DeepSeek V4 Pro vs Claude 4.6 Opus

DeepSeek V4 pro effectively reverse-engineered a recently released 100B LLM architecture entirely on its own and then adapted llama.cpp to run it. (in \~10M token and less then $2 )

The best thing I read in the last hours was a user who said "We werent the real customers, only the beta testers". This, or local LLMs Will be the next aproach for Freelancers who don't want or cant spend thousands on tokens usage.

Anyone else here doing full-stack Next.js in Cursor and watching the Claude quota evaporate before lunch? I used to be in the same boat — massive context windows from all the components, pages, and DB logic would smoke the default limits fast.
Not anymore. I’ve been on this setup for weeks and basically never hit a wall while still getting top-tier answers. Here’s exactly what I do:

**1. .cursorrules is non-negotiable**
I keep one in the root of every project. The key line I added: “Never explain the code to me. Just output the code blocks.”
That single rule saves me thousands of output tokens a day. No more walls of “here’s what I changed and why” — just the goods.

**2. Stopped using Cursor’s built-in Claude quota**
I killed the default Cursor Pro subscription for the heavy stuff. Instead I use my own API keys and point Cursor’s “OpenAI Compatible” base URL at LLM Router Gateway.
Inside [llmrouter](https://llmrouter.app/) routing settings I set up simple tags routing like this:

* **UI & CSS tweaks**: gemini-3.1-flash → gpt-5.4-mini
* **Deep backend / complex logic**: claude-opus-4.6 → deepseek-v3.2
* **General / quick questions**: llama-4-scout

I sorted the fallback chains by speed vs intelligence. The router auto-detects the query type, so 90% of my UI polish and small fixes go to Gemini (basically free + huge context). I only actually hit Claude Opus 4.6 when I’m doing nasty database refactors or tricky architecture stuff. My Anthropic bill dropped \~70% overnight.

**3. Cmd+K for everything small**
Don’t open the full chat sidebar just to rename a variable or extract a component. Highlight the code, hit Cmd+K, let a fast model handle the inline edit. Saves a ton of tokens and feels way snappier.
That’s it. Super simple but it completely changed how much I can actually use Cursor in a day.

How are you all managing the limits? Using a Cursor Team? Or did you build your own router hacks too? Drop your setups — always looking to steal better ideas.

Tested DeepSeek V4 Pro on FoodTruck Bench — our 30-day agentic benchmark where models run a food truck via 34 tools (locations, pricing, inventory, staff, weather, events) with persistent memory and daily reflection.

First Chinese model to land in the frontier tier on our benchmark. Tied with Grok 4.3 Latest on outcome, within 3% of GPT-5.2's median, #4 overall behind Opus 4.6, GPT-5.2, and Grok 4.3.

The timing is the interesting part. We tested GPT-5.2 in mid-February. DeepSeek V4 Pro matches its numbers ten weeks later. The China–US frontier gap on this benchmark used to feel like a year. Right now it's about ten weeks.

The pricing gap is even sharper. GPT-5.2 charges $1.75/M input and $14/M output. DeepSeek V4 Pro is at $0.435/M input and $0.87/M output, with discounted cache reads on top — **\~17× cheaper for the same agentic workload**. That's promo pricing today, but DeepSeek's track record is that promo becomes the floor.

On cost-efficiency (net worth per dollar of API spend) DeepSeek V4 Pro is #2 overall on the leaderboard — behind only Gemma 4 31B, ahead of every premium-tier model.

Against Grok 4.3 Latest specifically the medians are basically tied at the same price, but DeepSeek wins on consistency: zero loans, \~6× less food waste, 30% more meals served per day, 2.4× tighter outcome distribution. Grok matches DeepSeek's peak. DeepSeek matches its own peak every time.

Opus 4.6's peak run is still higher than DeepSeek's. Gemma is still cheaper. Otherwise this is a real frontier-tier competitor at a Chinese price point.

**Update — Xiaomi MiMo v2.5 Pro just finished its run set as well:** 5/5 survived, +1,019% median ROI, $22,388 median net worth at $2.41/run. Lands at #6 on the leaderboard, between Gemma 4 31B and Sonnet 4.6. Slightly behind DeepSeek on outcome and consistency (wider variance — $9K worst run vs $29K best), but a real result for a Chinese model at this price point.

That's now two Chinese models in our top 6, both at sub-$3.5/run. When we started this benchmark in February, neither of these tiers existed outside US labs.

Congrats to the DeepSeek and Xiaomi MiMo teams.

