Also, note that there's zero CUDA dependency. It runs entirely on Huawei chips. In other words, Chinese ecosystem has delivered a complete AI stack. Like it or not, that's a big news. But what's there not to like when monopolies break down?

I have a collection of novel probability and statistics problems at the masters and PhD level with varying degrees of feasibility. My test suite involves running these problems through first (often with about 2-6 papers for context) and then requesting a rigorous proof as followup. Since the problems are pretty tough, there is no quantitative measure of performance here, I'm just judging based on how useful the output is toward outlining a solution that would hopefully become publishable.

Just prior to this model, Gemini led the pack, with GPT-5 as a close second. No other model came anywhere near these two (no, not even Claude). Gemini would sometimes have incredible insight for some of the harder problems (insightful guesses on relevant procedures are often most useful in research), but both of them tend to struggle with outlining a concrete proof in a single followup prompt. This DeepSeek V4 Pro with max thinking does remarkably well here. I'm not seeing the same level of insights in the first response as Gemini (closer to GPT-5), but it often gets much better in the followup, and the proofs can be _very_ impressive; nearly complete in several cases.

Given that both Gemini and DeepSeek also seem to lead on token performance, I'm guessing that might play a role in their capacity for these types of problems. It's probably more a matter of just how far they can get in a sensible computational budget.

Despite what the benchmarks seem to show, this feels like a huge step up for open-weight models. Bravo to the DeepSeek team!

In my tests too[0], it doesn't reach top 10. One issue, which they also mentioned in their post, is that they can't really serve well the model at the moment, so V4-Pro is heavily rate-limited and gives a lot of timeout errors when I try to test it. This shouldn't be an issue though, considering the model is open-source, but it makes it hard to accurately test at the moment.

[0]: https://aibenchy.com/compare/deepseek-deepseek-v4-flash-high...

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

No BS, just a concise description of exactly what I need to write my own agent.

Pretty cool, I think they're the first to guarantee determinism with the fixed seed or at the temperature 0. Google came close but never guaranteed it AFAIK. DeepSeek show their roots - it may not strictly be a SotA model, but there's a ton of low-level optimizations nobody else pays attention to.

"Limited by the capacity of high-end computational resources, the current throughput of the Pro model remains constrained. We expect its pricing to decrease significantly once the Ascend 950 has been deployed into production."

https://api-docs.deepseek.com/zh-cn/news/news260424#api-%E8%...

I’d like somebody to explain to me how the endless comments of "bleeding edge labs are subsidizing the inference at an insane rate" make sense in light of a humongous model like v4 pro being $4 per 1M. I’d bet even the subscriptions are profitable, much less the API prices.

edit: $1.74/M input $3.48/M output on OpenRouter

For reference, the huawei Ascend 950 that this thing runs on is supposed to be roughly comparable to nVidia's H100 from 2022. In other words, things are hotting up in the GPU war!

Back in Nov 2025, Opus 4.5 (80.9%) was the first proprietary model to do so.

I just want to remind you that this is happening at the same time as Anthropic A/B tests removal of Code from Pro Plan, and as OpenAI releases gpt-5.5 2x more expensive than gpt-5.4...

Model was released and it's amazing. Frontier level (better than Opus 4.6) at a fraction of the cost.

Have you noticed the deepseek-v4-pro performing worse than deepseek-v4-flash? It performed even worse than qwen3.5-27b. I found it surprising and I'm wondering if there is a bug on my software because I had to implement sending the `reasoning_content` otherwise the API failed with BadRequestError.

Stuff that was prohibitive six months ago is now up for grabs. We keep on working on the infra level now, swithcing models whenever we run out of credits, or want a different result. The question is how do we build context, architecture and ensure the agent is effective and efficient..... wouldn't it be good if we simply used less energy to make these AI calls?

This is a pretty interesting thing they've built in my opinion, and not something I'd expect to be buried in the model paper like this. Does anyone have any details about it? Google doesn't seem to find anything of note, and I'd love to dive a bit deeper into DSec.

The website now has a link to the announcement on Twitter here https://x.com/deepseek_ai/status/2047516922263285776

Copying text of that below

DeepSeek-V4 Preview is officially live & open-sourced! Welcome to the era of cost-effective 1M context length.

DeepSeek-V4-Pro: 1.6T total / 49B active params. Performance rivaling the world's top closed-source models.

DeepSeek-V4-Flash: 284B total / 13B active params. Your fast, efficient, and economical choice.

Try it now at http://chat.deepseek.com via Expert Mode / Instant Mode. API is updated & available today!

Tech Report: https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main...

Open Weights: https://huggingface.co/collections/deepseek-ai/deepseek-v4

https://simonwillison.net/2026/Apr/24/deepseek-v4/

Both generated using OpenRouter.

