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DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence

https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro
146•cmrdporcupine•2h ago

Comments

woeirua•2h ago
Hmm. Looks like DeepSeek is just about 2 months behind the leaders now.
anonzzzies•2h ago
If that is really so, it would be now be good enough to replace claude for us; we use sonnet only; with our setup, use cases and tooling it works as well as opus 4.6, 4.7 so far. We won't replace sonnet as long as they have subscriptions but it is good to have alternatives for when they force pay per use eventually.
arunkant•28m ago
Yep, it should be better and more efficient then sonnet.
cmrdporcupine•2h ago
Pricing: https://api-docs.deepseek.com/quick_start/pricing

"Pro" $3.48 / 1M output tokens vs $4.40 for GLM 5.1 or $4.00 for Kimi K2.6

"Flash" is only $0.28 / 1M and seems quite competent

(EDIT: Note that if you hit the setting that opencode etc hit (deepseek-chat / deepseek-reasoner) for DeepSeek API, it appears to be "flash".)

mudkipdev•2h ago
This is refreshing right after GPT-5.5's $30
taosx•2h ago
I estimated that even with heavy usage it would cost your around 30-70$ depending on caching at around 40M tokens. That would give you around double the usage compared to gpt-5.5 on the 200$ sub
taosx•2h ago
So the R line (R2) is discontinued or folder back into v4 right?
mudkipdev•2h ago
I believe the R stood for reasoning, just like OpenAI had their own dedicated o1/o3 family, but now every model just has it built-in.
anonzzzies•1h ago
From this thread [0] I can assume that because, while 1.6T, it is A49B, it can run (theoretically, very slow maybe) locally on consumer hardeware, or is that wrong?

[0] https://news.ycombinator.com/item?id=47864835

Quasimarion•1h ago
Theoretically with streaming, any model that fit the disk can run on consumer hardware, just terribly slow.
alecco•21m ago
If 5090 has 32GB, and let's say somehow a 1-bit quantization is possible and you don't need more VRAM for anything else (forget KV cache etc), it would be able to fit a 256B 1-bit model. Just to picture it in extremes how unlikely this is.

And the active parameters come from the experts. For each token the model picks some experts to run the pass (usually 2 to 4, I haven't read V4's papers). It's not always the same experts.

OTOH, being DeepSeek, I foresee a bunch of V4 distilled FP8 models fitting in a 5090 with tiny batches and with performance close from 75 to 85% of V4. And this might be good enough for many everyday tasks.

Today is a good day for open models. Thank god for DeepSeek.

statements•1h ago
The quality of this model vs the price is an insane value deal.
statements•31m ago
Models like Deepseek is the only reason we are able to categorize and measure quality of thousands of MCP servers (https://glama.ai/blog/2026-04-03-tool-definition-quality-sco...). That's billions of tokens – an expense that would be otherwise very hard to swallow.
gwern•56m ago
Main discussion: https://news.ycombinator.com/item?id=47884971

DeepSeek v4

https://api-docs.deepseek.com/
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