DeepSeek V4 Targets Coding Dominance with Mid-February Launch
DeepSeek's upcoming V4 model enters final preparation for a mid-February 2026 release, incorporating the company's newly published Engram memory architecture and targeting performance that internal benchmarks suggest surpasses both Claude and GPT in code generation tasks.
TL;DR
DeepSeek plans to release V4 around February 17, 2026, coinciding with Lunar New Year. The model integrates Engram conditional memory technology published January 13, enabling efficient retrieval from contexts exceeding one million tokens. Internal benchmarks reportedly show V4 outperforming Claude and GPT series in long-context code generation. The anticipated open-source release would make V4 one of the most capable freely available coding models, continuing DeepSeek's pattern of matching proprietary performance at dramatically lower inference costs.
Release Timeline and Strategy
DeepSeek targets mid-February 2026 for V4's debut, according to The Information citing people with direct knowledge of the project [1]. The company aims for release around February 17, aligning with Lunar New Year festivities [2].
| Release | Date | Strategic Pattern |
|---|---|---|
| DeepSeek R1 | January 20, 2025 | One week before Lunar New Year |
| DeepSeek V4 | ~February 17, 2026 | Lunar New Year |
The timing mirrors DeepSeek's R1 launch strategy. That release triggered a $1 trillion tech stock selloff on January 27, 2025, including $600 billion from NVIDIA alone [3]. DeepSeek leverages the Lunar New Year period for maximum visibility in both Chinese and international markets.
DeepSeek has declined to comment on specific release timing [4]. The company maintains operational silence despite extensive technical publications hinting at V4's architecture.
Engram: The Architectural Foundation
DeepSeek published research on January 13, 2026 introducing Engram, a conditional memory system that separates static pattern retrieval from dynamic reasoning [5]. Industry observers immediately connected Engram to V4's architecture.
How Engram Works
Traditional Transformers force models to store factual knowledge within reasoning layers, creating computational inefficiency. Engram offloads static memory to a scalable lookup system [6].
| Component | Function | Resource |
|---|---|---|
| Dynamic Reasoning | Active computation | GPU HBM |
| Static Memory | Pattern retrieval | System DRAM |
| Optimal Split | 75% / 25% | Per DeepSeek research |
The researchers demonstrated a 100-billion-parameter embedding table entirely offloaded to host DRAM with throughput penalties below 3% [7]. A leaked GitHub repository (DeepSeek-Engram) suggests V4 may use a "hashed token n-gram" system enabling recall from contexts exceeding one million tokens [8].
Benchmark Results
Engram-27B testing against a standard 27B MoE baseline showed consistent improvements [9]:
| Benchmark Category | Performance Gain |
|---|---|
| Knowledge Tasks | +3.4 to +4.0 points |
| BBH (General Reasoning) | +5.0 points |
| ARC-Challenge | +3.7 points |
| HumanEval (Code) | +3.0 points |
| MATH | +2.4 points |
| NIAH (Long-Context) | 97% vs 84.2% |
The Needle in a Haystack improvement from 84.2% to 97% represents the most significant gain, directly relevant to V4's coding focus where long-context coherence determines practical utility [10].
Coding Performance Claims
People with direct knowledge of the project claim V4 outperforms both Anthropic's Claude and OpenAI's GPT series in internal benchmarks, particularly when handling extremely long code prompts [11].
Current Competitive Landscape
| Model | SWE-bench Verified | Context Length |
|---|---|---|
| Claude Opus 4.5 | 80.9% | 200K tokens |
| GPT-5.2 | ~75% | 400K tokens |
| DeepSeek V3.2-Speciale | ~70% | 128K tokens |
| DeepSeek V4 (rumored) | TBD | 1M+ tokens |
To claim coding dominance, V4 would need to beat Claude Opus 4.5's 80.9% on SWE-bench Verified [12]. DeepSeek's V3.2 already demonstrates gold-medal performance in the 2025 International Olympiad in Informatics (IOI) and ICPC World Finals without targeted training [13].
