🧠 Awesome DeepSeek V4 — Everything We Know About the Next-Gen Coding AI

Language / 语言: English | 中文 | 日本語 | 한국어

⚠️ DeepSeek V4 has not been officially released yet. This guide tracks everything we know from leaks, insider reports, research papers, and official hints. Information is subject to change.

What is DeepSeek V4?
Current Status & Timeline
Expected Features Deep Dive
Architecture
Coding Capabilities
Expected Benchmarks
Competitive Landscape
- GLM-5 / GLM-5-Turbo (Zhipu AI)
- MiniMax M2.5 / M2.7
What to Use While Waiting for V4
Timeline of Announcements & Delays
Prepare Now with Atlas Cloud
Atlas Cloud API Quick Start
- Python
- cURL
- Node.js
Pricing
V3.2 vs V4 Feature Comparison
Community Resources
FAQ
Contributing
Star History

What is DeepSeek V4?

DeepSeek V4 is the upcoming next-generation large language model from DeepSeek, the Chinese AI lab that has been making waves with its open-weight models. Building on the success of DeepSeek V3 and V3.2, the V4 model is expected to represent a generational leap in AI capabilities — particularly for software engineering, code comprehension, and long-context reasoning.

DeepSeek has consistently pushed the frontier of what open-weight models can achieve. Their V3 series demonstrated that Chinese AI labs could compete head-to-head with models from OpenAI, Anthropic, and Google on key benchmarks. V4 aims to take this even further.

Why the Hype?

Trillion-parameter scale with extreme efficiency via Mixture of Experts
Engram memory — a novel architecture for long-context retrieval that goes beyond standard attention
1M+ token context — process entire codebases in a single prompt
Multimodal from day one — text, code, and vision in one unified model
Open-weight commitment — DeepSeek has signaled V4 will follow their open-weight tradition
Leaked benchmarks suggest performance rivaling or exceeding GPT-5 and Claude 4 on coding tasks

Key Highlights at a Glance

Feature	DeepSeek V4 (Expected)
Total Parameters	~1 Trillion
Active Parameters	~37B per token
Architecture	Mixture of Experts (MoE)
Context Window	1M+ tokens
Memory System	Engram Memory Architecture
Modalities	Text + Code + Vision
Code Benchmark (HumanEval)	~90% (leaked)
SWE-bench Verified	80%+ (leaked)
Open Weights	Expected (Yes)

Current Status & Timeline

Last Updated: March 12, 2026

🔴 Status: NOT YET RELEASED

DeepSeek V4 has missed multiple expected release windows. The AI community has been eagerly watching for any announcements, but as of March 2026, no official release date has been confirmed.

What we know:

Internal testing is reportedly ongoing
Multiple release windows have passed without announcement
The DeepSeek team has been unusually quiet on social media
Some researchers with early access have hinted at "impressive" results
Leaked benchmark scores have surfaced on Chinese forums

Don't wait — start building with DeepSeek V3.2 on Atlas Cloud today and upgrade seamlessly when V4 drops.

Expected Features Deep Dive

Trillion-Parameter Scale

DeepSeek V4 is expected to feature approximately 1 trillion total parameters, making it one of the largest language models ever created. However, unlike dense models that activate all parameters for every token, V4 uses a Mixture of Experts architecture that keeps inference costs manageable.

Why it matters:

More parameters = more knowledge capacity
The model can store a vastly larger "knowledge base" of code patterns, algorithms, and programming concepts
Trillion-scale models have shown emergent capabilities not present in smaller models
Despite the massive size, MoE keeps per-token compute comparable to a 37B dense model

Technical details:

Estimated 1T total parameters across all experts
~37B parameters active per token (only relevant experts fire)
This gives V4 the knowledge capacity of a trillion-parameter model with the inference speed of a ~37B model
Training likely utilized thousands of GPUs over several months
Custom training frameworks optimized for MoE scaling

Engram Memory Architecture

Perhaps the most exciting innovation expected in DeepSeek V4 is the Engram Memory Architecture — a novel approach to long-context information retrieval that goes beyond standard transformer attention mechanisms.

What is Engram Memory?

