- \n
- a flagship open-source model that can sit closer to frontier closed systems<\/li>\n\n\n\n
- a cheaper, faster variant that is easier to deploy at scale<\/li>\n\n\n\n
- Dan 1M-token context<\/strong> positioned less like a luxury feature and more like a practical default for serious workloads<\/li>\n<\/ul>\n\n\n\n
And that matters, because this is not the first time the company has triggered global AI attention.<\/p>\n\n\n\n
When DeepSeek\u2019s earlier model cycle broke into the mainstream in January 2025, it became much more than another open-source launch. TechCrunch reported that DeepSeek climbed to No. 1 on the U.S. App Store on January 26<\/strong>, after jumping from No. 31 just a couple of days earlier<\/strong>, and reached 2.6 million combined downloads across the App Store and Google Play<\/strong> by Monday morning. One day later, TechCrunch also reported that DeepSeek\u2019s Android app hit No. 1 on the U.S. Play Store<\/strong>, with AppFigures estimating more than 1.2 million Play Store downloads and over 1.9 million App Store downloads<\/strong> worldwide since launch.<\/p>\n\n\n\n
That history matters when looking at DeepSeek V4<\/strong>.<\/p>\n\n\n\n
The reason people are paying attention again is not just because V4 has a 1 million token context window<\/strong>. It is because DeepSeek already proved it can break out of the AI bubble and become a global mainstream story.<\/p>\n\n\n\n
This release shows how fast open-source AI is closing the gap with frontier closed models \u2014 especially in coding, reasoning, and agent-style workflows. For teams building with AI, that matters more than hype.<\/p>\n\n\n\n
Why the World Is Watching Again<\/h2>\n\n\n\n
There are three reasons this launch is getting immediate attention.<\/p>\n\n\n\n
1. DeepSeek already has breakout history<\/h3>\n\n\n\n
DeepSeek is no longer an obscure lab. Its previous release cycle drew coverage across outlets like TechCrunch, CNBC, Forbes, Fortune, The Verge, and Business Insider<\/strong> \u2014 not just AI-native media.<\/p>\n\n\n\n
That changes how a new model launch is interpreted. When a previously viral AI brand ships another major release, people do not read it as \u201cinteresting news.\u201d They read it as a possible second-wave breakout.<\/p>\n\n\n\n
2. The current release already shows early traction signals<\/h3>\n\n\n\n
At launch, the official DeepSeek-V4-Pro<\/strong> page on Hugging Face showed strong immediate engagement, including a large follower base for DeepSeek and hundreds of likes on the model page within the first hours of release.<\/p>\n\n\n\n
Just as important, a search check right after launch showed something interesting: there were already fresh V4 explainers, landing pages, and benchmark summaries appearing in search \u2014 but essentially no established results for \u201cDeepSeek V4 review.\u201d<\/strong><\/p>\n\n\n\n
That means attention is arriving faster than high-quality interpretation.<\/p>\n\n\n\n
3. The narrative is bigger than one model<\/h3>\n\n\n\n
DeepSeek V4 is landing in a market that is already primed for an \u201copen-source is catching up again\u201d story. The new release fits directly into that broader narrative: better reasoning, longer context, more agent relevance, and stronger efficiency claims.<\/p>\n\n\n\n
That is why this feels bigger than a normal model card drop.<\/p>\n\n\n\n
Two Models, One Strategy<\/h2>\n\n\n\n
According to the official Hugging Face release, the DeepSeek V4 series includes two Mixture-of-Experts models:<\/p>\n\n\n\n
- \n
- DeepSeek-V4-Pro<\/strong>: 1.6T total parameters, 49B activated<\/li>\n\n\n\n
- DeepSeek-V4-Flash<\/strong>: 284B total parameters, 13B activated<\/li>\n<\/ul>\n\n\n\n
