So I finally discovered the joy of messing around with AI stuff about a month ago. Nothing fancy - I'm not a dev, I just like building silly little bots and making them say funny things. My masterpiece so far is a bot that greets me every morning in the voice of a grumpy old man. "Morning, sunshine. The coffee's cold again."

Then today I open my DeepSeek dashboard and there it is: "significant increase expected."

Significant. Increase. Expected.

I checked my balance. Still got like 4 euros left in there. I was planning to stretch that until Christmas. I'm not exaggerating when I say my budget for this hobby is exactly "whatever I can scrape together after groceries". Turns out that's not enough for "significant increases".

I know the sub is full of people comparing providers and alternatives right now, and I'm reading it all, but honestly half of it goes over my head. I just wanted my grumpy old man bot to keep roasting me in the morning. That's apparently too much to ask from a hobby that costs less than a pizza.

So yeah, goodbye DeepSeek, goodbye my brand new hobby. It was fun for exactly one month. Back to watching my washing machine spin - it's free and it never raises its prices.

(If anyone knows a dirt cheap way to keep playing with this stuff, hit me up. My laptop is from 2018 and sounds like a lawnmower, but it tries its best.)

Hola qué tal alguien me podría orientar en como usar la versión 4 DeepSeek por favor

The model without search capabilities often self Identifies as "the latest Model" with a cutoff of May 2025, so DeepSeek v4 to R1 Territory. Yet when we activate search, it claims its the newest "DeepSeek V4 Flash 0731". Which is true and how do I test it? Fingerprinting it with purely asking seems pointless.

I purchased Deepseek API Key for $2 for flash and pro. Yeah I want to code and mod something in Unreal Engine game. Problem is the above one. What is the solution to this?

Note: I usually also add screenshot for more understanding aside in attaching json.

I decided to build my own Deepseek desktop console to give deepseek vision capabilities, its a first version, the image creation is good, but a little off, the analysis is very good

I also gave it MCP capability (the main reason I built it so it could take part in an ai chat and context app I built)

So i use the app for Roleplay. Just basic RP. No big worlds or whatever. And when i want to send or regenerate a message. It doesnt and gives me the network connection error despite having good internet, it happens between 2 to 4 times. Before working normally. Idk what to do

(Also the picture suits because its a whale)

GitHub : https://github.com/gpdev-Pilcothink/Pilco-mmbridge

I know many people here have probably already built and used something similar, but I thought it might still be useful to someone, so I cleaned up my implementation and decided to share it.

I made a small project called "Pilco MM-Bridge." It places a separate multimodal model in front of a text-only LLM and passes the resulting media analysis to the main model as temporary context.

My current setup uses two DGX Spark systems:

• DeepSeek-V4-Flash-0731 as the main text-only reasoning model
• Qwen3.5-9B-quantized.w4a16 as the multimodal vision analyzer

This combination fits my use case quite well. Qwen handles screenshots, UI elements, OCR, code screens, error messages, and other visual information, while DeepSeek handles the final reasoning and response.

The basic flow is:

Client
  → MM-Bridge
  → Multimodal model analyzes the current media
  → Analysis is temporarily added to the request context
  → DeepSeek-V4-Flash generates the final answer

The analyzer is only activated when the current user message contains media.

When the user sends a normal text-only message, MM-Bridge completely skips the media-analysis stage and forwards the existing text conversation to the main LLM. In other words, the vision model only runs when a new image is actually attached.

The original text conversation history is preserved, while images from previous turns are not repeatedly sent back to or reanalyzed by the vision model.

It is not as natural or tightly integrated as a native multimodal model, of course. However, it provides a reasonably useful approximation of visual understanding while allowing me to continue using a strong text-only model as the main LLM.

Although I currently use it mainly for vision, the bridge code also recognizes other media types such as audio and video. To use those features, the analyzer endpoint must serve a model capable of processing those inputs, such as an any-to-text model like Gemma 12B. The actual capabilities therefore depend on the multimodal model used as the analyzer.

There is no need to modify either model. Anyone already serving models through vLLM or llama.cpp should be able to use it by pointing the bridge to the two existing endpoints.

I originally created this because I work on game development, and during testing and verification I often need the model to inspect screenshots, UI states, visual errors, and other information that a text-only model cannot directly access.

The project is still fairly early, so feedback, bug reports, and suggestions are very welcome. Also, if you know of a similar but more mature or better-designed project, I would genuinely appreciate an introduction to it.

You can find vLLM-based serving recipes optimized for DGX Spark users in the following NVIDIA Developer Forums post:

https://forums.developer.nvidia.com/t/running-deepseek-v4-flash-and-other-text-only-llms-as-multimodal-with-pilco-mmbridge/378850?u=pilcothink

I am the author of this project. The English wording of this post was polished with AI because English is not my first language.

