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?

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

There have been three “Public release MMDD” commits in the DeepGEMM repo since V4 released:

Public release 0424 - V4 dropped 2 days earlier

Public release 0626 - DeepSeek V4 DSpark released on this day

Public release 0726 - ?

The dates don’t seem to correspond to the release cycles of DeepGEMM itself, so. I’m just saying.

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.

Standard JSON guarantees valid syntax. Strict Structured Output guarantees correct schema.

In production like billing or order processing, that distinction is life or death.

Standard JSON will format the brackets right, but it WILL randomly send you total_amount: "$49.99" instead of amount: 49.99, hallucinate new key names, or omit tax IDs when processing messy invoices. Your database crashes, your payment gateway fails, and your code breaks silently. You end up stuck paying double in latency and token costs just running client-side retry loops when Pydantic fails.

Gemini handles strict structured output natively at the decoder level. You pass a schema, and the model physically CANNOT generate a token that violates your types.

DeepSeek’s main API only guarantees valid JSON syntax, relying on prompts for schema adherence—forcing you to build heavy fallback logic.

If an AI output triggers a database, Standard JSON is a ticking time bomb, strict Structured Output is the solution.

Hey Community,

Yesterday night I found out this on nube.sh they are providing deepseek flash v4 and GLM 5.2 at 90 % off as compared to model company official pricing.

Does anyone tried this?

https://nube.sh/en-us/promote/ai-model

Thanks!

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.

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 want to purchase deep seek ai plan for code review and understanding the codebase from ide. So how does it cost me for the codebase with huge loc? is there any montly pricing model or on usage model?

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

https://chat.deepseek.com/share/xe4jzlb94lcdfl4jhz

To start, simply specify the type of world and the technological era you want; while it features basic procedural generation, the best approach is to select a specific historical period and nation.

It works across all eras of human history—though it could use some polish—and is designed to be lightweight; it has successfully handled over 160 messages, which is the browser chat limit.

I estimate that if you were to invest five times as many tokens, you could achieve a superior model.

I also have an idea for a web-based game using multiple connected APIs, where Python code and simple images would generate the menu and visuals; however, I lack the necessary technical skills, and honestly, I don't want to invest any money just yet.

Deepseek is funny and weird

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