Both models support up to 1 million tokens of context<\/strong>.<\/p>\n\n\n\n
![\"\"](\"https:\/\/imastudio.com\/wp-content\/uploads\/2026\/04\/image-15-1024x584.png\")
<\/figure>\n\n\n\nThis matters because DeepSeek is no longer telling a one-model story.<\/p>\n\n\n\n
The more interesting reading is that it is building a two-layer product strategy<\/strong>:<\/p>\n\n\n\n
- \n
- Pro<\/strong> is the flagship, designed to compete for attention in reasoning, coding, long-context work, and agent-style execution<\/li>\n\n\n\n
- Flash<\/strong> is the value layer, designed to be smaller, faster, and much cheaper for broader deployment<\/li>\n<\/ul>\n\n\n\n
That split makes the launch feel more mature than a typical benchmark-focused release. It gives developers and teams a realistic choice between \u201cbest performance\u201d and \u201cbest efficiency,\u201d instead of forcing both goals into one model.<\/p>\n\n\n\n
DeepSeek also says V4 introduces several architectural upgrades designed to make long-context inference more practical, not just theoretically possible.<\/p>\n\n\n\n
These include:<\/p>\n\n\n\n
- \n
- Hybrid Attention Architecture<\/strong>, combining Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA)<\/li>\n\n\n\n
- Manifold-Constrained Hyper-Connections (mHC)<\/strong> to improve signal propagation across layers<\/li>\n\n\n\n
- Muon optimizer<\/strong> for faster and more stable training<\/li>\n<\/ul>\n\n\n\n
In DeepSeek\u2019s own numbers, DeepSeek-V4-Pro uses only 27% of the single-token inference FLOPs and 10% of the KV cache required by DeepSeek-V3.2 in a 1M-token setting<\/strong>.<\/p>\n\n\n\n
That is the kind of improvement that gets infrastructure teams interested.<\/p>\n\n\n\n
There is also a practical product story behind the launch. DeepSeek\u2019s official API docs show that both deepseek-v4-flash<\/strong> Dan deepseek-v4-pro<\/strong> are available through endpoints compatible with OpenAI<\/strong> Dan Antropis<\/strong> formats. Both support tool calls, JSON output, and a maximum output length of 384K tokens<\/strong>. For developers, this matters because it makes V4 easier to slot into existing applications and agent stacks without a full rewrite.<\/p>\n\n\n\n
Just as important, DeepSeek has already tied V4 to a migration path. The older model names deepseek-chat<\/strong> Dan deepseek-reasoner<\/strong> are scheduled to be deprecated on 2026\/07\/24<\/strong>, with compatibility mapping them to the non-thinking and thinking modes of deepseek-v4-flash<\/strong>.<\/p>\n\n\n\n
So, How Good Is DeepSeek V4 Actually?<\/h2>\n\n\n\n
If we strip away the hype and look at the official material, the answer is: DeepSeek V4 looks genuinely strong \u2014 especially for long-context work, coding, and reasoning-heavy workflows \u2014 but it should still be judged as a very promising preview rather than a fully settled winner.<\/strong><\/p>\n\n\n\n
That is the fairest review framing.<\/p>\n\n\n\n
1. DeepSeek V4-Pro looks like a serious open-source flagship<\/h3>\n\n\n\n
On paper, DeepSeek-V4-Pro-Max<\/strong> is clearly meant to compete with frontier models, not just other open releases.<\/p>\n\n\n\n
![\"\"](\"https:\/\/imastudio.com\/wp-content\/uploads\/2026\/04\/image-16-1024x622.png\")
<\/figure>\n\n\n\nIn the official comparison table, it posts notable numbers such as:<\/p>\n\n\n\n
- \n
- LiveCodeBench: 93.5<\/strong><\/li>\n\n\n\n
- Codeforces rating: 3206<\/strong><\/li>\n\n\n\n
- GPQA Diamond: 90.1<\/strong><\/li>\n\n\n\n
- SWE Verified: 80.6<\/strong><\/li>\n\n\n\n
- MRCR 1M: 83.5<\/strong><\/li>\n<\/ul>\n\n\n\n
