DeepSeek V4 Latest Information Roundup (as of 28 January 2026): What's Confirmed? What's Merely Rumour? And How Should You Prepare Now?
Over the past fortnight, "DeepSeek V4" has dominated developer circles for a straightforward reason: rumours suggest it will be a next-generation flagship model heavily geared towards programming. Crucially, its breakthrough lies not in "better handling short prompts," but in its ability to process exceptionally lengthy and complex code prompts – a capability of immense significance for real-world engineering scenarios (large repositories, cross-file operations, extended context).
However, let us establish the baseline:
**As of 28 January 2026, DeepSeek has not publicly released V4's model specifications, an officially accessible V4 API, or a formal announcement.** The model currently promoted and available from the company is DeepSeek-V3.2 (chat / reasoner), featuring 128K context window, with full specifications and pricing detailed in its API documentation.
Therefore, this article organises all core information from online sources using a "tiered credibility" approach: Fact Layer (verifiable) / Reporting Layer (authoritative media but non-official) / Speculation Layer (community and secondary interpretations). Finally, it provides a "Preparation Checklist Before V4 Launch" and "Key Indicators to Monitor".
1) Current most authoritative report: V4 expected to launch in mid-February 2026, focusing on programming + extremely long coding prompts
The highest-quality information regarding V4's release date and positioning currently comes from Reuters (citing The Information):
Release date: Expected mid-February 2026
Positioning: Next-generation model V4, with enhanced coding capabilities
Key feature: Significant improvement in handling **extremely long coding prompts**
Contrasting claims: Internal testing suggests potential superiority over leading models in coding tasks (Note: This is an "internal testing claim", not externally verified)
Status: DeepSeek has not commented on the report; Reuters could not independently verify
Numerous secondary online sources further specify the date as "17th February" (as the 2026 Lunar New Year falls on 17th February), but this constitutes media/community speculation rather than official confirmation.
2) Official "Verifiable Facts": The currently operational model is DeepSeek-V3.2 (128K, tool invocation, context cache, explicit pricing)
Until V4 is formally launched, the "hard facts" you can rely on are documented in DeepSeek's official API documentation:
Model: deepseek-chat, deepseek-reasoner correspond to DeepSeek-V3.2 (non-reasoning/reasoning)
Context length: 128K
Features: JSON Output, Tool Calls, (Beta) Prefix Completion, (Beta) FIM Completion (chat supported)
Pricing: Input tokens charged per cache hit/miss; output billed separately (specific rates provided in official pricing table)
This signifies that even before V4 arrives, DeepSeek has already established foundational infrastructure at the interface layer for agent-oriented/long-context engineering tasks (e.g., context caching enabled by default).
3) Why is there widespread belief that V4 will be "more engineering-ready"? Two strong indicators: Engram (conditional memory) and mHC (scalable training stability).
Relying solely on hearsay that "V4 is superior" feels rather vague; however, DeepSeek has recently published two research achievements verifiable through papers and code, both highly relevant to "long context + engineering-grade encoding".
3.1 Engram: Decoupling "memory/lookup" from computationally expensive operations to enhance model suitability for ultra-long contexts
The DeepSeek team published the paper Conditional Memory via Scalable Lookup (arXiv: 2601.07372) in January 2026, alongside open-sourcing the corresponding implementation repository deepseek-ai/Engram. Key innovations include:
Proposing conditional memory as a novel "sparse axis" to complement capabilities beyond MoE
Achieving near O(1) knowledge/pattern retrieval via Engram (a modernised implementation based on N-gram embeddings)
The paper reports improvements across inference, code, mathematics, and long-context retrieval metrics (e.g., HumanEval, long-context retrieval benchmarks)
Emphasising infrastructure friendliness: offloading massive tables to host memory with deterministic addressing for prefetching, reducing GPU memory bottlenecks
This approach aligns closely with Reuters' "very long coding prompts" direction: when prompts contain extensive code, dependencies, and context, prioritising "lookup" over "computation" frees computational resources for genuinely challenging reasoning and planning tasks.
3.2 mHC: Addressing Stability and Efficiency for "Scalable Training"
Another development is mHC: Manifold-Constrained Hyper-Connections (submitted to arXiv on 31 December 2025). The paper's core insight is that while more complex connection structures (Hyper-Connections) yield performance gains, they risk disrupting the identity mapping of residual connections, leading to training instability and increased computational overhead. mHC restores properties by "projecting onto specific manifolds" and implements engineering optimisations to enable more stable, scalable training.
Business Insider's coverage also interprets mHC as a significant breakthrough for DeepSeek in "scalable training", speculating it may relate to their next-generation model direction (note: this remains media conjecture).
4) How to view rumours and "leaks": Read them, but take them with a pinch of salt
You may encounter articles claiming "MODEL1 leaked" or "V4 architecture exposed". Such content typically lacks verifiable evidence (no official model specifications, no callable endpoints, no formal release notes), making it more suitable as "community buzz" than decision-making material.
A simple evaluation principle:
Can it be traced back to (a) official documentation/announcements, (b) arXiv papers, (c) official GitHub repositories, or (d) authoritative media reports? If not, reduce its weighting.
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