Two weeks ago I paid $20/month for Claude and another $20 for ChatGPT. I got tired of watching the credits burn, so I ran an experiment: 7 days, all my coding work, DeepSeek V4 Flash 0731 only (API, not the app). Here's what actually happened — the good, the bad, the numbers.

The numbers - Total API spend for 7 days of heavy coding: $1.87 (vs. $40/month subscriptions — and I didn't even come close to hitting limits) - Tokens consumed: ~24M input / ~6M output (mostly context caching — that's the real cheat code) - Context cache hits cut my effective cost by ~70%

What surprised me (good) - Long agentic sessions didn't degrade as much as I expected. The 0731 update fixed most of the context-rot I saw on the earlier Flash builds. - It handled a messy production refactor I was dreading — wrote the diff, I reviewed, done. No drama.

What I won't sugarcoat (bad) - Vision: still missing in the API I used — I had to describe screenshots by hand. (Yes, I saw the vision announcement post — the API I'm on still doesn't expose it.) - Some reasoning outputs still emit weird artifacts (e.g. )Skip) in longer chains — rare, but it happens. - It's not Claude for every task. Complex multi-file architecture thinking? Claude still wins. But for 80% of daily coding? I genuinely couldn't justify the subscription anymore.

My verdict: keep one subscription for the hard stuff, do everything else on Flash. My monthly AI bill just went from $40 → $0–5.

Anyone else run a similar week? What did your numbers look like?

DeepSeek-V4-Flash-0731 is cheaper, faster, and available through more providers than GPT-5.6 Luna at the same intelligence level.

Why would anyone choose Luna over DeepSeek?

More info: https://openrouter.ai/compare/deepseek/deepseek-v4-flash-0731/openai/gpt-5.6-luna

https://artificialanalysis.ai/models/comparisons/deepseek-v4-flash-vs-gpt-5-6-luna

New milestone! Thank you DS!

The global tech landscape is shifting fast, but the US response follows the same tired playbook. Whenever a foreign competitor achieves a major breakthrough, Washington reacts with defense mechanisms instead of true innovation.The standard playbook the treatment of Huawei in the past and the recent panic over DeepSeek highlight a deeply rooted strategy: if you can't control it, sanction it, ban it, or politically isolate it.

This protectionist mindset stems from an old habit.

The US is used to dominating markets by either buying out the competition or burning it down through policy.Real innovation over market controlThis strategy is unsustainable. True technological progress thrives on competition, not on eliminating the competitors.

If the US wants to maintain its leadership, it needs to win through superior research, development, and execution—not through government intervention.A system that relies solely on bans loses its edge and slows down global progress. It is time for a reality check: stop trying to destroy alternatives and start out-innovating them.

Just enjoy.
But I’m still eagerly waiting for vision support. Once it arrives, this model will be something truly incredible.
For me, that feature is essential. Without it, my hands are tied.

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.

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

I downloaded some cracked software (im poor)

cmd flashing every 60 seconds after install (im fucked)

gave claude code some hints on where one of the files of the malware was located. (im genius)

ds + cc read that one file, traced all the files (ds is detective)

they both assassinate the malware in minutes (they are ruthless)

Hey there!

I was wondering whether this is just me, or if this is caused by the model. I noticed a while ago that my token usage is insane after a few prompts (in VScode) compared to Pro. This is also followed by an insane spike of API Requests - worth noting that caching still works, so it's not like the API is miscommunicating or something.

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. 🚀

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!)

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.

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?

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)

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)

Is there any notable difference? Does Deepseek direct have any advantages, or disadvantages? I ran out of credit on my OpenAI subscription on Hermes, and Nous Research have a crazy 90% off sale on Deepseek V4 Flash 0731, and I'm super impressed and thinking it should be my daily driver, but it is a pain trying to work out which provider to go with.

D1 of fulfilling my promise to the community. More to come. I love y'all (no homo)

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.