The broader takeaway is not that DeepSeek V4 beats every closed model across the board. It does not. The more credible conclusion is that it now belongs in the same serious conversation for a number of advanced technical tasks.<\/p>\n\n\n\n
2. Flash may be the sleeper story<\/h3>\n\n\n\n
A lot of attention will go to the Pro variant, but DeepSeek-V4-Flash<\/strong> may end up being just as important commercially.<\/p>\n\n\n\n
According to DeepSeek\u2019s API pricing page, V4-Flash is priced at:<\/p>\n\n\n\n
- \n
- $0.14 \/ 1M input tokens (cache miss)<\/strong><\/li>\n\n\n\n
- $0.028 \/ 1M input tokens (cache hit)<\/strong><\/li>\n\n\n\n
- $0.28 \/ 1M output tokens<\/strong><\/li>\n<\/ul>\n\n\n\n
By comparison, DeepSeek-V4-Pro<\/strong> is priced at:<\/p>\n\n\n\n
- \n
- $1.74 \/ 1M input tokens (cache miss)<\/strong><\/li>\n\n\n\n
- $0.145 \/ 1M input tokens (cache hit)<\/strong><\/li>\n\n\n\n
- $3.48 \/ 1M output tokens<\/strong><\/li>\n<\/ul>\n\n\n\n
That creates a more interesting product story than \u201cbigger model wins.\u201d Flash gives DeepSeek a realistic value layer for high-volume use cases, while Pro carries the flagship positioning.<\/p>\n\n\n\n
3. DeepSeek wants to win the agent conversation, not just the chatbot conversation<\/h3>\n\n\n\n
One of the clearest signals in the V4 release is what DeepSeek chooses to emphasize.<\/p>\n\n\n\n
The official evaluation tables do not stop at knowledge and reasoning benchmarks. They also highlight agentic and tool-use oriented tasks<\/strong> such as:<\/p>\n\n\n\n
- \n
- Terminal Bench 2.0<\/strong><\/li>\n\n\n\n
- SWE Verified<\/strong><\/li>\n\n\n\n
- SWE Pro<\/strong><\/li>\n\n\n\n
- BrowseComp<\/strong><\/li>\n\n\n\n
- MCPAtlas<\/strong><\/li>\n\n\n\n
- Toolathlon<\/strong><\/li>\n<\/ul>\n\n\n\n
That matters because it suggests DeepSeek wants V4 to be judged as an agent-ready model family<\/strong>, not only as a chatbot or coding assistant.<\/p>\n\n\n\n
For teams building AI products, that is a more relevant ambition than raw leaderboard theater.<\/p>\n\n\n\n
4. Flash may be the sleeper story<\/h3>\n\n\n\n
A lot of attention will go to the Pro variant, but DeepSeek-V4-Flash<\/strong> may end up being just as important commercially.<\/p>\n\n\n\n
According to DeepSeek\u2019s API pricing page, V4-Flash is priced at:<\/p>\n\n\n\n
- \n
- $0.14 \/ 1M input tokens (cache miss)<\/strong><\/li>\n\n\n\n
- $0.028 \/ 1M input tokens (cache hit)<\/strong><\/li>\n\n\n\n
- $0.28 \/ 1M output tokens<\/strong><\/li>\n<\/ul>\n\n\n\n
By comparison, DeepSeek-V4-Pro<\/strong> is priced at:<\/p>\n\n\n\n
- \n
- $1.74 \/ 1M input tokens (cache miss)<\/strong><\/li>\n\n\n\n
- $0.145 \/ 1M input tokens (cache hit)<\/strong><\/li>\n\n\n\n
- $3.48 \/ 1M output tokens<\/strong><\/li>\n<\/ul>\n\n\n\n
That creates a more interesting product story than \u201cbigger model wins.\u201d Flash gives DeepSeek a realistic value layer for high-volume use cases, while Pro carries the flagship positioning.<\/p>\n\n\n\n
5. Reasoning modes are a real usability advantage<\/h3>\n\n\n\n
DeepSeek V4 supports different reasoning effort modes rather than forcing one behavior for every task.<\/p>\n\n\n\n
That is a meaningful product decision.<\/p>\n\n\n\n
For routine requests, users can prioritize speed. For complex planning, code, or research tasks, they can allocate more reasoning effort. In practice, this makes the model family more adaptable to real workloads than a single static inference style.<\/p>\n\n\n\n
6. The strongest claim is long-context efficiency<\/h3>\n\n\n\n
A lot of AI launches talk about context length. Fewer make long-context execution look operationally believable.<\/p>\n\n\n\n
This is where V4 may be most interesting.<\/p>\n\n\n\n