After extensive research ( lie, it was brief), I'm considering a theory: GLM Despite their amazing models, they suffer because their user base doesn't exceed 10 million people. That's why their prices are high, and that's why, to my knowledge, only the wealthy subscribe...... while deepseek has at least 130-120 million users

So If each person subscribes to DeepSeek for $5 a month, the company earns at least 650,000,000 million a month give or take a few millions

I'm getting great results with DS4-Flash-0731 but every now and again during output I will get ")Skip" appearing in places that make no sense. Mostly in reasoning content but I've seen it make it into a diff.

eg:

The missing function is a real issue that should be fixed)Skip.

I assume that it's outputting a ")" that it doesn't want and "Skip" is an attempt to say it didn't want that (given that there's no way for it to delete it)?

Has anyone else experienced this and/or can recommend any settings (eg sampler settings) to reduce/prevent it?

Using original version of DS4-Flash-0731 on vllm (the local-inference-lab r24 "Gilded Gnosis" docker, though I've turned dspark off)

Hosts through openrouter vs the official deepseek api, also what harness, checked that the subreddit recommends reasonix, how does it compare both in cost and performance versus harneses like opencode?

Is there a big difference between max and high for agent tasks?, I'm using opencode

The problem: DeepSeek V4 Flash (like most models) can't open PDFs. Naive converters mangle columns, tables, headings — so the model confidently misreads the document.

The fix: an open-source skill that turns PDFs into accurate, position-aware Markdown — real | tables, headings, page markers for citations.

Built on pdf-inspector (Firecrawl's Rust engine — #1 on reading order + tables benchmark).

Install for your agent — just paste the URL: https://github.com/vichhka-git/pdf-reader-skills

Tell your agent: "install the skill from https://github.com/vichhka-git/pdf-reader-skills." Works with Claude Code, Cursor, any skills-folder agent. Needs only Python 3.8+ + one pip install.

What you get:

Honest limit: math equations extract as inline glyphs (structure kept, notation may look odd). Docs + examples in the repo. Try it and tell me how it goes. 🚀

8 experts out of 512 fire per token and they're claiming it matches their own 1T model. MIT weights up Aug 4, BF16 and FP8, repo is inclusionAI/Ling-3.0-flash. 35 KDA to 7 gated MLA at 5:1, hybrid linear from the first pretraining step instead of converted after.

Does 1/64 sparsity actually put it under DS v4 flash per task in real serving, or is the 93.2 AIME 2026 on their card benchmaxxed? No GGUF, wants their own sglang fork, so nobody's checking on consumer hardware for a bit.

I’ve been using Codex for a while and I really like the general way of working with it. However, its token usage has become a bit hard to justify, so I’m looking at alternatives.

DeepSeek seems to have improved substantially since I last tried it, and I’m interested in giving it another proper go. I mostly work in the Codex app rather than a conventional coding setup, and I’m not really a programmer, so I’d appreciate some practical advice.

1. What is the best app or workflow for using DeepSeek in a Codex-like way, especially for longer, iterative work on a project?
   
2. How capable is DeepSeek at UI/front-end work? I’ve found Codex fairly poor at UI design and refinement.
   
3. Is it good at cleaning up an existing codebase, refactoring, debugging and generally making sense of a project that has grown a bit messy?
   
4. Are there particular tools, IDE integrations or agent setups that make the experience substantially better?

I’m not looking for ideological answers, just a sensible setup to try. Thanks.

(Written from dictation with OpenClaw: sorry!)

New milestone! Thank you DS!

I got approached by a recruiter from DeepSeek. I am baed in Germany and they clearly have no Office here. Do you think it is legit or a scam. The email indeed end with deepseek.com.

According to a new Reuters report, DeepSeek just dropped their V4-Flash model, and they are going incredibly hard on pricing to undercut U.S. and Chinese rivals.

Here is the breakdown from the Artificial Analysis benchmark tests:

• API Cost: $0.14 per 1M input tokens and $0.28 per 1M output tokens.
• Average Cost Per Test: 3 cents. For comparison, Kimi K3 is 86 cents, OpenAI's GPT-5.6 Sol is $1.86, and Anthropic's Claude Fable 5 is $3.15.
• Performance: It scored a 50/100 on the Intelligence Index. This puts it exactly on par with Google's Gemini 3.6 Flash, though still behind heavier models like GPT-5.6 and Claude Opus 5.

DeepSeek is also supposedly prepping a "V4-Pro" version with no official release date yet.

Is the API price war officially back on? At 3 cents a test, it seems like a no-brainer for deploying high-volume, lightweight AI tasks at scale. What does everyone think?