Full write-up: [https://foodtruckbench.com/blog/deepseek-v4-pro](https://foodtruckbench.com/blog/deepseek-v4-pro)
Leaderboard: [https://foodtruckbench.com](https://foodtruckbench.com/)

Just wanted to share that I used u/LegacyRemaster slightly modified (Q4\_K\_M conversion support) DeepSeek V4 [CUDA repo](https://github.com/Fringe210/llama.cpp-deepseek-v4-flash-cuda) (based on u/antirez [work](https://github.com/antirez/llama.cpp-deepseek-v4-flash)) to convert and run Q4\_K\_M [DeepSeek V4 Pro](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro) on my Epyc workstation (Genoa 9374F, 12 x 96GB RAM, single RTX PRO 6000 Max-Q) and it worked right from the start:

(base) phm@epyc:~/projects/llama.cpp-deepseek-v4-flash-cuda/build-cuda$ ./bin/llama-cli -m ../models/DeepSeek-V4-Pro-Q4_K_M.gguf --no-repack -ub 128 --chat-template-file ../models/templates/deepseek-ai-DeepSeek-V3.2.jinja
ggml_cuda_init: found 1 CUDA devices (Total VRAM: 97247 MiB):
Device 0: NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition, compute capability 12.0, VMM: yes, VRAM: 97247 MiB

Loading model...

▄▄ ▄▄
██ ██
██ ██ ▀▀█▄ ███▄███▄ ▀▀█▄ ▄████ ████▄ ████▄
██ ██ ▄█▀██ ██ ██ ██ ▄█▀██ ██ ██ ██ ██ ██
██ ██ ▀█▄██ ██ ██ ██ ▀█▄██ ██ ▀████ ████▀ ████▀
██ ██
▀▀ ▀▀

build : b8936-44c7b01de
model : DeepSeek-V4-Pro-Q4_K_M.gguf
modalities : text

available commands:
/exit or Ctrl+C stop or exit
/regen regenerate the last response
/clear clear the chat history
/read <file> add a text file
/glob <pattern> add text files using globbing pattern

> who are you?

[Start thinking]
Okay, the user is asking "who are you?" This is a simple, introductory question. I need to introduce myself clearly and warmly. I should state my name, creator, and key features that are most relevant to a new user. I can mention that I'm free, my context window, knowledge cutoff, file support, and availability on web and app. I'll end with an open invitation for further questions to keep the conversation going.
[End thinking]

Hi there! I'm DeepSeek, an AI assistant created by the Chinese company DeepSeek (深度求索). I'm here to help you with questions, creative tasks, problem-solving, and pretty much anything you're curious about!

Here's a bit about me:
- **Free to use** - no charges for chatting with me
- **1M context window** - I can handle huge amounts of text at once (like entire book trilogies!)
- **Knowledge cutoff: May 2025** - I'm reasonably up-to-date
- **File upload support** - I can read text from images, PDFs, Word docs, Excel files, and more
- **Web search capability** - though you need to manually enable it via the search button
- **Available on web and mobile app** - with voice input support on the app

I'm a pure text-based model, so I can't "see" images directly, but I can read any text in uploaded files. I aim to be warm, helpful, and detailed in my responses.

What can I help you with today? 😊

[ Prompt: 12.2 t/s | Generation: 8.6 t/s ]

> /exit

Exiting...
common_memory_breakdown_print: | memory breakdown [MiB] | total free self model context compute unaccounted |
common_memory_breakdown_print: | - CUDA0 (RTX PRO 6000 Blackwell Max-Q Workstation Edition) | 97247 = 4022 + ( 92472 = 87766 + 84 + 4621) + 753 |
common_memory_breakdown_print: | - Host | 793994 = 793954 + 0 + 39 |
~llama_context: CUDA_Host compute buffer size of 39.1719 MiB, does not match expectation of 15.3535 MiB

The model file is 859GB.

Update: ran some lineage-bench prompts to see if the model has healthy brain and no problems so far.

Time and time again I find posts about these fine tunes that promise increased intelligence and reasoning with base models, and I continuously try them, realize they're botched, and delete them shortly after. I sometimes do resort to a lower quant since they are bigger, in this case, a 40b variant of Qwen 3.5 27b, but they seem to always let me down. I've resorted to not downloading any model with "Claude Opus 4.6" in the name.

Kudos to everyone who tries to make the foundation models more intelligent, but imo, it never works.

Note that this example is anecdotal evidence on a single prompt, but it's overall always the case of decreased intelligence when using with a local agent setup + llama.cpp in WSL2. This is irrespective of the quant as well - I've tried many.

One thing to notice however, the reasoning/thinking is significantly less, perhaps that's part of the problem.

Have any you found these better than base, ever?

The attached screenshots are:

./llama-server -hf mradermacher/Qwen3.5-27B-heretic-GGUF:Q4_K_S --temp 1.0 --top-p 0.8 --top-k 20 --min-p 0.00 --fit on --alias default --jinja --flash-attn on --ctx-size 262144 --ctx-checkpoints 256 --cache-ram -1 --cache-type-k q4_0 --cache-type-v q4_0 --threads 8 --threads-batch 16 --no-mmap

./llama-server -hf mradermacher/Qwen3.5-40B-Claude-4.6-Opus-Deckard-Heretic-Uncensored-Thinking-i1-GGUF:i1-Q3_K_S --temp 1.0 --top-p 0.8 --top-k 20 --min-p 0.00 --fit on --alias default --jinja --flash-attn on --ctx-size 131072 --ctx-checkpoints 256 --cache-ram -1 --cache-type-k q4_0 --cache-type-v q4_0 --threads 8 --threads-batch 16 --no-mmap