A 1M-token context window<\/strong> is already a headline feature, but the more important detail is DeepSeek\u2019s claim that V4-Pro needs only 27% of the single-token inference FLOPs<\/strong> Dan 10% of the KV cache<\/strong> required by DeepSeek-V3.2 at that context scale.<\/p>\n\n\n\n
If those gains hold up in practice, that could matter just as much as benchmark scores.<\/p>\n\n\n\n
Why the 1M-Token Context Window Is a Bigger Deal Than It Sounds<\/h2>\n\n\n\n
A million-token context window is not just a marketing bullet.<\/p>\n\n\n\n
In practical terms, it means developers and teams can push much larger amounts of source material into a single session \u2014 long codebases, massive documentation sets, research archives, customer transcripts, or multi-file workflows that used to require awkward chunking strategies.<\/p>\n\n\n\n
That opens up several high-value use cases:<\/p>\n\n\n\n
1. Large codebase understanding<\/h3>\n\n\n\n
Teams can analyze bigger repositories with less manual slicing, which improves debugging, refactoring, and agent-based coding workflows.<\/p>\n\n\n\n
2. Research and knowledge synthesis<\/h3>\n\n\n\n
Instead of passing fragments into a model and losing global context, users can work with much larger source collections in one shot.<\/p>\n\n\n\n
3. Better AI agents<\/h3>\n\n\n\n
Agent systems perform better when they can keep more memory in context. For planning, tool use, and multi-step task execution, context efficiency matters almost as much as raw reasoning quality.<\/p>\n\n\n\n
4. Enterprise document workflows<\/h3>\n\n\n\n
Long contracts, compliance docs, support archives, and internal wikis become more workable inside one reasoning loop.<\/p>\n\n\n\n
That said, context length by itself does bukan<\/strong> guarantee quality. Many models advertise long windows but degrade when retrieval quality, memory focus, or latency becomes a problem.<\/p>\n\n\n\n
That is why DeepSeek\u2019s efficiency claims are arguably more important than the 1M number itself.<\/p>\n\n\n\n
Why This Launch Feels Bigger Than a Normal Benchmark Drop<\/h2>\n\n\n\n
DeepSeek is not positioning V4 as just a long-context model.<\/p>\n\n\n\n
It is also making a serious push in reasoning<\/strong>, coding<\/strong>, Dan agentic performance<\/strong>.<\/p>\n\n\n\n
The release highlights DeepSeek-V4-Pro-Max<\/strong> as the strongest reasoning mode in the lineup, and frames it as one of the best open-source models currently available.<\/p>\n\n\n\n
Across the published comparison tables, V4-Pro-Max shows especially strong results in areas like:<\/p>\n\n\n\n
- \n
- LiveCodeBench<\/strong><\/li>\n\n\n\n
- Codeforces-style coding performance<\/strong><\/li>\n\n\n\n
- GPQA Diamond<\/strong><\/li>\n\n\n\n
- BrowseComp<\/strong><\/li>\n\n\n\n
- SWE-style software engineering benchmarks<\/strong><\/li>\n\n\n\n
- Long-context tests such as MRCR 1M and CorpusQA 1M<\/strong><\/li>\n<\/ul>\n\n\n\n
The exact rankings will keep changing as labs update models every few weeks. But the strategic signal is already clear:<\/p>\n\n\n\n
Open-source models are becoming increasingly credible for serious technical workflows, not only for lightweight chat use cases.<\/strong><\/p>\n\n\n\n
That is the real reason this launch matters.<\/p>\n\n\n\n
The Most Interesting Part: Reasoning Modes<\/h2>\n\n\n\n
DeepSeek V4 supports three reasoning effort modes:<\/p>\n\n\n\n
- \n
- Non-think<\/strong> for fast, lightweight responses<\/li>\n\n\n\n
- Think High<\/strong> for slower, more deliberate analysis<\/li>\n\n\n\n
- Think Max<\/strong> for maximum reasoning effort<\/li>\n<\/ul>\n\n\n\n
This is important because it reflects where the model market is heading.<\/p>\n\n\n\n
The future is not just \u201cone model, one behavior.\u201d It is increasingly about adaptive inference<\/strong>: fast when you need speed, deeper when you need accuracy.<\/p>\n\n\n\n