Specific Coding Capabilities
Internal reports describe V4 solving complex repository-level bugs that cause other models to hallucinate or enter loops [14]. Key claimed advantages include:
- Multi-file reasoning: Managing project-wide logic across large codebases
- Long-context stability: Maintaining coherence over significantly longer prompts
- Inference efficiency: Lower per-token costs than V3 despite improved performance
- Structural coherence: Preserving code architecture during refactoring operations
Wei Sun, principal analyst for AI at Counterpoint Research, described DeepSeek's published techniques as a "striking breakthrough" demonstrating the company can "bypass compute bottlenecks and unlock leaps in intelligence" despite U.S. export restrictions [15].
Technical Architecture
V4 builds on DeepSeek's Mixture-of-Experts (MoE) foundation while integrating multiple architectural innovations.
Expected Specifications
| Specification | V3 Baseline | V4 Expected |
|---|---|---|
| Parameters | 671B | 700B+ |
| Active Parameters | ~37B/token | ~40B/token |
| Architecture | MoE | MoE + Engram |
| Context Length | 128K | 1M+ |
| Training Method | Standard | mHC enhanced |
Manifold-Constrained Hyper-Connections (mHC)
DeepSeek's January 1, 2026 paper introduced mHC, a framework addressing fundamental problems in scaling large language models [16]. The technique improves scalability while reducing computational and energy demands during training.
Co-authored by founder Liang Wenfeng, mHC enables "aggressive parameter expansion" by bypassing GPU memory constraints [17]. The approach allows training larger models on the same hardware that would otherwise limit capacity.
Model Variants
V4 reportedly includes two configurations [18]:
| Variant | Optimization | Target Use |
|---|---|---|
| V4 Flagship | Heavy long-form coding, complex projects | Enterprise development |
| V4 Lite | Speed, responsiveness, cost efficiency | Daily interaction |
Competitive Implications
V4's arrival intensifies pressure on Western AI providers who have maintained coding benchmark leadership.
Market Position Comparison
| Provider | Model | Pricing Model | Open Source |
|---|---|---|---|
| Anthropic | Claude Opus 4.5 | $5/$25 per M tokens | No |
| OpenAI | GPT-5.2 | $10/$30 per M tokens | No |
| DeepSeek | V4 | Expected <$1/$2 per M tokens | Likely Yes |
DeepSeek's consistent pattern of open-sourcing flagship models under permissive licenses creates competitive pressure beyond direct performance comparisons [19]. Both V3 and R1 received open releases, and speculation suggests V4 will follow [20].
Infrastructure Implications
DeepSeek's efficiency focus produces models runnable on consumer hardware. V4's MoE architecture reportedly enables dual RTX 4090s or single RTX 5090s to run "GPT-5 class" performance locally [21]. Organizations deploying AI infrastructure can evaluate these tradeoffs between cloud API costs and local compute investments.
Introl's coverage area supports GPU deployments across 257 global locations, enabling enterprises to evaluate local inference options as open-source models like V4 approach proprietary performance levels. The company's 550 HPC-specialized engineers manage installations scaling to 100,000 GPUs.
Regulatory Context
V4's release occurs amid increasing international scrutiny of DeepSeek's data practices.
Government Actions
| Country | Action | Date |
|---|---|---|
| Australia | Banned from government devices | February 2025 |
| Czech Republic | Banned from public administration | 2025 |
| Netherlands | Privacy investigation launched | 2025 |
These restrictions target DeepSeek's consumer products rather than open-source model weights [22]. Enterprises can self-host open-source releases without exposure to DeepSeek's API infrastructure.