Traditional transformers struggle with very long contexts because attention scales quadratically with sequence length. Engram Memory introduces a secondary memory system that:

Compresses long-range context into dense "engram" representations
Indexes these representations for efficient retrieval
Integrates retrieved information seamlessly with the model's attention mechanism
Persists across the generation process, acting like a form of "working memory"

Why it matters for developers:

Process entire codebases without losing track of distant dependencies
Maintain coherent understanding across 1M+ tokens
Better recall of function signatures, type definitions, and API patterns from early in the context
More accurate cross-file reasoning in large projects

How it differs from RAG:

RAG is an external system bolted onto a model; Engram is built into the architecture
No separate embedding/retrieval pipeline needed
The model learns what to remember and how to retrieve it during training
Lower latency than external retrieval systems
Better integration with the model's reasoning process

1M+ Token Context Window

DeepSeek V4 is expected to support a context window exceeding 1 million tokens — enough to fit:

~750,000 words of text
~25,000 pages of documentation
An entire medium-sized codebase (100+ files)
Multiple full-length books
Thousands of API documentation pages

Context window comparison:

Model	Context Window
GPT-4 (original)	8K / 32K
Claude 3	200K
Gemini 1.5 Pro	1M
DeepSeek V3.2	128K
DeepSeek V4 (expected)	1M+
Claude 4	500K
GPT-5	256K

Practical applications:

Feed an entire repository into a single prompt for comprehensive code review
Analyze complete documentation sets for API integration
Process full conversation histories for context-aware assistants
Multi-document summarization and cross-referencing
Complete codebase migration planning in one shot

Multimodal Capabilities

DeepSeek V4 is expected to be natively multimodal, handling text, code, and vision in a unified architecture.

Vision capabilities (expected):

Screenshot-to-code generation
UI/UX analysis and suggestions
Diagram and architecture chart understanding
Bug identification from screenshots
Design system comprehension

Code + Vision integration:

Understand code alongside its visual output
Debug UI issues by seeing both code and rendered result
Generate code from wireframes and mockups
Analyze data visualizations and suggest improvements

Text + Code integration:

Natural language to code translation at unprecedented quality
Code to documentation generation
Technical writing assistance with code awareness
Automated README and API documentation generation

Architecture

Mixture of Experts (MoE)

DeepSeek V4 continues the MoE tradition established in V3, but at a much larger scale.

How MoE works in V4:

Input Token
    │
    ▼
┌─────────┐
│  Router  │ ── Selects which experts to activate
└─────────┘
    │
    ▼
┌─────────────────────────────┐
│  Expert 1  │  Expert 2  │...│ ── Only ~37B params active
│  (active)  │  (active)  │   │    out of ~1T total
└─────────────────────────────┘
    │
    ▼
┌─────────┐
│  Output  │
└─────────┘

Key architectural decisions:

Fine-grained experts: More experts with smaller sizes for better specialization
Shared experts: Some experts are always active, providing a "common knowledge" backbone
Load balancing: Advanced routing ensures even utilization across experts
Expert parallelism: Experts distributed across multiple GPUs for efficient inference

Efficient Inference

Despite its trillion-parameter scale, V4 is designed for practical deployment:

37B active parameters per token — comparable to running a 37B dense model
Speculative decoding support for faster generation
KV-cache optimization for long-context efficiency
Quantization-friendly architecture (expected FP8/INT4 support)
Multi-head Latent Attention (MLA) — continued from V3 for reduced memory footprint

Expected inference performance:

Comparable latency to V3.2 despite much larger total model
Optimized for both batch and interactive workloads
Designed for deployment on standard cloud GPU infrastructure
Expected support for various precision levels (FP16, FP8, INT4)

Training Infrastructure

Based on what we know about DeepSeek's capabilities:

Training on thousands of NVIDIA H800/H100 GPUs
Custom distributed training framework
Multi-stage training: pretraining → instruction tuning → RLHF → code specialization
Estimated training compute: significantly more than V3
Extensive use of synthetic data for code training
Multi-epoch training on high-quality data mixtures

Coding Capabilities

Repository-Level Comprehension

One of the most anticipated features of DeepSeek V4 is true repository-level code comprehension. While current models can understand individual files or small groups of files, V4 is expected to understand entire codebases as coherent systems.

