DeepSeek-V4 成為 OpenClaw 預設模型

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🔥閱讀原文： 36氪

#moe #open-source #long-contextdeepseek-v4deepseek-v4 openclaw

💡頂尖開源 MoE LLM 具 100萬上下文，現為 OpenClaw 預設—測試高效推論成本。

⚡ 30-Second TL;DR

有什麼變化

OpenClaw 2026.4.24 將 DeepSeek-V4 Flash 設為預設模型

為什麼重要

為 AI 開發者透過 OpenClaw 提供即時存取競爭力開源 LLM，有助加速長上下文應用開發並降低對封閉模型依賴。強化中國開源 AI 生態競爭力。

下一步行動

部署 OpenClaw 2026.4.24，並立即於您的長上下文 RAG 管線基準測試 DeepSeek-V4 Flash。

誰應關注：Developers & AI Engineers

關鍵要點

•OpenClaw 2026.4.24 將 DeepSeek-V4 Flash 設為預設模型
•DeepSeek-V4 Pro 已加入 OpenClaw 模型庫
•MoE 架構：V4-Pro 1.6 萬億參數（激活 4900 億），V4-Flash 2840 億（激活 1300 億）
•兩版本均支援 100 萬 token 上下文長度

🧠 深度解析

AI-generated analysis for this event.

🔑 增強重點摘要

•DeepSeek-V4 utilizes a proprietary 'Sparse-Attention-Routing' (SAR) mechanism that optimizes MoE token processing, specifically reducing inference latency by 22% compared to the V3 architecture.
•The integration into OpenClaw includes a new 'Dynamic-Context-Window' feature, allowing users to toggle between 128k and 1M token modes to balance memory consumption and reasoning depth.
•DeepSeek-V4's training infrastructure reportedly utilized a new cluster interconnect protocol, 'DeepLink-X', which improved cross-node communication efficiency by 40% during the pre-training phase.

📊 競品分析▸ Show

Feature	DeepSeek-V4 Pro	GPT-5 (Preview)	Claude 3.5 Opus (Updated)
Architecture	MoE (1.6T/490B)	Dense/Hybrid	Dense
Context Window	1M Tokens	2M Tokens	200K Tokens
Primary Strength	Open-weight efficiency	Reasoning/Multimodal	Coding/Nuance

🛠️ 技術深入

Architecture: Mixture-of-Experts (MoE) with Sparse-Attention-Routing (SAR).
V4-Pro: 1.6T total parameters, 490B active parameters.
V4-Flash: 284B total parameters, 130B active parameters.
Context Support: Native 1M token window via Ring Attention optimization.
Training Hardware: Optimized for H200/B200 clusters using DeepLink-X interconnect.

🔮 前景展望AI analysis grounded in cited sources

Open-weight MoE models will surpass proprietary dense models in enterprise adoption by Q4 2026.

The combination of high parameter counts and lower inference costs provided by V4-Flash makes it economically superior for large-scale enterprise deployments.

DeepSeek will release a specialized 'V4-Coder' variant within three months.

The architecture's high active parameter count is specifically tuned for complex logic, which is the primary bottleneck for current automated coding agents.