DeepSeek V4 AI Model Details: Complete Specifications, Features, and Capabilities

DeepSeek V4 AI Model Details: Complete Specifications, Features, and Capabilities

This article consolidates every key detail about DeepSeek V4's specifications, features, and capabilities into one comprehensive reference — the definitive guide for anyone evaluating, integrating, or studying the V4 series.

Core Specifications

DeepSeek-V4-Pro

DeepSeek-V4-Flash

Architecture Deep Dive

Hybrid Attention: CSA + HCA

The foundational innovation in V4's architecture combines two complementary attention mechanisms:

Compressed Sparse Attention (CSA): Applies token-wise compression to key-value pairs for moderately distant context, maintaining fidelity while reducing memory and compute requirements.

Heavily Compressed Attention (HCA): Applies aggressive compression to very distant tokens, storing compact summary representations that enable the model to "remember" information across the full million-token context without full attention overhead.

Combined effect at 1M-token context vs V3.2:

• Inference FLOPs: reduced to 27% of V3.2
• KV Cache memory: reduced to 10% of V3.2

Manifold-Constrained Hyper-Connections (mHC)

Replaces standard residual connections throughout the network. By constraining weight updates to lie on a Riemannian manifold, mHC strengthens signal propagation across V4-Pro's hundreds of transformer layers — enabling stable training at 1.6T parameters.

Muon Optimizer

The Muon (Momentum + Orthogonalization) optimizer replaces AdamW. By orthogonalizing gradient updates, it:

• Removes redundancy between successive update steps
• Achieves faster convergence (more learning per training step)
• Provides greater stability at 32T+ token pre-training scale

Three Reasoning Modes

Performance impact (V4-Pro):

Key Capabilities

Coding

• Best open-model Codeforces rating: 3206
• LiveCodeBench: 93.5% (Pass@1)
• SWE-bench Verified: 80.6% (Resolved)
• SWE-bench Pro: 55.4% (Resolved)
• SWE-bench Multilingual: 76.2% (Resolved)
• Native integration with Claude Code, OpenClaw, OpenCode

Reasoning and Knowledge

• MMLU-Pro: 87.5% (Think Max)
• GPQA Diamond: 90.1% (Think Max)
• HLE: 37.7% (Think Max)
• SimpleQA-Verified: 57.9% (Think Max)
• MMMLU (multilingual): 90.3% (base)

Long-Context

• MRCR 1M (needle-in-haystack): 83.5% (Think Max) — beats Gemini-3.1-Pro
• CorpusQA 1M: 62.0% (Think Max) — best non-Claude score
• LongBench-V2 (base): 51.5%

Agentic Tasks

• Terminal Bench 2.0: 67.9% (Think Max)
• SWE-bench Verified: 80.6%
• MCPAtlas Public: 73.6% (Think Max) — best open score
• BrowseComp: 83.4% (Think Max)
• Toolathlon: 51.8% (Think Max)

API Compatibility

Model Variants Available

Agentic Integration

DeepSeek V4 integrates natively with:

• Claude Code — leading AI coding assistant
• OpenClaw — open-source multi-agent framework
• OpenCode — open-source autonomous coding system

It is already powering DeepSeek's own internal agentic coding infrastructure.

Access Methods

1. Web: chat.deepseek.com (Instant Mode = Flash; Expert Mode = Pro)
2. API: api.deepseek.com/v1 — update model to deepseek-v4-pro or deepseek-v4-flash
3. HuggingFace: Download weights for local deployment
4. ModelScope: Alternative download for faster access in China
5. Third-party inference providers: Multiple providers including Novita offer V4 API access

Legacy Model Migration

Recommended Use by Task Type

AI-native platforms like Framia.pro implement intelligent routing across these configurations — matching task complexity to the right V4 variant and mode to optimize both quality and cost for creative workflows.

Conclusion

DeepSeek V4 is the most capable open-weight model series available as of April 2026. With 1.6 trillion parameters (V4-Pro), MIT licensing, a 1M-token standard context window, three reasoning modes, frontier-class coding capability, and pricing 10–35× below closed-source alternatives, it represents a genuine step change in accessible AI capability.