DeepSeek: Most Powerful Open-Source AI

💰 Extremely Low Cost

V4-Pro API is just $0.435/$0.87 per M tokens (Flash $0.14/$0.28) — roughly 5-30x cheaper than GPT-5.4, Claude 4.6 and Gemini 3.1. Even enterprise apps can afford it easily.

🎯 Exceptional Performance

Excels at agentic coding, math reasoning and long-document understanding. V4 scores SWE-bench 80.6%, LiveCodeBench 93.5, GPQA 90.1% — matching frontier models like GPT-5.4, Claude 4.6 and Gemini 3.1 Pro.

🔓 Fully Open Source

Model weights and technical reports fully public. Can be deployed locally for data security.

🚀 Continuous Evolution

V1 to V4 continuous iteration. Each update brings performance leap.

DeepSeek-V4 (April 2026)

Latest flagship, open-sourced under MIT (weights on Hugging Face). V4-Pro 1.6T total / 49B active, V4-Flash 284B / 13B. MoE + CSA/HCA hybrid attention with a 1M-token context window. SWE-bench Verified 80.6% — highest among open models.

DeepSeek-V3 (Dec 2024)

671B total params, 37B active. MoE architecture achieves low-cost high-performance. Trained on 14.8T tokens, cost only 2.788M H800 GPU hours, stable training with no rollbacks.

DeepSeek-V2 (May 2024)

236B total params, 21B active, supports 128K context. Training cost reduced 42.5%, KV cache reduced 93.3%, throughput improved 5.76x.

DeepSeek-Coder-V2 (Jun 2024)

Code specialist, supports 338 programming languages, 128K ultra-long context, industry-leading code completion and generation.

DeepSeek-VL (Mar 2024)

Open-source vision-language model, supports 1024×1024 high-res image understanding, excellent multimodal performance.

💻 Code Development

Code generation, bug fixes, code explanation, unit test writing. 10x productivity boost.

📚 Document Understanding

Long document summarization, contract review, paper analysis. 128K context handles easily.

🎓 Education Tutoring

Math problem solving, Q&A, concept explanation. AI tutor assistant.

✍️ Content Creation

Article writing, marketing copy, multilingual translation. Boost content output.

Register Account

Sign up on Atlas Cloud, no credit card required. Complete registration in 1 minute and get free credits.

Get API Key

Log into console, create API key with one click. Supports multiple key management for different projects.

Start Calling

Copy sample code, replace API key and start using. Compatible with OpenAI format, zero migration cost.

What is DeepSeek?

DeepSeek is an open-source large language model developed by Chinese company DeepSeek AI. Its latest flagship, DeepSeek V4 (released and open-sourced under MIT on April 24, 2026), is a frontier model that matches GPT-5.4, Claude 4.6 and Gemini 3.1 Pro across many benchmarks — at roughly 5-30x lower cost. Supports code generation, document understanding and math reasoning, and can be deployed locally.

Is DeepSeek free?

Yes! DeepSeek provides free API quota. Individual developers can use directly. Enterprise users can choose paid version for higher quota. Register on Atlas Cloud to get free trial credits.

Is DeepSeek better than ChatGPT?

DeepSeek V4 matches GPT-5.4, Claude 4.6 and Gemini 3.1 Pro across many benchmarks — SWE-bench Verified 80.6% (vs GPT-5.4's 77.2%), LiveCodeBench 93.5, GPQA 90.1%. Main advantages: roughly 5-30x lower cost and fully open-source (MIT). Enterprises can deploy locally to protect data security.

Is DeepSeek safe?

DeepSeek is developed by a legitimate company with fully public code. Enterprise users can choose local deployment, data stays on-premise. However, any AI has potential risks. Recommended to use enterprise version on Atlas Cloud with professional security guarantees.

How to use DeepSeek?

Three ways: 1) Online trial - Atlas Cloud provides free trial; 2) API calls - Integrate into your apps; 3) Local deployment - Download model weights to your server. Beginners recommended to try on Atlas Cloud first.

When was DeepSeek V4 released?

DeepSeek V4 was officially released and open-sourced under the MIT license on April 24, 2026, with weights published on Hugging Face. It comes in two versions: V4-Pro (1.6T total / 49B active) and V4-Flash (284B / 13B), both with a 1M-token context window. You can use it now on chat.deepseek.com, the official API, or Atlas Cloud.

What are DeepSeek V4's key features?

DeepSeek V4 key features: 1) Agentic coding — 80.6% on SWE-bench Verified, the highest among open models; 2) 1.6 trillion parameters total (49B active per token) for V4-Pro, 284B/13B for V4-Flash; 3) 1 million-token context window; 4) CSA+HCA hybrid attention for ultra-low-cost long context (~27% compute, ~10% KV-cache memory vs V3.2); 5) Fully open-source (MIT) and free to self-host. Pricing starts at $0.435/$0.87 per million tokens (Pro).

What languages does DeepSeek support?

DeepSeek supports Chinese, English and multiple languages. DeepSeek-Coder-V2 specifically for code tasks, supports 338 programming languages, a powerful assistant for developers.

Can DeepSeek generate images?

