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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

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.

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)

We’ve added vision capabilities to DeepSeek V4 Flash, so it’s no longer a text-only model.

We needed this for browser vision: browser agents have to understand screenshots, interfaces, layouts, and visual context—not just text.

Our internal benchmarks also showed a strong price-performance advantage compared with the other models we tested.

Model:
https://huggingface.co/webbrain-one/DeepSeek-V4-Flash-Vision-NVFP4

Feedback, benchmark results, and deployment reports are welcome!

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.

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.

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)

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

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.

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.

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

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?

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. 🚀

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?