What this means:

Understand the full dependency graph of a project
Track type definitions across multiple files and packages
Comprehend architectural patterns (MVC, microservices, event-driven, etc.)
Identify code smells and anti-patterns in the context of the full system
Suggest refactoring that considers all downstream effects

Example use cases:

"Refactor this authentication module and update all callers"
"Find all places where this API endpoint is consumed and suggest type-safe wrappers"
"Analyze the full test suite and identify gaps in coverage"
"Migrate this project from Express to Fastify, updating all route handlers"

Multi-File Reasoning

Building on repository-level comprehension, V4 is expected to excel at multi-file reasoning — understanding how changes in one file affect others.

Capabilities:

Cross-file type checking and inference
Import/export chain analysis
Side-effect tracking across modules
Database schema ↔ ORM model ↔ API route consistency checking
Full-stack reasoning (frontend ↔ backend ↔ database)

Practical applications:

Developer: "I changed the User model to add a 'role' field.
            What else needs to change?"

DeepSeek V4: Based on your codebase analysis:
1. Update the database migration (migrations/20260301_add_role.ts)
2. Update the User TypeScript interface (types/user.ts)
3. Add 'role' to the registration API handler (routes/auth.ts)
4. Update the user serializer (utils/serializers.ts)
5. Add role-based middleware (middleware/rbac.ts)
6. Update 17 test files that create User fixtures
7. Update the GraphQL schema (schema/user.graphql)
8. Add role to the admin dashboard user list (components/UserTable.tsx)

Advanced Code Generation

Leaked benchmarks suggest V4 will achieve:

~90% on HumanEval — near-perfect on standard coding benchmarks
80%+ on SWE-bench Verified — solving real-world GitHub issues from popular repos
Significant improvements in:
- Complex algorithm implementation
- System design and architecture
- Test generation with high coverage
- Bug diagnosis and fixing
- Performance optimization suggestions

Expected Benchmarks

Based on leaked information and community analysis, here's how DeepSeek V4 is expected to compare:

Coding Benchmarks

Benchmark	DeepSeek V4 (expected)	Claude 4	GPT-5	GLM-5	MiniMax M2.5	Qwen 3.5	DeepSeek V3.2
HumanEval	~90%	90.2%	91.0%	—	—	85.4%	82.6%
HumanEval+	~85%	84.5%	86.2%	—	—	80.1%	77.3%
MBPP	~88%	87.8%	89.5%	—	—	83.2%	80.1%
SWE-bench Verified	80%+	72.5%	68.3%	77.8%	80.2%	55.8%	48.2%
SWE-bench Lite	~85%	78.4%	74.1%	—	—	62.3%	55.7%
LiveCodeBench	~75%	70.2%	72.8%	—	—	63.5%	58.4%
CodeContests	~35%	31.5%	33.2%	—	—	25.8%	22.1%

General Benchmarks

Benchmark	DeepSeek V4 (expected)	Claude 4	GPT-5	GLM-5	MiniMax M2.5	Qwen 3.5	DeepSeek V3.2
MMLU	~92%	91.8%	93.2%	—	—	88.5%	85.7%
MMLU-Pro	~78%	76.5%	79.1%	—	—	72.3%	68.9%
GPQA Diamond	~68%	65.2%	67.8%	—	—	58.4%	52.1%
MATH-500	~95%	93.5%	94.8%	—	—	89.2%	85.3%
AIME	—	—	—	92.7%	—	—	—
ARC-Challenge	~98%	97.5%	98.2%	—	—	95.8%	93.4%
HellaSwag	~97%	96.8%	97.5%	—	—	95.2%	93.1%

Long-Context Benchmarks

Benchmark	DeepSeek V4 (expected)	Claude 4	GPT-5	GLM-5	MiniMax M2.5	Qwen 3.5	DeepSeek V3.2
RULER (128K)	~95%	92.3%	88.5%	—	—	85.2%	80.4%
Needle in Haystack (1M)	~98%	95.1%	N/A	—	—	N/A	N/A
LongBench	~60%	55.8%	52.3%	—	—	48.5%	43.2%
InfiniteBench	~75%	68.4%	62.1%	—	—	55.8%	48.7%

Note: All V4 numbers are from leaked/unverified sources. Official benchmarks will be available upon release. Other model scores are approximate and may vary by version.

Competitive Landscape

While DeepSeek V4 remains unreleased, several strong competitors have emerged in the open-weight and commercial model space. Here are the most notable recent entrants:

GLM-5 / GLM-5-Turbo (Zhipu AI)

Released: February–March 2026 | License: MIT

GLM-5 is the flagship model from Zhipu AI, the Beijing-based lab behind the ChatGLM series. GLM-5 is a 744B parameter MoE model with 40B active parameters, fully open-sourced under the MIT license.

Feature	GLM-5	GLM-5-Turbo
Total Parameters	744B	744B (optimized)
Active Parameters	40B	40B
Architecture	MoE	MoE (agent-optimized)
SWE-bench Verified	77.8%	—
AIME	92.7%	—
License	MIT	MIT
Training Hardware	Huawei Ascend	Huawei Ascend
Pricing (Atlas Cloud)	—	$1.20 / $4.00 per M tokens

Key highlights:

Trained entirely on Huawei Ascend chips — notable as one of the first top-tier models trained without NVIDIA hardware
77.8% SWE-bench Verified — competitive with leaked DeepSeek V4 scores and well ahead of GPT-5
92.7% AIME — exceptional mathematical reasoning performance
GLM-5-Turbo is specifically optimized for agentic workflows, function calling, and multi-step tool use
Fully open-source under MIT, allowing commercial use without restrictions

Try GLM-5-Turbo on Atlas Cloud — available via OpenAI-compatible API at $1.20/$4.00 per M tokens.

MiniMax M2.5 / M2.7

M2.5: Released | M2.7: Coming Soon

MiniMax has been quietly building one of the most cost-effective model families in the industry. Their M2.5 model has achieved remarkable benchmark results at a fraction of the cost of competitors.

Feature	MiniMax M2.5	MiniMax M2.7 (Expected)
SWE-bench Verified	80.2%	Higher (TBD)
Pricing (Input/1M)	$0.27	TBD
Pricing (Output/1M)	$0.95	TBD

Key highlights:

80.2% SWE-bench Verified — currently one of the highest scores reported, matching or exceeding leaked V4 numbers
Extremely competitive pricing at $0.27/$0.95 per M tokens — a fraction of GPT-5 or Claude 4 costs
M2.7 is the next iteration, expected to push performance even further
Strong focus on practical software engineering tasks

Try MiniMax M2.5 on Atlas Cloud — available at just $0.27/$0.95 per M tokens.

Competitors at a Glance

Model	SWE-bench Verified	Input Price (per 1M)	Output Price (per 1M)	Open Source
DeepSeek V4 (expected)	80%+	TBD	TBD	Expected
MiniMax M2.5	80.2%	$0.27	$0.95	No
GLM-5	77.8%	—	—	Yes (MIT)
GLM-5-Turbo	—	$1.20	$4.00	Yes (MIT)
Claude 4	72.5%	$3.00	$15.00	No
GPT-5	68.3%	$2.50	$10.00	No
DeepSeek V3.2	48.2%	$0.26	$0.38	Yes

All models in this table are available or planned on Atlas Cloud.

What to Use While Waiting for V4

DeepSeek V4 keeps missing release windows. Instead of waiting, here are excellent alternatives you can use right now on Atlas Cloud:

Recommended Alternatives

Model	Best For	SWE-bench	Price (Input/Output per 1M)
MiniMax M2.5	Best SWE-bench score at lowest cost	80.2%	$0.27 / $0.95
GLM-5-Turbo	Agentic workflows & tool use	—	$1.20 / $4.00
DeepSeek V3.2	Budget-friendly general coding	48.2%	$0.26 / $0.38

Why these models?

MiniMax M2.5 — If you care about SWE-bench performance (real-world software engineering), M2.5 already matches or exceeds DeepSeek V4's leaked scores at incredibly low pricing. It's the best value for production coding workloads right now.
GLM-5-Turbo — If you're building AI agents that need reliable function calling, multi-step planning, and tool use, GLM-5-Turbo is purpose-built for these workflows. Its MIT open-source license also makes it ideal for on-premise deployment.
DeepSeek V3.2 — If you want the cheapest possible option for general coding tasks, V3.2 remains unbeatable on price at $0.26/$0.38 per M tokens.

All three are available on Atlas Cloud with the same OpenAI-compatible API. When V4 finally drops, just change the model name — no code changes needed.

Timeline of Announcements & Delays

Date	Event	Details
2025-07	DeepSeek V3 Release	The V3 model launches, establishing DeepSeek as a top-tier AI lab
2025-09	V3.1 Update	Incremental improvements, better instruction following
2025-11	V3.2 Release	Major update with improved coding, 128K context
2025-12	V4 Rumors Begin	Chinese tech forums report V4 training has started
2026-01	First Leaked Benchmarks	HumanEval ~90% score surfaces on social media
2026-01	Expected Release Window #1	Community expected January release — missed
2026-02	SWE-bench Leak	80%+ SWE-bench Verified score reported by insiders
2026-02	Expected Release Window #2	February target rumored — missed
2026-02	GLM-5 Released	Zhipu AI releases 744B MoE model, MIT license, 77.8% SWE-bench
2026-03	GLM-5-Turbo Released	Agent-optimized variant at $1.20/$4.00 per M tokens
2026-03	MiniMax M2.5 Released	80.2% SWE-bench Verified at $0.27/$0.95 per M tokens
2026-03	Architecture Details Leak	Engram memory and 1M+ context details surface
2026-03	Expected Release Window #3	March speculation ongoing — not yet released
TBD	Official V4 Release	Awaiting announcement from DeepSeek

Prepare Now with Atlas Cloud

Why wait for V4 when you can start building today?

Atlas Cloud offers DeepSeek V3.2, GLM-5-Turbo, and MiniMax M2.5 via a fully managed, OpenAI-compatible API. Models like MiniMax M2.5 already match V4's leaked SWE-bench scores. When V4 drops, you'll be able to upgrade with a single line change — just update the model name.

Why Atlas Cloud?

🔒 SOC I & II Certified | 🏥 HIPAA Compliant | 🇺🇸 US-based Company

OpenAI-compatible API — drop-in replacement, works with any OpenAI SDK
DeepSeek V3.2 available now at $0.26/$0.38 per million tokens (input/output)
Seamless V4 upgrade — same API, same endpoint, just change the model name
No infrastructure to manage — fully serverless, auto-scaling
Enterprise-grade security — SOC I & II, HIPAA compliant
99.9% uptime SLA — production-ready reliability
Global edge network — low latency from anywhere
Generous free tier — start building without a credit card

Start Building Today

💡 Sign up with this referral link and get a 25% bonus on your first deposit (up to $100)!

Sign up at atlascloud.ai
Get your API key from the dashboard
Use DeepSeek V3.2 with any OpenAI-compatible SDK
When V4 releases, just change the model name — done!

Atlas Cloud API Quick Start

The Atlas Cloud API is fully OpenAI-compatible. If you've used the OpenAI SDK before, you already know how to use it.

Python

from openai import OpenAI

client = OpenAI(
    api_key="your-atlas-cloud-api-key",
    base_url="https://api.atlascloud.ai/v1"
)

# Use DeepSeek V3.2 today — switch to V4 when it launches
response = client.chat.completions.create(
    model="deepseek/deepseek-v3.2",  # Change to deepseek/deepseek-v4 when available
    messages=[
        {"role": "system", "content": "You are an expert software engineer."},
        {"role": "user", "content": "Implement a thread-safe LRU cache in Python with O(1) get and put operations."}
    ],
    temperature=0.7,
    max_tokens=4096
)

print(response.choices[0].message.content)

Streaming example:

from openai import OpenAI

client = OpenAI(
    api_key="your-atlas-cloud-api-key",
    base_url="https://api.atlascloud.ai/v1"
)

stream = client.chat.completions.create(
    model="deepseek/deepseek-v3.2",
    messages=[
        {"role": "user", "content": "Write a comprehensive REST API with FastAPI including auth, CRUD, and tests."}
    ],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

cURL

curl https://api.atlascloud.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-atlas-cloud-api-key" \
  -d '{
    "model": "deepseek/deepseek-v3.2",
    "messages": [
      {"role": "system", "content": "You are an expert software engineer."},
      {"role": "user", "content": "Design a microservices architecture for an e-commerce platform."}
    ],
    "temperature": 0.7,
    "max_tokens": 4096
  }'

Streaming with cURL:

curl https://api.atlascloud.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer your-atlas-cloud-api-key" \
  -d '{
    "model": "deepseek/deepseek-v3.2",
    "messages": [
      {"role": "user", "content": "Implement a distributed rate limiter using Redis and Lua scripts."}
    ],
    "stream": true
  }'