What V4 Must Demonstrate
Several claims require validation upon release:
Benchmark Targets
| Claim | Verification Method |
|---|---|
| Beats Claude/GPT in coding | SWE-bench Verified score |
| 1M+ token context | RULER and NIAH benchmarks |
| Lower inference cost | Published throughput metrics |
| Open-source release | License and weight availability |
Key Unknowns
- Whether Engram integration matches research paper results at V4's scale
- Actual context length limits in production deployment
- Inference cost comparisons with detailed hardware specifications
- Timeline and scope of any open-source release
Key Takeaways
For AI Engineers
- Engram architecture offers potential path to efficient long-context processing
- V4's MoE design may enable local inference at competitive quality levels
- Monitor SWE-bench and RULER benchmarks for objective performance validation
For Enterprise Decision-Makers
- Open-source V4 could reduce dependency on proprietary API providers
- Self-hosting options may address data sovereignty concerns
- Evaluate total cost of ownership including GPU infrastructure for local deployment
For Infrastructure Teams
- Consumer-grade hardware (RTX 5090) potentially sufficient for V4 inference
- MoE architectures reduce active parameter counts, lowering VRAM requirements
- Plan capacity for organizations shifting from cloud APIs to local inference
References
[1] The Information. "DeepSeek To Release Next Flagship AI Model With Strong Coding Ability." January 2026. https://www.theinformation.com/articles/deepseek-release-next-flagship-ai-model-strong-coding-ability
[2] Vertu. "DeepSeek V4 Guide: Coding Benchmarks, Engram Memory & Release Date." January 2026. https://vertu.com/lifestyle/deepseek-v4-is-coming-everything-we-know-about-the-coding-monster/
[3] CNBC. "DeepSeek blew up markets a year ago." January 2026. https://www.cnbc.com/2026/01/06/why-deepseek-didnt-cause-an-investor-frenzy-again-in-2025.html
[4] South China Morning Post. "DeepSeek stays mum on next AI model release." January 2026. https://www.scmp.com/tech/tech-trends/article/3339769/deepseek-stays-mum-next-ai-model-release-technical-papers-show-frontier-innovation
[5] Tom's Hardware. "DeepSeek touts memory breakthrough, Engram conditional memory module." January 2026. https://www.tomshardware.com/tech-industry/artificial-intelligence/deepseek-touts-memory-breakthrough-engram
[6] VentureBeat. "DeepSeek's conditional memory fixes silent LLM waste." January 2026. https://venturebeat.com/data/deepseeks-conditional-memory-fixes-silent-llm-waste-gpu-cycles-lost-to
[7] GitHub. "deepseek-ai/Engram: Conditional Memory via Scalable Lookup." https://github.com/deepseek-ai/Engram
[8] Vertu. "DeepSeek V4: New Engram AI Beats GPT-4o in Coding." January 2026. https://vertu.com/lifestyle/deepseek-v4-guide-engram-architecture-release-date-and-coding-benchmarks/
[9] 36Kr. "Liang Wenfeng Publicly Releases Memory Module." January 2026. https://eu.36kr.com/en/p/3637114445349889
[10] BigGo News. "DeepSeek's Engram: A Memory Breakthrough." January 2026. https://biggo.com/news/202601132021_DeepSeek-Engram-AI-Memory-Breakthrough
[11] Decrypt. "Insiders Say DeepSeek V4 Will Beat Claude and ChatGPT at Coding." January 2026. https://decrypt.co/354177/insiders-say-deepseek-v4-beat-claude-chatgpt-coding-launching-weeks
[12] Fello AI. "DeepSeek V4 Has the Internet Buzzing." January 2026. https://felloai.com/deepseek-v4-leaks-vs-chatgpt-gemini-claude/
[13] arXiv. "DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models." https://arxiv.org/html/2512.02556v1
[14] Techzine Global. "DeepSeek to release V4 AI model with powerful coding capabilities." January 2026. https://www.techzine.eu/news/analytics/137800/deepseek-to-release-v4-ai-model-with-powerful-coding-capabilities-in-february/
[15] Bloomberg. "DeepSeek Touts New Training Method as China Pushes AI Efficiency." January 2026. https://www.bloomberg.com/news/articles/2026-01-02/deepseek-touts-new-training-method-as-china-pushes-ai-efficiency
