State-of-the-Art Mixture-of-Experts Coding Intelligence at 1/10th the Cost of GPT-4.
DeepSeek Coder V2 is a leading-edge coding assistant built on a Mixture-of-Experts (MoE) architecture, specifically optimized for logic, mathematics, and 338+ programming languages. By leveraging a 236B total parameter model where only 21B are active per token, it achieves state-of-the-art performance on benchmarks like HumanEval and MBPP while maintaining industry-low latency and operational costs. Positioned for the 2026 market as the primary open-weights alternative to closed-source giants like GitHub Copilot and Claude 3.5 Sonnet, DeepSeek Coder offers a 128K context window, enabling it to process entire codebases for complex refactoring and architecture-aware suggestions. Its pricing model has disrupted the industry, offering tokens at a fraction of the cost of Western competitors, making it the preferred choice for high-volume automated engineering agents and enterprise-scale CI/CD integrations. Whether deployed via its free web interface or integrated into IDEs like VS Code and Cursor via its OpenAI-compatible API, it provides professional-grade bug localization, unit test generation, and multi-file code completion.
Uses Mixture-of-Experts with 236B parameters to optimize inference speed and accuracy.
Accelerate development with AI-powered code suggestions and integrated security scanning across the SDLC.
The leading terminal-based AI pair programmer for high-velocity software engineering.
Accelerate development cycles with context-aware AI code generation and deep refactoring logic.
The first AI code assistant for enterprise-grade development with total privacy control.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Supports prefix and suffix context awareness for mid-line code completion.
Large context window allowing the model to 'read' hundreds of files simultaneously.
Pre-trained on 2 trillion tokens across 338 programming languages.
API-level caching of frequently used system prompts and documentation.
Model weights are available for local deployment via vLLM or Ollama.
Fine-tuned specifically for chat-based debugging and complex instruction execution.
Developers spend up to 20% of their time writing boilerplate test code.
Registry Updated:2/7/2026
Translating outdated Java 8 or COBOL logic to modern Python/Go microservices.
Changing a shared API signature across 50 different files.