Node.js

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: 'your-atlas-cloud-api-key',
  baseURL: 'https://api.atlascloud.ai/v1'
});

// Use DeepSeek V3.2 today — switch to V4 when it launches
const response = await client.chat.completions.create({
  model: 'deepseek/deepseek-v3.2', // Change to deepseek/deepseek-v4 when available
  messages: [
    { role: 'system', content: 'You are an expert software engineer.' },
    { role: 'user', content: 'Create a real-time WebSocket server with authentication and rate limiting in Node.js.' }
  ],
  temperature: 0.7,
  max_tokens: 4096
});

console.log(response.choices[0].message.content);

Streaming example:

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: 'your-atlas-cloud-api-key',
  baseURL: 'https://api.atlascloud.ai/v1'
});

const stream = await client.chat.completions.create({
  model: 'deepseek/deepseek-v3.2',
  messages: [
    { role: 'user', content: 'Build a full-stack todo app with React, Express, and PostgreSQL.' }
  ],
  stream: true
});

for await (const chunk of stream) {
  const content = chunk.choices[0]?.delta?.content || '';
  process.stdout.write(content);
}

Pricing

Current: DeepSeek V3.2 on Atlas Cloud

	Price per Million Tokens
Input	$0.26
Output	$0.38

Expected: DeepSeek V4 on Atlas Cloud

	Estimated Price per Million Tokens
Input	$0.40 - $0.60 (estimated)
Output	$0.60 - $1.00 (estimated)

Note: V4 pricing is speculative. Despite the massive parameter count, MoE architecture keeps inference costs manageable. Actual pricing will be announced upon release.

Cost Comparison

Provider	Model	Input (per 1M)	Output (per 1M)
Atlas Cloud	DeepSeek V3.2	$0.26	$0.38
Atlas Cloud	MiniMax M2.5	$0.27	$0.95
Atlas Cloud	GLM-5-Turbo	$1.20	$4.00
OpenAI	GPT-4o	$2.50	$10.00
Anthropic	Claude 3.5 Sonnet	$3.00	$15.00
Google	Gemini 1.5 Pro	$1.25	$5.00

DeepSeek V3.2 on Atlas Cloud is up to 40x cheaper than comparable models from other providers.

💰 Sign up with our referral link and get 25% bonus credits (up to $100)!

V3.2 vs V4 Feature Comparison

Feature	DeepSeek V3.2 (Available Now)	DeepSeek V4 (Expected)
Total Parameters	~236B	~1T
Active Parameters	~21B	~37B
Architecture	MoE	MoE + Engram Memory
Context Window	128K tokens	1M+ tokens
Modalities	Text + Code	Text + Code + Vision
HumanEval	82.6%	~90%
SWE-bench Verified	48.2%	80%+
Memory System	Standard attention	Engram Memory Architecture
Code Comprehension	File-level	Repository-level
Multi-file Reasoning	Basic	Advanced
Open Weights	Yes	Expected (Yes)
Atlas Cloud	✅ Available	🔜 Day-one support planned
API Compatibility	OpenAI-compatible	OpenAI-compatible
Price (Input/1M)	$0.26	TBD (~$0.40-0.60 est.)
Price (Output/1M)	$0.38	TBD (~$0.60-1.00 est.)

Bottom line: V3.2 is an incredible model available right now. Start building today and get an effortless upgrade path when V4 drops.

Community Resources

Official

Community

r/LocalLLaMA — Active discussions about DeepSeek models
Hugging Face DeepSeek — Model weights and demos
DeepSeek Discord — Community discussions

Tutorials & Guides

FAQ

1. When will DeepSeek V4 be released?

As of March 2026, there is no confirmed release date. DeepSeek V4 has missed multiple expected release windows (January, February, March 2026). The model appears to still be in internal testing. We will update this guide as soon as an official date is announced.

2. Will DeepSeek V4 be free / open source?

DeepSeek has a strong track record of releasing open-weight models. V3 and V3.2 were released with open weights under permissive licenses. It is widely expected that V4 will follow the same approach, though this has not been officially confirmed.