For product teams, this creates a better balance between:<\/p>\n\n\n\n
- \n
- latency<\/li>\n\n\n\n
- cost<\/li>\n\n\n\n
- reasoning depth<\/li>\n\n\n\n
- user experience<\/li>\n<\/ul>\n\n\n\n
In other words, DeepSeek is not only shipping a model. It is shipping a usage pattern<\/strong> that matches how real AI products are evolving.<\/p>\n\n\n\n
What This Means for Open-Source AI<\/h2>\n\n\n\n
DeepSeek V4 reinforces three broader trends.<\/p>\n\n\n\n
1. Open-source is becoming harder to ignore<\/h3>\n\n\n\n
The gap between top open and closed models is still real, but it is narrowing in visible ways. Every major release now forces product teams to re-evaluate whether they truly need a closed model for every workflow.<\/p>\n\n\n\n
2. Efficiency is becoming a first-class battleground<\/h3>\n\n\n\n
The model with the highest score is not automatically the most useful model. For real deployments, memory efficiency, throughput, and inference cost shape product viability.<\/p>\n\n\n\n
3. Agent workflows are raising the bar<\/h3>\n\n\n\n
As more companies build AI agents, the most valuable models are those that can handle long context, multi-step reasoning, and tool-oriented execution at the same time.<\/p>\n\n\n\n
DeepSeek V4 is clearly aiming at that intersection.<\/p>\n\n\n\n
A Few Caveats Before the Hype Gets Out of Control<\/h2>\n\n\n\n
Ini adalah preview release<\/strong>, so teams should stay realistic.<\/p>\n\n\n\n
A few things are worth watching:<\/p>\n\n\n\n
- \n
- Real-world latency under heavy long-context loads<\/li>\n\n\n\n
- Performance consistency across different prompting styles<\/li>\n\n\n\n
- Tool-use reliability outside benchmark settings<\/li>\n\n\n\n
- Deployment complexity for teams that want to run it locally<\/li>\n\n\n\n
- Whether benchmark gains translate into stronger production outcomes<\/li>\n<\/ul>\n\n\n\n
DeepSeek also notes that local deployment requires its own encoding and inference workflow, rather than a simple plug-and-play template. That is not a dealbreaker, but it does mean adoption may be easier for technically mature teams than for casual users.<\/p>\n\n\n\n
Final Take<\/h2>\n\n\n\n
DeepSeek V4 matters not just for its specs, but because it proves DeepSeek can capture global attention at scale.<\/p>\n\n\n\n
That\u2019s why the industry is watching again.<\/p>\n\n\n\n
On the technical side, the model pushes forward with a 1M-token context window, stronger long-context efficiency, improved coding and reasoning performance, and a clear move toward agent-style workflows.<\/p>\n\n\n\n
On the market side, it arrives with momentum. DeepSeek is no longer starting from zero. It already has global brand recognition from its previous breakout, and V4 is launching into a market actively looking for the next credible open-model leap.<\/p>\n\n\n\n
If you\u2019re building with AI, this isn\u2019t just another benchmark release. It\u2019s a signal that open models are becoming more competitive, more practical, and increasingly ready for real production use.<\/p>\n\n\n\n
DeepSeek V4 may not end the closed vs open debate. But it definitely raises the floor for what teams should expect from open-source AI in 2026.<\/p>\n\n\n\n
\n\n\n\nHow to Try DeepSeek V4<\/h3>\n\n\n\n
If you want to explore it yourself, there are a few ways to get started:<\/p>\n\n\n\n
- \n
- Run locally (full control)<\/strong>
Download and deploy via Hugging Face:
\ud83d\udc49 https:\/\/huggingface.co\/deepseek-ai\/DeepSeek-V4-Pro<\/a><\/li>\n\n\n\n- Try instantly (no setup)<\/strong>
Use the official chat interface:
\ud83d\udc49 https:\/\/chat.deepseek.com\/<\/a><\/li>\n\n\n\n- Integrate via API (build with it)<\/strong>