For comparison, here's what I got from DeepSeek 3.2 back in December: https://simonwillison.net/2025/Dec/1/deepseek-v32/

And DeepSeek 3.1 in August: https://simonwillison.net/2025/Aug/22/deepseek-31/

And DeepSeek v3-0324 in March last year: https://simonwillison.net/2025/Mar/24/deepseek/

Gemini-3.1-Pro at 91.0

Opus-4.6 at 89.1

GPT-5.4, Kimi2.6, and DS-V4-Pro tied at 87.5

Pretty impressive

For context, for an agent we're working on, we're using 5-mini, which is $2/1m tokens. This is $0.30/1m tokens. And it's Opus 4.6 level - this can't be real.

I am uncomfortable about sending user data which may contain PII to their servers in China so I won't be using this as appealing as it sounds. I need this to come to a US-hosted environment at an equivalent price.

Hosting this on my own + renting GPUs is much more expensive than DeepSeek's quoted price, so not an option.

Which strikes me as odd - Inwoukd have assumed someone had an edge in terms of at least 10% extra GPUs.

"Not seduced by praise, not terrified by slander; following the Way in one's conduct, and rectifying oneself with dignity." (不诱于誉，不恐于诽，率道而行，端然正己)

(It is mainly used to express the way a Confucian gentleman conducts himself in the world. It reminds me of an interview I once watched with an American politician, who said that, at its core, China is still governed through a Confucian meritocratic elite system. It seems some things have never really changed.

In some respects, Liang Wenfeng can be compared to Linux. The political parallel here is that the advantages of rational authoritarianism are often overlooked because of the constraints imposed by modern democratic systems. )

Codex shows ~258k for me and Claude Code often shows ~200k, so I’m curious how DeepSeek is exposing such a large window.

"Due to constraints in high-end compute capacity, the current service capacity for Pro is very limited. After the 950 supernodes are launched at scale in the second half of this year, the price of Pro is expected to be reduced significantly."

So it's going to be even cheaper

But if it does, then in the following week we'll see DeepSeek4 floods every AI-related online space. Thousands of posts swearing how it's better than the latest models OpenAI/Anthropic/Google have but only costs pennies.

Then a few weeks later it'll be forgotten by most.

Was expecting that the release would be this month [1], since everyone forgot about it and not reading the papers they were releasing and 7 days later here we have it.

One of the key points of this model to look at is the optimization that DeepSeek made with the residual design of the neural network architecture of the LLM, which is manifold-constrained hyper-connections (mHC) which is from this paper [2], which makes this possible to efficiently train it, especially with its hybrid attention mechanism designed for this.

There was not that much discussion around it some months ago here [3] about it but again this is a recommended read of the paper.

I wouldn't trust the benchmarks directly, but would wait for others to try it for themselves to see if it matches the performance of frontier models.

Either way, this is why Anthropic wants to ban open weight models and I cannot wait for the quantized versions to release momentarily.

[0] https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main...

[1] https://news.ycombinator.com/item?id=47793880

[2] https://arxiv.org/abs/2512.24880

[3] https://news.ycombinator.com/item?id=46452172

    bash({"command":"gh pr create --title "Improve Calendar module docs and clean up idiomatic Elixir" --body "$(cat <<'EOF'
    Problem
    The Calendar modu...

> We present a preview version of DeepSeek-V4 series, including two strong Mixture-of-Experts (MoE) language models — DeepSeek-V4-Pro with 1.6T parameters (49B activated) and DeepSeek-V4-Flash with 284B parameters (13B activated) — both supporting a context length of one million tokens. DeepSeek-V4 series incorporate several key upgrades in architecture and optimization: (1) a hybrid attention architecture that combines Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA) to improve long-context efficiency; (2) Manifold-Constrained Hyper-Connections (mHC) that enhance conventional residual connections; (3) and the Muon optimizer for faster convergence and greater training stability. We pre-train both models on more than 32T diverse and high-quality tokens, followed by a comprehensive post-training pipeline that unlocks and further enhances their capabilities. DeepSeek-V4-Pro-Max, the maximum reasoning effort mode of DeepSeek-V4-Pro, redefines the state-of-the-art for open models, outperforming its predecessors in core tasks. Meanwhile, DeepSeek-V4 series are highly efficient in long-context scenarios. In the one-million-token context setting, DeepSeek-V4-Pro requires only 27% of single-token inference FLOPs and 10% of KV cache compared with DeepSeek-V3.2. This enables us to routinely support one-million-token contexts, thereby making long-horizon tasks and further test-time scaling more feasible. The model checkpoints are available at https://huggingface.co/collections/deepseek-ai/deepseek-v4.

1: https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main...

https://news.ycombinator.com/item?id=47885014

https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro