{"id":135519,"date":"2026-02-02T13:23:35","date_gmt":"2026-02-02T05:23:35","guid":{"rendered":"https:\/\/vertu.com\/?post_type=aitools&p=135519"},"modified":"2026-02-02T13:23:35","modified_gmt":"2026-02-02T05:23:35","slug":"kimi-k2-5-powerful-open-source-model-unleashes-agent-swarm-and-visual-coding-revolution","status":"publish","type":"aitools","link":"https:\/\/legacy.vertu.com\/ar\/ai-tools\/kimi-k2-5-powerful-open-source-model-unleashes-agent-swarm-and-visual-coding-revolution\/","title":{"rendered":"Kimi K2.5: Powerful Open-Source Model Unleashes Agent Swarm and Visual Coding Revolution"},"content":{"rendered":"

\"\"<\/h1>\n

The Breakthrough: 100 Sub-Agents, 1,500 Tool Calls, 4.5x Speed Increase\u2014Native Multimodal AI Redefining Agentic Intelligence<\/h2>\n

Kimi K2.5 represents the most powerful open-source model to date, achieving state-of-the-art coding and vision capabilities through native multimodal architecture trained on approximately 15 trillion mixed visual and text tokens<\/strong>. The Agent Swarm Innovation<\/strong>: Self-directed orchestration of up to 100 sub-agents<\/strong> executing parallel workflows across up to 1,500 tool calls<\/strong>, reducing execution time by 4.5x<\/strong> compared to single-agent setups\u2014automatically created and coordinated without predefined workflows. The Coding Revolution<\/strong>: Strongest open-source coding model with exceptional front-end capabilities, turning simple conversations into complete interactive layouts with rich scroll-triggered animations, excelling at visual debugging<\/strong> by reasoning over images\/video to improve image-to-code generation and lower barriers for visual intent expression. The Cost Advantage<\/strong>: Delivering strong performance on agentic benchmarks (HLE, BrowseComp, SWE-Verified) at fraction of competitor costs<\/strong>. The Office Productivity<\/strong>: Handling high-density large-scale work end-to-end, reasoning over massive inputs, coordinating multi-step tool use, delivering expert-level documents\/spreadsheets\/PDFs\/slides through conversation\u201459.3% improvement<\/strong> on AI Office Benchmark and 24.3% improvement<\/strong> on General Agent Benchmark over K2 Thinking. The PARL Training<\/strong>: Parallel-Agent Reinforcement Learning with trainable orchestrator decomposing tasks into parallelizable subtasks, frozen subagents executing concurrently, staged reward shaping preventing serial collapse, critical-steps metric forcing parallel strategies. Availability<\/strong>: Via Kimi.com, Kimi App, API (platform.moonshot.ai), and Kimi Code\u2014four modes (K2.5 Instant, Thinking, Agent, Agent Swarm Beta).<\/p>\n

Part I: The Multimodal Foundation<\/h2>\n

Massive-Scale Vision-Text Joint Pretraining<\/h3>\n

Training Corpus<\/strong>: Approximately 15 trillion mixed visual and text tokens<\/p>\n

Architecture<\/strong>: Native multimodal model from ground up<\/p>\n

Key Insight<\/strong>: “At scale, the trade-off between vision and text capabilities disappears\u2014they improve in unison”<\/p>\n

Result<\/strong>: State-of-the-art performance in both coding and vision tasks<\/p>\n

Paradigm Shift<\/strong>: Vision and text not competing but complementary<\/p>\n

The Unified Capability Emergence<\/h3>\n

Traditional Approach<\/strong>: Separate text and vision models<\/p>\n

Kimi K2.5 Innovation<\/strong>: Single model excelling at both<\/p>\n

Synergistic Learning<\/strong>: Visual reasoning enhancing code understanding<\/p>\n

Practical Impact<\/strong>: Seamless multimodal workflows<\/p>\n

Part II: Coding with Vision\u2014The Front-End Revolution<\/h2>\n

Conversational Interface Generation<\/h3>\n

Capability<\/strong>: Turning simple conversations into complete front-end interfaces<\/p>\n

