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模型列表 - Kimi API 开放平台

Navigation 开始使用 模型列表 开始使用 快速开始 模型列表 Kimi K3 模型 Kimi K2.7 Code 模型 Kimi K2.6 模型 模型能力 思考模型 推理强度 多轮对话 流式输出 JSON Mode Partial Mode 视觉输入 上下文缓存 动态加载工具 工具调用 基础介绍 联网搜索 New 内置联网搜索 官方工具列表 工具调用约束 工具调用最佳实践 核心工作流 response_format 自动断线重连 文件问答指南 Batch API 指南 生态集成 Kimi Code CLI Codex OpenClaw Claude Code OpenCode Hermes Agent ModelScope MCP 调试与运维 调试工具 开发工作台调试 组织管理最佳实践 开始使用 模型列表 复制页面 复制页面 查看 Kimi 开放平台当前可用的多模态与编程模型，以及已下线模型的迁移提示。 复制页面 复制页面 模型定价信息参见 产品定价 页面。 ​ 多模态模型 模型名称 描述 kimi-k3 Kimi 迄今能力最强的模型，

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🔥DeepSeek-V4-Pro API is 75% OFF until May 5th, 2026, 15:59 (UTC Time)! Don't miss out on this massive discount. 🛠️Integration Updates: 🔹Claude Code: Set model to deepseek-v4-pro[1m] to unlock 1M context! 🔹OpenCode: Update to v1.14.24+ 🔹OpenClaw: Update to v2026.4.24+ Check https://t.co/rvf11eSsoJ

HazardAuditor: From Executable Threats to Safer Computer-Use Agents

Computer-use agents increasingly interact with browsers, terminals, file systems, and external services, introducing safety risks that emerge through runtime behavior rather than generated content alone. Existing guard models target static prompts and responses and are poorly suited to agent execution; existing executable safety platforms produce evaluation verdicts rather than the normalized supervision a guard model needs to learn across heterogeneous agent frameworks. We introduce HazardAuditor, an execution-grounded framework that closes both gaps. Its infrastructure runs heterogeneous agents (Claude Code, Codex, Hermes, and OpenClaw) in controlled environments and normalizes their interactions into a canonical event representation for cross-framework supervision. We further observe that token-level post-training objectives create a structural mismatch for generative guards, causing longer rationales to dominate gradient updates. Guard Policy Optimization (GuardPO) addresses this by converting deterministic safety outcomes into sequence-level advantages and normalizing rationale and verdict regions, making the safety decision the effective unit of optimization. Across multiple benchmarks and heterogeneous computer-use systems, HazardAuditor improves accuracy by up to 16.5 percentage points over the strongest prior guard. Code, models, and evaluation artifacts will be available at https://yunhao-feng.github.io/HazardAuditor/.