Key facts: DeepSeek V4 is expected mid-February 2026 (tentative - not officially confirmed). Two official research papers (Engram, mHC) suggest the technical foundation. Internal testing reportedly shows coding performance exceeding Claude and GPT - but this is unverified. DeepSeek V3 costs 5-15x less than competitors. V4 is expected to maintain this cost advantage with MIT-style licensing.
DeepSeek V4 is generating significant buzz in the AI community. But separating verified facts from speculation is critical before making any decisions.
After researching official DeepSeek sources, published papers, and credible news reports, here's everything we actually know - and what remains unconfirmed.
Important Disclaimer
As of January 19, 2026, DeepSeek has NOT officially announced V4. All release dates and performance claims come from third-party reports citing anonymous sources. This article clearly distinguishes between confirmed, reported, and rumored information.
What's Actually Verified vs. Rumored
Understanding the reliability of available information.
Information Verification Status
| Claim | Status | Source |
|---|---|---|
| Release: Mid-February 2026 | REPORTED | The Information (insider sources) |
| Engram memory system | CONFIRMED | Official arXiv paper + GitHub |
| mHC architecture | CONFIRMED | Official arXiv paper |
| Engram/mHC in V4 | SPECULATED | Third-party analysis |
| Beats Claude/GPT at coding | UNVERIFIED | Internal testing claims |
| 1 trillion parameters | RUMORED | No official source |
| 1M token context | RUMORED | No official source |
| Open source (MIT) | EXPECTED | Based on V3 precedent |
The key takeaway: DeepSeek's research papers on Engram and mHC are real and verified. Whether these technologies will be integrated into V4 is speculation - albeit reasonable speculation given DeepSeek's track record of publishing research ahead of model releases.
Release Date: Mid-February 2026 (Tentative)
What we know about the expected launch timeline.
According to The Information (January 9, 2026), citing two people with direct knowledge of the project, DeepSeek is targeting a release around mid-February 2026 - coinciding with the Lunar New Year on February 17.
Historical Context
This timing follows DeepSeek's pattern with R1, which released on January 20, 2025 - one week before Chinese New Year. DeepSeek appears to favor major releases around this holiday period.
Important caveats:
- Reuters stated it could not independently verify The Information's report
- DeepSeek did not respond to requests for comment
- No official announcement has been made on DeepSeek's website, GitHub, or social channels
Earlier projections based on DeepSeek's 7-month release cadence (V1: October 2023, V2: May 2024, V3: December 2024) had suggested July 2025, which did not materialize.
Technical Architecture: What's Actually Confirmed
The verified research that may power V4.
Engram: Conditional Memory System (Confirmed)
DeepSeek published the Engram paper on arXiv (2601.07372) in January 2026, with an accompanying GitHub repository. This is verified DeepSeek research.
How Engram works:
- Uses modernized hashed N-gram embeddings for O(1) memory retrieval
- Stores static knowledge in system RAM (not GPU VRAM)
- A gating mechanism filters retrieved memory based on context
- Allocates 75% of model capacity to dynamic reasoning, 25% to static lookups
Engram Performance Results (Official Paper)
| Benchmark Type | Without Engram | With Engram | Improvement |
|---|---|---|---|
| Complex reasoning | 70% | 74% | +4 points |
| Knowledge-focused | 57% | 61% | +4 points |
Source: arXiv:2601.07372
mHC: Manifold-Constrained Hyper-Connections (Confirmed)
DeepSeek published the mHC paper on arXiv (2512.24880) in December 2025. This addresses a critical problem in scaling MoE models.
The problem it solves: Standard Hyper-Connections caused signal gains exceeding 3000x in a 27B parameter model, leading to training instability.
The solution: mHC constrains connection matrices to be doubly stochastic using the Sinkhorn-Knopp algorithm, controlling signal amplification to just 1.6x.
mHC Performance Results (Official Paper)
| Metric | Result |
|---|---|
| BIG-Bench Hard improvement | +2.1% |
| Training overhead | Only 6.7% |
| Signal amplification | 1.6x (vs 3000x unconstrained) |
Source: arXiv:2512.24880
Speculated V4 Specifications (Unverified)
Based on third-party analysis and leaks, V4 may include:
Rumored V4 Specifications
| Specification | Rumored Value | V3 (Confirmed) | Confidence |
|---|---|---|---|
| Total parameters | ~1 trillion | 671B | Low |
| Active parameters | ~32B | 37B | Low |
| Context window | 1M tokens | 128K | Low |
| Architecture | MoE + Engram + mHC | MoE | Medium |
No Official V4 Paper
As of January 19, 2026, no official DeepSeek V4 technical report has been published on arXiv. All specifications above are based on third-party speculation.
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Expected Benchmarks: Claims vs. Reality
What internal testing reportedly shows - and why we can't verify it.
Reported V4 Performance (Unverified)
According to The Information's sources:
- Internal testing shows V4 outperforming Claude 3.5 Sonnet and GPT-4o on coding tasks
- Particularly strong on "extremely long code prompts"
- Claims of solving "repository-level bugs that cause other models to hallucinate"
Why we can't verify this: No benchmark scores have been publicly released. Internal testing often uses favorable configurations. Independent verification will only be possible after release.
