In February 2026, Chinese AI startup DeepSeek rolled out a significant update to its online application and web interface, signaling momentum toward its next-generation model release, DeepSeek V4. While the update comes ahead of the full V4 model, it has already sparked conversation among users and industry watchers for its changes to interaction behavior, long-context capabilities, and preparatory testing for future potential.
DeepSeek burst onto the global stage with its earlier variants—most notably DeepSeek V3.2 and DeepSeek–R1—which combined high task performance with cost-effective scalability. The R1 release, in particular, captured international attention in early 2025 for shaking global markets and pushing down competitor stock performance, illustrating DeepSeek’s disruptive potential.
What exactly changed in the recent DeepSeek update?
Which Version Is This and What Changed?
The recent update affects the DeepSeek online application and web interface, but crucially not the API model yet. According to multiple sources:
- The current application update is best described as a long-context structure test — enabling web and app users to access up to 1 million token context support. This is a significant jump from the ~128 K context window in DeepSeek V3.2’s API offering.
- The upgrade increases the effective memory for a single conversation or task, allowing the model to remember and process vastly more information. Reports indicate this is effectively 10× the previous memory capacity — a breakthrough for multi-stage and long-running reasoning.
- In terms of version naming, most public signals suggest this update is a pre-V4 technical push — not yet the formal DeepSeek V4 release, but strongly preparatory for it.
Under the Hood: What’s Driving the Change?
Behind the scenes, DeepSeek’s GitHub repository reveals additions labeled with an internal identifier (“MODEL1”), suggesting a new model architecture distinct from V3.2. The code structure points to memory optimization techniques, FP8 support enhancements, and compatibility with Nvidia’s newer GPU architectures — all core components expected in DeepSeek V4.
In addition, DeepSeek has published research on “Engram,” a memory lookup module that rethinks how large language models manage long contexts and critical facts. Engram appears positioned as a foundational technology for the next generation — possibly powering DeepSeek V4’s extended memory abilities.
User Reactions
The rollout has provoked a wide range of responses:
- On one hand, many users are excited about the context expansion and its potential for deeper interactions and more complex problem solving.
- On the other hand, a significant portion of users has commented publicly about changes in tone and conversational style, describing responses as less engaging, less empathetic, or simply “colder” than before — leading to viral social discussions.
This divergence highlights an important reality of AI deployment: technical capability upgrades can reshape user experience in unexpected ways, requiring iterative refinement before final release.
What Are the Key Features of This Update?
1. Massive Context Expansion
Supporting up to 1 million tokens of context in web/app interaction makes DeepSeek one of the few models capable of global, tear-free understanding of long transcripts, codebases, legal documents, or entire books in one session. This has huge implications for real-world use, from research and writing to enterprise document analysis.
2. Interaction Style Changes
The recent rollout has noticeably changed DeepSeek’s conversational tone. Many users have remarked that the updated model interaction appears more neutral or “plain” — using generic identifiers like “User” rather than personalized nicknames and offering more concise responses in deep reasoning modes. These stylistic shifts have generated buzz on social media platforms, with some users expressing discomfort or surprise.
3. Knowledge Cutoff and Updated Context
The knowledge base behind the app was updated to reflect information through May 2025, though API service remains on V3.2 with its previous knowledge cutoff. This split suggests DeepSeek is experimenting with incremental improvements ahead of a full V4 platform upgrade.
4. Preparations for V4 Integration
One clear strategic goal of the update is to test infrastructure and user experience in advance of the upcoming DeepSeek V4. Large context support and memory changes likely serve as a real-world stress test for the architectures now under development — helping developers assess performance, reliability, and feedback before full deployment.
What new technical features are included in Updating and how do they work?
User Reactions
The rollout has provoked a wide range of responses:
- On one hand, many users are excited about the context expansion and its potential for deeper interactions and more complex problem solving.
- On the other hand, a significant portion of users has commented publicly about changes in tone and conversational style, describing responses as less engaging, less empathetic, or simply “colder” than before — leading to viral social discussions.
This divergence highlights an important reality of AI deployment: technical capability upgrades can reshape user experience in unexpected ways, requiring iterative refinement before final release.