DeepSeek V4 focuses on text, code and reasoning rather than image generation. DeepSeek-VL can understand image content, but for generating images use a dedicated tool like Stable Diffusion or DALL-E.

Is DeepSeek open source?

Yes, DeepSeek is fully open source! Model weights, training code, technical reports are all public on GitHub. Enterprises can freely download and deploy without vendor lock-in concerns.

How to download DeepSeek?

Visit DeepSeek GitHub repo or HuggingFace model library to download. Note: model is large (tens of GB), requires high-end GPU to run. Recommend trying on Atlas Cloud first, confirm needs before local deployment.

How long context does DeepSeek support?

DeepSeek-V2/V3 supports 128K token context, about 100K characters. DeepSeek V4 supports a 1 million-token context window (both Pro and Flash) — enough to process entire books, full codebases, or thousands of pages of documents in a single query, made affordable by its CSA+HCA hybrid attention.

What is Atlas Cloud?

Atlas Cloud is a leading AI model service platform and OpenRouter ecosystem partner. Provides enterprise-grade access to DeepSeek and open-source models, with new models available on release day. Passed SOC I & II, HIPAA international certifications, meets enterprise security compliance requirements. Provides: 1) Ready-to-use API; 2) 99.9% SLA guarantee; 3) Multi-region deployment; 4) Technical support. New users get free credits upon registration.

Who is DeepSeek suitable for?

Individual developers: low-cost AI assistant; Enterprises: local deployment protects data; Students: free AI learning tool; Researchers: open-source customizable. Basically suitable for all scenarios needing AI!

What are DeepSeek limitations?

Free version has request rate limits. Local deployment needs high-end GPU (at least 24GB VRAM). Some sensitive topics may be refused. Using on Atlas Cloud solves most limitation issues.

How does DeepSeek V4 deliver low-cost ultra-long context?

V4 uses a hybrid attention architecture combining Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA) on top of its MoE design. At a 1M-token context, this cuts per-token compute to roughly 27% of V3.2 and KV-cache memory to about 10%, making million-token context practical and cheap.

Can I run DeepSeek V4 locally?

Yes — the weights are open-source under MIT on Hugging Face. The smaller V4-Flash (284B total / 13B active) is far more practical for local setups, while the full V4-Pro (1.6T MoE) requires enterprise clusters. Quantized and GGUF builds make consumer and Apple Silicon deployment feasible for Flash.

Does DeepSeek V4 run on Apple Silicon (Mac M3/M4)?

Yes. Optimized GGUF builds of the smaller V4-Flash run on Mac Studio/Pro with 64GB+ unified memory. Apple's Metal Performance Shaders provide GPU acceleration for efficient local inference on M3/M4 chips.

Can I use DeepSeek with VS Code or Cursor?

Yes. DeepSeek is fully compatible with Cursor, Continue.dev, Cline, and other AI coding assistants via API key. Simply set the DeepSeek API endpoint and key in your IDE settings. Compatible with OpenAI API format.

DeepSeek V4 vs GPT-5.4: Which is better?

DeepSeek V4 scores 80.6% on SWE-bench Verified (vs GPT-5.4's 77.2%) at $0.435/$0.87 per M tokens for V4-Pro (vs GPT-5.4's $2.50/$15). That's roughly 5-15x cheaper with stronger coding performance. Plus V4 is open-source (MIT) with free self-hosting — GPT-5.4 is closed-source API-only.

DeepSeek V4 vs Claude 4.6: Which is better for coding?

Claude Opus 4.6 scores 80.8% on SWE-bench at $5/$25 per M tokens. DeepSeek V4-Pro scores 80.6% at $0.435/$0.87/M — roughly 10-30x cheaper. Claude excels in long-context reliability, but V4 offers a 1M-token window cheaply via CSA+HCA hybrid attention. Key advantage: V4 is open-source (MIT).

DeepSeek V4 vs Gemini 3.1 Pro: How do they compare?

Gemini 3.1 Pro scores 80.6% on SWE-bench at $2/$12 per M tokens. DeepSeek V4-Pro ties it at 80.6% with a 1M-token context window, at $0.435/$0.87/M — about 5-14x cheaper, plus open-source (MIT) weights for self-hosting. V4 focuses on text, code and reasoning.

Can DeepSeek V4 fix bugs across an entire repository?

Yes. V4's 1M-token context window lets it analyze full codebases for repo-level bug fixing, kept affordable by CSA+HCA hybrid attention. It can understand cross-file dependencies, trace bug origins, and generate fixes that consider the entire project structure.

Does DeepSeek V4 support Python, Rust, and other languages?

V4 is SOTA for Python, Rust, C++, JavaScript, TypeScript, Go, and 50+ other languages. Trained on a massive specialized code corpus, it excels at code generation, debugging, refactoring, and test writing across all major programming languages.

Is user data used for training by DeepSeek?

API data is NOT used for training by default. Web chat data may be used unless you opt out in settings. For maximum privacy, self-host V4 locally using the open-source weights — your data never leaves your servers.

What is DeepSeek V4's hybrid attention (CSA + HCA)?

V4 uses a hybrid attention architecture combining Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA). At 1M context it brings per-token compute down to ~27% of V3.2 and KV-cache memory to ~10%, delivering frontier performance with a 1M-token window at a fraction of the cost.