[16] South China Morning Post. "DeepSeek kicks off 2026 with paper signalling push to train bigger models." January 2026. https://www.scmp.com/tech/big-tech/article/3338427/deepseek-kicks-2026-paper-signalling-push-train-bigger-models-less
[17] South China Morning Post. "DeepSeek founder's latest paper proposes new AI model training." January 2026. https://www.scmp.com/tech/tech-trends/article/3339740/deepseek-founders-latest-paper-proposes-new-ai-model-training-bypass-gpu-limits
[18] Geeky Gadgets. "AI News: DeepSeek V4 Aims at Long Code & February Launch." January 2026. https://www.geeky-gadgets.com/gmail-gemini-upgrades/
[19] Parsers.vc. "DeepSeek V4: China's AI Challenger Targets Coding Supremacy." January 2026. https://parsers.vc/news/260112-deepseek-v4--china-s-ai-challenger-targets/
[20] Edward Kiledjian. "DeepSeek V4: Next-generation AI model targets coding dominance." January 2026. https://kiledjian.com/2026/01/09/deepseek-v-nextgeneration-ai-model.html
[21] xpert.digital. "China & new AI model DeepSeek V4." January 2026. https://xpert.digital/en/deepseek-v4/
[22] Insurance Journal. "Governments, Regulators Increase Scrutiny of Chinese AI Startup DeepSeek." January 2026. https://www.insurancejournal.com/news/international/2026/01/07/853376.htm
[23] VARINDIA. "DeepSeek set to unveil V4 model, raising stakes in global AI race." January 2026. https://www.varindia.com/news/deepseek-set-to-unveil-v4-model-raising-stakes-in-global-ai-race
[24] Digitimes. "DeepSeek V4 update: Conditional memory reshapes large-model efficiency." January 2026. https://www.digitimes.com/news/a20260113PD241/deepseek-language.html
[25] 36Kr. "New Paper by Liang Wenfeng: Is DeepSeek V4's Architecture Revealed." January 2026. https://eu.36kr.com/en/p/3637163406624008
[26] X/Twitter. "Ice Universe on DeepSeek-V4 Architecture." January 2026. https://x.com/UniverseIce/status/2010874588650225757
[27] Hugging Face. "deepseek-ai/DeepSeek-V3.2-Exp." https://huggingface.co/deepseek-ai/DeepSeek-V3.2-Exp
[28] arXiv. "DeepSeek-V3 Technical Report." https://arxiv.org/pdf/2412.19437
[29] Thesys. "DeepSeek V3.2: Performance, Benchmarks, and Tradeoffs." https://www.thesys.dev/blogs/deepseek-v3-2
[30] Medium. "DeepSeek V3.2-Exp Review." https://medium.com/@leucopsis/deepseek-v3-2-exp-review-49ba1e1beb7c
[31] DeepSeek API Docs. "Introducing DeepSeek-V3.2-Exp." https://api-docs.deepseek.com/news/news250929
[32] MGX Dev. "DeepSeek V3.2 Review: Performance Benchmarks." https://mgx.dev/blog/deepseek-v3-2-agents-benchmarks
[33] Recode China AI. "DeepSeek-V3.2: Outperforming Through Verbosity." https://recodechinaai.substack.com/p/deepseek-v32-make-scaling-laws-keep
[34] NVIDIA NIM. "deepseek-v3.1 Model by Deepseek-ai." https://build.nvidia.com/deepseek-ai/deepseek-v3_1/modelcard
[35] South China Morning Post. "Another Chinese quant fund joins DeepSeek in AI race." January 2026. https://www.scmp.com/tech/big-tech/article/3338794/another-chinese-quant-fund-joins-deepseek-ai-race-model-rivalling-gpt-51-claude
[36] Yahoo Finance. "DeepSeek set to launch next-gen V4 model." January 2026. https://finance.yahoo.com/news/deepseek-set-launch-next-gen-153258894.html
[37] RSWebsols. "DeepSeek V4 Release, AI Set to Surpass GPT in Coding." January 2026. https://www.rswebsols.com/news/deepseek-v4-release-ai-model-poised-to-surpass-gpt-in-coding-abilities/
[38] Aitoolsbee. "DeepSeek V4 to launch in February, aiming to beat GPT and Claude." January 2026. https://aitoolsbee.com/news/deepseek-v4-to-launch-in-february-aiming-to-beat-gpt-and-claude/
[39] Apiyi. "DeepSeek V4 Coming Soon: Programming Capabilities May Surpass Claude and GPT." January 2026. https://help.apiyi.com/en/deepseek-v4-release-coding-ai-model-en.html
[40] Caixin Global. "Year in Review: DeepSeek's Breakout Rewrites U.S.-China AI Race." January 2026. https://www.caixinglobal.com/2026-01-01/year-in-review-deepseeks-breakout-rewrites-us-china-ai-race-102399261.html