DeepSeek V3 Benchmarks (Verified Baseline)
For context, here's what DeepSeek V3 actually achieved:
DeepSeek V3 Official Benchmarks
| Benchmark | V3 Score | Current Leader |
|---|---|---|
| SWE-bench Verified | 42.0% | Claude Opus 4.5: 80.9% |
| HumanEval (base) | 65.2% | — |
| GSM8K (math) | 89.3% | — |
| MMLU | 88.5% | — |
| GPQA-Diamond | 59.1% | Gemini 3 Pro: 91.9% |
Source: DeepSeek V3 Technical Report (arXiv:2412.19437)
Current Coding Benchmark Leaders (January 2026)
SWE-bench Verified Leaderboard
| Model | Score | Status |
|---|---|---|
| Claude Opus 4.5 | 80.9% | Current record holder |
| GPT-5.2 | 80.0% | — |
| Claude Sonnet 4.5 | 77.2% | — |
| DeepSeek V3 | 42.0% | — |
For V4 to claim "coding dominance," it would need to beat Claude Opus 4.5's 80.9% on SWE-bench Verified - nearly double V3's score.
Pricing: The DeepSeek Advantage
What V4 pricing might look like based on V3.
No official V4 pricing has been announced. However, DeepSeek's V3 pricing provides a reliable baseline:
Current API Pricing Comparison
| Model | Input/1M tokens | Output/1M tokens | vs DeepSeek V3 |
|---|---|---|---|
| DeepSeek V3 | $0.56 | $1.68 | Baseline |
| GPT-5 | $1.25 | $10.00 | 2-6x more expensive |
| Claude Sonnet 4.5 | $3.00 | $15.00 | 5-9x more expensive |
| Claude Opus 4.5 | $5.00 | $25.00 | 9-15x more expensive |
Cost Advantage
DeepSeek's primary competitive advantage is cost. V3 is approximately 5-15x cheaper than Western alternatives. V4 is expected to maintain or improve this pricing advantage.
Open Source Licensing (Expected)
DeepSeek V3 was released under:
- Code: MIT License (extremely permissive)
- Model weights: DeepSeek Model License (allows commercial use, fine-tuning, and distillation)
V4 is expected to follow a similar open-source approach, though this is not confirmed.
How V4 Might Stack Up Against Competition
Context for evaluating V4 when it releases.
vs. GPT-5.2
- GPT-5.2 strengths: 80.0% SWE-bench, adaptive routing, broad ecosystem integration
- DeepSeek advantage: Expected 5-10x cost reduction, open-source availability
- V4 claim: Internal testing reportedly beats GPT series on long code prompts (unverified)
vs. Claude Opus 4.5
- Claude strengths: 80.9% SWE-bench record, superior spatial logic consistency
- DeepSeek advantage: Expected 9-15x cost reduction, self-hosting option
- V4 must beat: 80.9% on SWE-bench to claim coding leadership
vs. Qwen3-Max
- Both use: Mixture-of-Experts architecture
- Both offer: Significant cost advantages over Western models
- Qwen3 verified: 92.7% HumanEval (Qwen 2.5-Max)
- Competition: Both vying for "best Chinese AI model" position
For a detailed benchmark comparison between DeepSeek and Qwen3, see our DeepSeek vs Qwen3 comparison.
Concerns and Limitations
Important factors to consider before adoption.
Security Vulnerabilities
A Cisco study found DeepSeek failed to block a single harmful prompt in security assessments:
Safety Benchmark Comparison
| Model | Harmful Prompts Blocked |
|---|---|
| GPT-4o | 86% |
| Google Gemini | 64% |
| DeepSeek | 0% |
Source: Cisco Security Research
Government Restrictions
Italy, Taiwan, Australia, and South Korea have blocked or banned DeepSeek on government devices. NASA and the US Navy have instructed employees against using DeepSeek.
Other Limitations
- Infrastructure issues: DeepSeek has struggled with server capacity, suspending new signups and API top-ups due to demand
- Context window limits: Native 128K context often capped at 64K by third-party providers
- Consistency: Users report Claude maintains better spatial logic consistency
- IP concerns: OpenAI has raised concerns about potential "inappropriate distillation" of its models
The Bottom Line
What to expect and how to prepare.
What We Know for Sure
- DeepSeek has published verified research on Engram and mHC architectures
- DeepSeek V3 is 5-15x cheaper than GPT-5 and Claude
- DeepSeek V3 is open-source under permissive licensing
- Current coding leader is Claude Opus 4.5 at 80.9% SWE-bench
What's Likely But Unconfirmed
- Release around mid-February 2026 (tentative)
- Coding-focused improvements over V3
- Integration of Engram and/or mHC technologies
- Continued cost advantage and open-source availability
What to Watch For
V4 Evaluation Checklist (Post-Release)
- 1Official benchmark scores on SWE-bench Verified (target: >80.9%)
- 2Independent third-party testing (not just internal benchmarks)
- 3Actual pricing compared to V3 and competitors
- 4Open-source availability and licensing terms
- 5Security assessments from independent researchers
DeepSeek V4 has the potential to be significant - the Engram and mHC research is real and impressive. But until official benchmarks are published and independently verified, treat all performance claims with appropriate skepticism.
The most reliable advantage DeepSeek offers is cost. If V4 maintains V3's pricing while improving coding capabilities even modestly, it will remain a compelling option for cost-conscious developers and enterprises.
We'll update this article when official V4 information becomes available.
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