Access DeepSeek V4 through a unified API gateway:
\ud83d\udc49 https:\/\/www.imarouter.com<\/a> You can easily plug it into your existing workflows or agent tools like Cakar Terbuka<\/strong>, Claude Code<\/strong>, and other automation systems.<\/li>\n<\/ul>\n\n\n\nSources<\/h2>\n\n\n\n
- \n
- Official model page: https:\/\/huggingface.co\/deepseek-ai\/DeepSeek-V4-Pro<\/li>\n\n\n\n
- TechCrunch: DeepSeek displaces ChatGPT as the App Store\u2019s top app<\/li>\n\n\n\n
- TechCrunch: DeepSeek reaches No. 1 on US Play Store<\/li>\n\n\n\n
- CNBC: China\u2019s DeepSeek AI dethrones ChatGPT on App Store: Here\u2019s what you should know<\/li>\n<\/ul>\n\n\n\n
<\/p>","protected":false},"excerpt":{"rendered":"
DeepSeek is back in the spotlight. This time, the story is bigger than a model card. With DeepSeek-V4-Pro and DeepSeek-V4-Flash, DeepSeek is not just releasing another open-weight model family. It is trying to turn three ideas into one launch at the same time: And that matters, because this is not the first time the company […]<\/p>","protected":false},"author":17,"featured_media":7334,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"rank_math_title":"DeepSeek V4 Review: Why the World Is Watching Again","rank_math_description":"DeepSeek V4 Pro brings a 1M-token context window, stronger coding and reasoning performance, and a familiar wave of global attention. Here\u2019s why the world is watching DeepSeek again.","footnotes":""},"categories":[11],"tags":[],"class_list":["post-7325","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-trends"],"_links":{"self":[{"href":"https:\/\/imastudio.com\/id\/wp-json\/wp\/v2\/posts\/7325","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/imastudio.com\/id\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/imastudio.com\/id\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/imastudio.com\/id\/wp-json\/wp\/v2\/users\/17"}],"replies":[{"embeddable":true,"href":"https:\/\/imastudio.com\/id\/wp-json\/wp\/v2\/comments?post=7325"}],"version-history":[{"count":3,"href":"https:\/\/imastudio.com\/id\/wp-json\/wp\/v2\/posts\/7325\/revisions"}],"predecessor-version":[{"id":7335,"href":"https:\/\/imastudio.com\/id\/wp-json\/wp\/v2\/posts\/7325\/revisions\/7335"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/imastudio.com\/id\/wp-json\/wp\/v2\/media\/7334"}],"wp:attachment":[{"href":"https:\/\/imastudio.com\/id\/wp-json\/wp\/v2\/media?parent=7325"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/imastudio.com\/id\/wp-json\/wp\/v2\/categories?post=7325"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/imastudio.com\/id\/wp-json\/wp\/v2\/tags?post=7325"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
- Try instantly (no setup)<\/strong>
- Run locally (full control)<\/strong>
- Think High<\/strong> for slower, more deliberate analysis<\/li>\n\n\n\n
- Codeforces-style coding performance<\/strong><\/li>\n\n\n\n
- $0.145 \/ 1M input tokens (cache hit)<\/strong><\/li>\n\n\n\n
- $0.028 \/ 1M input tokens (cache hit)<\/strong><\/li>\n\n\n\n
- SWE Verified<\/strong><\/li>\n\n\n\n
- $0.145 \/ 1M input tokens (cache hit)<\/strong><\/li>\n\n\n\n
- $0.028 \/ 1M input tokens (cache hit)<\/strong><\/li>\n\n\n\n
- Codeforces rating: 3206<\/strong><\/li>\n\n\n\n
- Manifold-Constrained Hyper-Connections (mHC)<\/strong> to improve signal propagation across layers<\/li>\n\n\n\n
- Flash<\/strong> is the value layer, designed to be smaller, faster, and much cheaper for broader deployment<\/li>\n<\/ul>\n\n\n\n
- DeepSeek-V4-Flash<\/strong>: 284B total parameters, 13B activated<\/li>\n<\/ul>\n\n\n\n
- DeepSeek-V4-Pro<\/strong>: 1.6T total parameters, 49B activated<\/li>\n\n\n\n