3. How can I try DeepSeek models right now?

You can use DeepSeek V3.2 today via Atlas Cloud. It's available through an OpenAI-compatible API at just $0.26/$0.38 per million tokens (input/output). You can also try GLM-5-Turbo ($1.20/$4.00) and MiniMax M2.5 ($0.27/$0.95), which offer SWE-bench performance close to or matching V4's leaked scores. When V4 launches, you'll be able to upgrade by simply changing the model name.

4. Will DeepSeek V4 be better than GPT-5?

Based on leaked benchmarks, V4 appears competitive with GPT-5 on many tasks and may significantly outperform it on coding benchmarks like SWE-bench. However, official benchmarks are not yet available, and real-world performance may differ from benchmark scores.

5. What is the Engram Memory Architecture?

Engram Memory is a novel architecture that augments standard transformer attention with a secondary memory system. It allows the model to efficiently compress, index, and retrieve information from very long contexts (1M+ tokens), similar to how human memory works with "engrams" (memory traces in the brain).

6. How does MoE make V4 efficient despite 1T parameters?

Mixture of Experts (MoE) only activates a subset of parameters for each token. In V4's case, only ~37B out of ~1T parameters are active per token. This means you get the knowledge capacity of a trillion-parameter model but the inference speed and cost of a ~37B model.

7. Can DeepSeek V4 understand images?

Yes — V4 is expected to be natively multimodal, supporting text, code, and vision inputs. This enables use cases like screenshot-to-code, UI analysis, diagram comprehension, and visual debugging.

8. What context window will V4 support?

V4 is expected to support 1M+ tokens of context, enabled by the Engram Memory Architecture. This is enough to process entire codebases, documentation sets, or book-length documents in a single prompt.

9. Will Atlas Cloud support V4 on launch day?

Atlas Cloud plans to offer DeepSeek V4 as soon as it becomes available. Since the API is OpenAI-compatible, existing integrations will work with V4 by simply changing the model name — no code changes required.

10. How does V4 compare to Claude 4 for coding?

Based on leaked benchmarks, V4 is expected to match or exceed Claude 4 on most coding benchmarks, particularly SWE-bench Verified (80%+ vs 72.5%). However, different models have different strengths, and real-world performance depends heavily on the specific use case.

11. What programming languages does V4 support?

Like V3.2, V4 is expected to support all major programming languages including Python, JavaScript/TypeScript, Java, C/C++, Go, Rust, PHP, Ruby, Swift, Kotlin, and many more. The repository-level comprehension feature is expected to work across all supported languages.

12. Is DeepSeek V4 safe for enterprise use?

When accessed through Atlas Cloud, enterprise-grade security is guaranteed:

🔒 SOC I & II Certified
🏥 HIPAA Compliant
🇺🇸 US-based Company
Data encryption at rest and in transit
No training on customer data
Enterprise SSO and access controls available

13. What's the best way to prepare for V4?

Start building with V3.2, GLM-5-Turbo, or MiniMax M2.5 now on Atlas Cloud
Design your application with model-agnostic abstractions
Use the OpenAI-compatible API format
When V4 drops, change one line of code (the model name) and you're done
Star this repo to get notified of V4 updates!

14. Will V4 support function calling / tool use?

Based on the capabilities of V3.2 and the industry trend, V4 is very likely to support function calling, tool use, and structured output (JSON mode). These features are essential for building AI agents and autonomous coding assistants.

Contributing

Contributions are welcome! If you have:

New information about DeepSeek V4
Benchmark results or comparisons
Tutorials or guides
Tool integrations
Bug reports or corrections

Please open an issue or submit a pull request. See CONTRIBUTING.md for details.

How to contribute:

Fork this repository
Create your feature branch (git checkout -b feature/amazing-addition)
Commit your changes (git commit -m 'Add some amazing addition')
Push to the branch (git push origin feature/amazing-addition)
Open a Pull Request

Star History

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Get Started Today

Don't wait for V4 — start building with DeepSeek V3.2 on Atlas Cloud right now.

🔒 SOC I & II Certified | 🏥 HIPAA Compliant | 🇺🇸 US-based Company

Feature	Details
Model	DeepSeek V3.2 (V4 coming soon)
Pricing	$0.26 / $0.38 per million tokens
API	OpenAI-compatible
Upgrade	Seamless when V4 launches
Bonus	25% on first deposit (up to $100)

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