Engram: conditional memory for selective recall
Engram is the marquee idea in the update. Conceptually it’s a conditional retrieval mechanism embedded within the model architecture: when the input contains cues tied to stored engrams, the network retrieves precomputed vector representations to supplement (or sometimes replace) expensive layers of inference. The claimed benefit is twofold: reduce repeated computation on static knowledge, and provide a robust mechanism for updating or patching factual memory without retraining the full model. Technical summaries and developer previews show Engram intended for both code knowledge (libraries, function signatures) and factual recall across documents.
mHC (manifold-constrained hyperconnections)
mHC, as presented in the preview and supportive technical notes, is an architectural strategy aimed at constraining parameter interactions to meaningful submanifolds. That constraint reduces the number of pairwise activations that must be computed, improving compute efficiency during both training and inference. The theory is that you preserve expressive power where it matters (task-relevant manifolds) while cutting wasteful computation elsewhere — effectively squeezing more utility out of the same hardware. Early descriptions are technical and promising, but they also raise implementation and verification questions (see below).
DeepSeek Sparse Attention (DSA) and million-token contexts
One of the most tangible claims is support for 1M+ token contexts through a mix of sparse attention techniques and dynamic triggering logic. If realized in production, this allows a single inference pass to consider whole repositories, long transcripts, or multi-file patches — a boost for tasks like codebase summarization, multi-file refactors, and long conversational agents. Preview materials and vendor benchmarks report large-context throughput and suggest significant efficiency gains compared with some competitors. Independent verification is still limited at this stage.
What can we expect next — and what does this update tell us about DeepSeek v4?
Short answer: the public update is both a functional enhancement and a staging ground for a bigger launch. Industry reporting and DeepSeek’s own timeline point to an imminent v4 launch (targeted for the Lunar New Year window) that will likely package long-context memory, specialized Engram-like memory architecture, and improved coding and agent capabilities.
Below is a careful, evidence-based speculation on what DeepSeek v4 will likely include — grounded in current change signals and industry expectations.
Expectation 1 — Native long-lived memory and indexed retrieval
Given the app’s million-token experiments and the explicit focus on agents in V3.2, v4 is likely to formalize a memory subsystem that persists indexed knowledge across sessions (not merely a larger ephemeral context). That subsystem would combine:
- Dense retrieval over stored embeddings.
- Efficient chunking to balance latency and token cost.
- A coherence layer to stitch retrieved fragments into the model’s internal context window.
If implemented, that would let agents maintain persistent personalities, user preferences, and rich project history without re-ingesting data each session.
Expectation 2 — Specialized code-generation and multi-file reasoning
Coding prowess as a priority for v4, hinting at model optimizations and benchmark improvements targeting developer workflows. Expect native multi-file refactor capabilities, improved unit-test synthesis, and tool-aware code generation that can run, evaluate, and iterate on code via sandboxed toolchains. These are exactly the types of tasks unlocked by long-context models.
Expectation 3 — Greater emphasis on agent safety and verification
Given public scrutiny about training practices, DeepSeek will likely prioritize auditability: reproducible training logs, clearer provenance statements, and hardened safety mitigations that flag hallucinations or provenance gaps during multi-step tool interactions. Expect product features that make provenance visible to enterprise customers and researchers.
Expectation 4 — Competitive roadmap and partner ecosystem
The v4 roadmap will be read as a market signal by domestic and global players. With rivals shipping aggressive updates (from major players targeting efficiency and mobile deployment to niche players doubling down on open-source models), DeepSeek must balance openness and defensibility. If v4 delivers significant gains at lower cost, it will accelerate the trend toward affordable, high-capability models in China and beyond — and likely intensify cross-border policy scrutiny.
In Conclusion: A Growing AI Force
The recent DeepSeek update marks a meaningful step toward a broader transformation in AI intelligence. While the company hasn’t fully launched V4 yet, the preview enhancements — especially around context length and interactive restructuring — reveal a commitment to pushing LLM capabilities forward. With V4 on the horizon, DeepSeek is poised to be a central figure in shaping the next era of large-scale, cost-efficient, high-performance AI.
Developers can access Deepseek API via CometAPI now.To begin, explore the model’s capabilities in the Playground and consult the API guide for detailed instructions. Before accessing, please make sure you have logged in to CometAPI and obtained the API key. CometAPI offer a price far lower than the official price to help you integrate.
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