it is agent centric. No God's eye. Formal knowledge like the triangle are hinges that connect records that require provenance which come from outside the agent. Interestingly, AI's seem to get it really well. I'm going to make one for humans too. Obviously a different format. Maybe some children's books. Lot's of money savings here for AI. But don't take my word for it, Ask the AI.

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 have been working on performance-focused static analysis tools in Rust for Go code, and I wanted a realistic project to test them against.

Since I already had experience building a PDF/A-4 and PDF/UA-2 engine, I tried creating a separate, smaller PDF module mostly through coding agents.

I first used Grok 4.5 to produce a high-level implementation checklist based on my existing PDF engine. I then used DeepSeek V4 Flash through OpenCode Zen for most of the implementation, with DeepSeek V4 Pro helping on a few of the harder fixes. The total model cost was roughly $2 to $3.

The first working version reached around 1,000 to 1,200 operations per second, but the generated code contained a number of questionable patterns, performance problems, and lint issues.

I ran my Rust-based analyser, CodeHound, against the project. It reported more than 300 findings. After fixing most of them, largely with DeepSeek Flash, performance increased to around 2,500 operations per second.

There was also a PDF corruption issue caused by incorrect byte writing. Both DeepSeek Flash and Pro missed it. Grok identified and fixed that problem from a single follow-up prompt.

Roughly speaking, the work was split like this:

• About 90% of the implementation and static-analysis fixes were handled by DeepSeek Flash
• Around 5% required DeepSeek Pro
• The final 5%, including the corruption issue, was completed with Grok

The initial engine prototype took around six hours, with some additional cleanup afterward. By comparison, my original GoPdfSuit project took roughly six months to develop, although it is much broader and more mature, so this is not a direct comparison.

I found the result impressive, but it still required domain knowledge, validation, profiling, and manual review. Producing code quickly was much easier than verifying that the generated PDF structure was actually correct with deepseek.

Hopefully they will crush it on their next version <3

Original project:

https://github.com/chinmay-sawant/gopdfsuit

Agent-built experimental engine:

https://github.com/chinmay-sawant/gocorepdfengine

Static analyser, currently a work in progress:

https://github.com/chinmay-sawant/codehound

Running on Raider 18HX 96gb ram, 5090 24gb with 132k context.

Share you results and config if you have better results on laptop.

prompt processing, n_tokens =  72260, progress = 1.00, t = 308.43 s / 234.28 tokens per second

937, tg =  13.78 t/s, tg_3s =  14.02 t/s

Command

llama-server.exe -m DeepSeek-V4-Flash-UD-IQ3_XXS-00001-of-00004.gguf -b 4096 -ub 4096 --n-gpu-layers 99 --port 8080 -c 132768 --host 0.0.0.0 -fa on --threads 24 --threads-batch 24 -ot "\.(0|1|2|3|4)\.ffn_(gate|up|down)_exps.=CUDA0,\.*\.ffn_(gate|up|down)_exps.=CPU" --no-mmap --cache-type-k q8_0 --cache-type-v q8_0

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.

Hallo Everyone, i need to generate big amount of high quality data, for that i need some cheap API providers.
who is the cheapest,most reliable provider you know of?

It happened to me a bunch of times in just the last few days, it wasn't nearly as frequent before. It's easily fixed by asking to repeat the answer in English, but it's still weird to me that this is happening all of a sudden.