As of February 16, 2026: Not Released Yet

DeepSeek V4

Flagship Coding Model Release Tracker

DeepSeek V4 is widely expected to be the next flagship model from DeepSeek AI, positioned for repo-level coding, long-context reasoning, and agentic workflows. This page follows current status, rumor-based specs, and leaked benchmarks. If details conflict, this page follows the Feb 16, 2026 reference brief.

View Release Status
deepseek_v4_status.py
1 import deepseek as ds
2
3 # Check whether V4 is officially available
4 status = ds.check_release("deepseek-v4")
5
6 # Fallback to current public model
7 model = ds.load_model(status.available and "deepseek-v4" or "deepseek-v3.2")

Release Status (Feb 16, 2026)

Current launch signal summary based on official surfaces plus market chatter

Not Released Yet

As of Monday, February 16, 2026, DeepSeek V4 has not been officially released.

Current Official Model: V3.2

DeepSeek's official web/app/API surfaces currently highlight DeepSeek-V3.2 as the active model.

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Rumored Timing

Multiple reports point to a near-term launch window around February 17, 2026, near Lunar New Year timing.

🗣️

Delay Chatter Also Exists

Some discussion suggests a possible delay into the next year cycle, but short-term launch expectations remain strong this week.

📡

No Official X Announcement Yet

No confirmed launch tweet from @deepseek_ai was available in the reference snapshot, while leak chatter increased.

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How to Read This Page

This page separates official status from rumors. Leaked metrics are explicitly marked as unverified.

Rumored Specs & Innovations

Compiled from leak discussions and paper-linked technical claims

~1 Trillion Parameters (MoE) Rumor

DeepSeek V4 is repeatedly described as a large MoE system around the 1T parameter class, designed for coding-first workloads while keeping inference practical.

1M+ Token Context Long Context

Leaks point to a 1M+ context target, expanding repo-level analysis and long-horizon reasoning beyond snippet-based interaction.

Engram Memory Core Tech

Described as conditional O(1) memory lookup for massive contexts, based on DeepSeek/Peking University paper discussions addressing transformer memory limits.

mHC Hyper-Connections Reasoning

Manifold-Constrained Hyper-Connections are cited as a mechanism for better information flow and stronger reasoning stability at scale.

Sparse Attention + MODEL1 Efficiency

Circulating claims mention around 40% lower memory use and up to 1.8x faster inference versus older long-context approaches.

Target Strength Profile Coding + Math

Positioning emphasizes SWE-Bench style software tasks, HumanEval coding quality, AIME-level math, and robust long-prompt behavior with open weights.

Leaked Benchmarks (Unverified)

These numbers are rumor-level claims and should not be treated as official results

Benchmark / Metric Claimed V4 Result Claimed Comparison Verification Status
SWE-Bench Verified 83.7% GPT-5.2 ~80%, Claude Opus 4.5 at 80.9% Leak only
HumanEval ~90-92% Presented as top-tier coding output Leak only
AIME 2026 99.4% Some observers question score alignment Disputed
FrontierMath / IMO Very strong claims, incl. "11x better" No reproducible public leaderboard evidence yet Unverified
API Price Expectation $0.01-$0.14 / 1M tokens Framed as a major cost disruption Not officially announced

All benchmark and pricing rows above are leak-derived claims as of February 16, 2026. Official launch materials may differ.

Why It Matters

Potential impact if leaked capability and pricing signals are directionally correct

Cost Pressure on the Market

Expected low API pricing plus open weights could push down development costs for teams building coding agents and automated workflows.

Repo-Scale Agent Workflows

Long context and coding focus target real repo operations: refactoring large codebases, multi-file debugging, and architecture-level reasoning.

Local and Self-Hosted Adoption

Open-weight positioning matters for teams that need private deployment, custom fine-tuning, and tighter control over data handling.

Competitive Acceleration

The V4 cycle is unfolding alongside aggressive moves from other labs, including Qwen 3.5 and Alibaba model lines, raising the pace of iteration.

How to Get DeepSeek V4 (When It Drops)

Practical watchpoints while official release assets are still pending

1

Watch Official Channels

Monitor deepseek.com and DeepSeek platform pages first. Those are the primary sources for confirmed availability.

2

Track GitHub Org

Watch github.com/deepseek-ai for model repo updates, release assets, and inference instructions.

3

Use DeepSeek-V3.2 Now

V3.2 is currently the available option for reasoning/coding workflows on web, app, and API surfaces.

4

Prepare Your Rollout Plan

Predefine evaluation tasks, latency/cost budgets, and local inference tooling so you can test V4 quickly once official assets land.

Notes & Scope

This page follows the reference brief dated February 16, 2026. Where older website copy conflicts, the reference brief takes precedence.

DeepSeek V4 details here combine official status signals with rumor-based technical and benchmark claims. Anything leak-based is explicitly marked unverified.

The practical takeaway is simple: DeepSeek-V3.2 is available now, and V4 appears close but not officially released as of February 16, 2026.

Not Yet Official Release
~1T Params (Rumored)
1M+ Context (Rumored)
83.7% SWE-Bench Leak