alt Hacker NewsDeepSeek v4
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There are quite a few comments here about benchmark and coding performance. I would like to offer some opinions regarding its capacity for mathematics problems in an active research setting.
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!
Something is odd with this model, their blog posts shows REALLY good results, but in most other third-party benchmarks, people realize it's not really SOTA, even bellow Kimi K2.6 and GLM-5/5.1
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...
Seriously, why can't huge companies like OpenAI and Google produce documentation that is half this good??
https://api-docs.deepseek.com/guides/thinking_mode
No BS, just a concise description of exactly what I need to write my own agent.
>we implement end-to-end, bitwise batch-invariant, and deterministic kernels with minimal performance overhead
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.
Deepseek v4 is basically that quiet kid in the back of the class who never says a word but casually ruins the grading curve for everyone else on the final exam.
It's interesting that they mentioned in the release notes:
"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%...
> pricing "Pro" $3.48 / 1M output tokens vs $4.40
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
There's something heartwarming about the developer docs being released before the flashy press release.
I like deepseek. It works very well. I haven't tried v4 yet but on their web chat interface, just typing "Taiwan" causes it to give you a lecture about how Taiwan is part of China. :)
DeepSeek is commoditizing frontier capability... Opus 4.6-level benchmarks at a fraction of the cost changes also who can access these tools.
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?
I’m deeply interested and invested in the field but I could really use a support group for people burnt out from trying to keep up with everything. I feel like we’ve already long since passed the point where we need AI to help us keep up with advancements in AI.
So, this is the version that's able to serve inference from Huawei chips, although it was still trained on nVidia. So unless I'm very much mistaken this is the biggest and best model yet served on (sort of) readily-available chinese-native tech. Performance and stability will be interesting to see; openrouter currently saying about 1.12s and 30tps, which isn't wonderful but it's day one after all.
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!
While SWE-bench Verified is not a perfect benchmark for coding, AFAIK, this is the first open-weights model that has crossed the threshold of 80% score on this by scoring 80.6%.
Back in Nov 2025, Opus 4.5 (80.9%) was the first proprietary model to do so.
Already on Openrouter. Pro version is $1.74/m/input, $3.48/m/output, while flash $0.14/m/input, 0.28/m/output.
Weights available here: https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro
For those who rely on open source models but don't want to stop using frontier models, how do you manage it? Do you pay any of the Chinese subscription plans? Do you pay the API directly? After GPT 5.5 release, however good it is, I am a bit tired of this price hiking and reduced quota every week. I am now unemployed and cannot afford more expensive plans for the moment.
Assuming it is almost as good as Opus 4.6 (which benchmarks seem to give evidence for), and assuming we are having a good enough harness (PI, OpenCode), it's is now more than 5x cheaper.
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...
For comparison on openrouter DeepSeek v4 Flash is slightly cheaper than Gemma 4 31b, more expensive than Gemma 4 26b, but it does support prompt caching, which means for some applications it will be the cheapest. Excited to see how it compares with Gemma 4.
Truly open source coming from China. This is heartwarming. I know if the potential ulterior motives.
https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main...
Model was released and it's amazing. Frontier level (better than Opus 4.6) at a fraction of the cost.
The Flash version is 284B A13B in mixed FP8 / FP4 and the full native precision weights total approximately 154 GB. KV cache is said to take 10% as much space as V3. This looks very accessible for people running "large" local models. It's a nice follow up to the Gemma 4 and Qwen3.5 small local models.
so many models not enough time
The king is back! I remember vividly being very amazed and having a deep appreciation reading DeepSeek's reasoning on Chat.DeepSeek.com, even before the DeepSeek moment in January later that year. I can't quite remember the date, but it's the most profound moment I have ever had. After OpenAI O1, no other model has “reasoning” capability yet. And DeepSeek opens the full trace for us. Seeing DeepSeek's “wait, aha…” moments is something hard to describe. I learned strategy and reasoning skills for myself also. I am always rooting for them.
They released 1.6 T pro base model on huggingface. First time I'm seeing a "T" model here.
In their paper, point 5.2.5 talks about their sandboxing platform(DeepSeek Elastic Compute). It seems like they have 4 different execution methods: function calls, container, microVM and fullVM.
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.
I know people don't like Twitter links here but the main link just goes to their main docs site generic 'getting started' page.
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
Just tested it via openrounter in the Pi Coding agent and it regularly fails to use the read and write tool correctly, very disappointing. Anyone know a fix besides prompting "always use the provided tools instead of writing your own call"
Feels like the real story here is cost/performance tradeoff rather than raw capability. Benchmarks keep moving incrementally, but efficiency gains like this actually change who can afford to build on top.
I like the pelican I got out of deepseek-v4-flash more than the one I got from deepseek-v4-pro.
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/
At this point 'frontier model release' is a monthly cadence, Kimi 2.6 Claude 4.6 GPT 5.5, the interesting question is which evals will still be meaningful in 6 months.
MMLU-Pro:
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
This is shockingly cheap for a near frontier model. This is insane.
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.
On a seperate note, I am guessing that all the new models have announced in the space of a few days because the time to train a model is the same for each AI company.
Which strikes me as odd - Inwoukd have assumed someone had an edge in terms of at least 10% extra GPUs.
What's the current best framework to have a 'claude code' like experience with Deepseek (or in general, an open-source model), if I wanted to play?
somehow i canot open the link. but in their chinese version's release article, in the end ,there is a quote from xunzi(https://en.wikipedia.org/wiki/Xunzi_(philosopher))
"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. )
The speed of progress here is wild. It feels like the hard part is shifting from having access to a strong model to actually building trustworthy systems around it.
Oh well, I should have bought 2x 512GB RAM MacStudios, not just one :(
They still don’t support json schema or batch api. It’s like deepseek does not want to make money
For those who didn't check the page yet, it just links to the API docs being updated with the upcoming models, not the actual model release.
DeepSeek’s docs say V4 has a 1M context length. Is that actually usable in practice, or just the model/API limit?
Codex shows ~258k for me and Claude Code often shows ~200k, so I’m curious how DeepSeek is exposing such a large window.
I don't mind that High Flyer completely ripped off Anthropic to do this so much as I mind that they very obviously waited long enough for the GAB to add several dozen xz-level easter eggs to it.
Wow, never seen a post with so many comments posted overnight like this.
Excited that the long awaited v4 is finally out. But feel sad that it's not multimodal native.
It is great! I asked the question what I always ask of new models ("what would Ian M Banks think about the current state of AI") and it gave me a brilliant answer! Funny enough the answer contained multiple criticisms of his own creators ("Chinese state entities", "Social Credit System").
Is there a harness that is as good as cloud code that can be used with open weight models?
Are there better providers for inferencing this right now? I know it's launch day, but openrouter showing 30tps isn't looking great.
Such different time now than early 2025 when people thought Deepaeek was going to kill the market for Nvidia.
Open Source as it gets in this space, top notch developer documentation, and prices insanely low, while delivering frontier model capabilities. So basically, this is from hackers to hackers. Loving it!
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?