DeepSeek V4 Flash

DeepSeek V4 Flash is a Mixture-of-Experts (MoE) language model with 284B total parameters and 13B activated per token. This is the official release, which supersedes the earlier preview and substantially enhances agentic capability while keeping the same model structure and size. It targets highly efficient long-context intelligence, supporting a context window of up to one million tokens, and is a strong general-purpose model for reasoning, coding, and agentic workflows.

Architecture

DeepSeek V4 Flash pairs a sparsely activated MoE design with a hybrid attention mechanism that combines Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA) to improve long-context efficiency. Manifold-Constrained Hyper-Connections (mHC) strengthen residual connections and stabilize signal propagation, and the model was trained on more than 32T tokens using the Muon optimizer for faster convergence. Weights use an FP4 + FP8 mixed format, with MoE expert parameters stored in FP4 and most remaining parameters in FP8.

The model exposes a reasoning effort control with three levels, low, high, and max, which set how much deliberation it spends before answering. A speculative decoding module is attached to the checkpoint for faster generation.

Key Features

• Mixture-of-Experts model with 284B total parameters and 13B activated per token
• Context window of up to one million tokens for long-document analysis and retrieval
• Hybrid CSA and HCA attention for efficient long-context serving
• Three reasoning effort levels spanning fast responses to deep deliberation
• Native tool-calling and reasoning parsing for agentic applications
• Open-source under the MIT License

Use Cases

• Conversational assistants and chat applications
• Coding assistance, code generation, and software engineering agents
• Mathematical problem-solving and step-by-step reasoning
• Long-context document analysis, summarization, and retrieval over very large inputs
• Agentic workflows involving tool use, browsing, and multi-step planning

Benchmarks

The official release reports large gains on agentic and coding-agent work over the preview it replaces. It scores 82.7 on Terminal Bench 2.1, up from 61.8, and 54.4 on DeepSWE, up from 7.3. Other results include 54.2 on NL2Repo, 76.7 on Cybergym, 70.3 on Toolathlon-Verified, 25.2 on Agents' Last Exam, and 25.1 on AutomationBench Public.

DeepSeek reports that these figures exceed those of the larger DeepSeek V4 Pro preview across the published agentic benchmarks, despite Flash activating far fewer parameters per token. Code-agent evaluations were run with the max reasoning effort level.

For full model details and the complete evaluation tables, see the model card on Hugging Face.