Features Implemented<\/strong>:<\/p>\n

    \n
  • Interactive layouts<\/li>\n
  • Rich animations<\/li>\n
  • Scroll-triggered effects<\/li>\n
  • Complex UI components<\/li>\n<\/ul>\n

    Single Prompt Power<\/strong>: Complete implementations from minimal descriptions<\/p>\n

    Example<\/strong>: Image-gen tool integration producing fully functional interfaces<\/p>\n

    Developer Impact<\/strong>: Dramatically reduced front-end development time<\/p>\n

    Visual Debugging Breakthrough<\/h3>\n

    The Innovation<\/strong>: Reasoning over images and video for code generation<\/p>\n

    Image-to-Code<\/strong>: Converting visual designs directly to implementation<\/p>\n

    Video-to-Code<\/strong>: Reconstructing websites from video demonstrations<\/p>\n

    Example Workflow<\/strong>:<\/p>\n

      \n
    1. Record video of desired website behavior<\/li>\n
    2. Feed to K2.5<\/li>\n
    3. Receive complete code reconstruction<\/li>\n
    4. Iterate based on visual feedback<\/li>\n<\/ol>\n

      Barrier Reduction<\/strong>: Users expressing intent visually instead of technical specifications<\/p>\n

      Autonomous Visual Iteration<\/h3>\n

      Kimi Code Integration<\/strong>: Terminal-based tool integrating with VSCode, Cursor, Zed<\/p>\n

      Open Source<\/strong>: Freely available codebase<\/p>\n

      Multimodal Input<\/strong>: Supports images and videos<\/p>\n

      Auto-Discovery<\/strong>: Automatically finds and migrates existing skills and MCPs<\/p>\n

      Example – Matisse's La Danse Translation<\/strong>:<\/p>\n

        \n
      1. Visual input: Famous painting aesthetic<\/li>\n
      2. Documentation lookup: Kimi App design guidelines<\/li>\n
      3. Visual inspection: K2.5 checking own output<\/li>\n
      4. Autonomous iteration: Refining until aesthetically matching<\/li>\n
      5. End-to-end result: Art-inspired webpage created autonomously<\/li>\n<\/ol>\n

        The Breakthrough<\/strong>: AI visually debugging its own work without human intervention<\/p>\n

        Real-World Software Engineering<\/h3>\n

        Kimi Code Bench<\/strong>: Internal benchmark covering diverse end-to-end tasks<\/p>\n

        Task Categories<\/strong>:<\/p>\n

          \n
        • Building from scratch<\/li>\n
        • Debugging existing code<\/li>\n
        • Refactoring for improvements<\/li>\n
        • Testing implementation<\/li>\n
        • Scripting automation<\/li>\n<\/ul>\n

          Language Coverage<\/strong>: Multiple programming languages<\/p>\n

          K2.5 vs K2 Improvement<\/strong>: Consistent and meaningful gains across all task types<\/p>\n

          Production Readiness<\/strong>: Strong performance on real-world engineering workflows<\/p>\n

          Visual Reasoning Example<\/h3>\n

          Puzzle Solving<\/strong>: K2.5 analyzing visual puzzle<\/p>\n

          Code-Based Marking<\/strong>: Using code to mark shortest path solution<\/p>\n

          Integration<\/strong>: Vision understanding + code generation + logical reasoning<\/p>\n

          Practical Applications<\/strong>:<\/p>\n

            \n
          • Algorithm visualization<\/li>\n
          • Game development<\/li>\n
          • Educational tools<\/li>\n
          • Interactive problem solving<\/li>\n<\/ul>\n

            Part III: Agent Swarm\u2014Scaling Out, Not Just Up<\/h2>\n

            The Paradigm Shift<\/h3>\n

            Traditional Scaling<\/strong>: Single agent with more compute (scaling up)<\/p>\n

            K2.5 Innovation<\/strong>: Multiple coordinated agents (scaling out)<\/p>\n

            Research Preview<\/strong>: Agent Swarm currently in beta<\/p>\n

            Shift Significance<\/strong>: From sequential to parallel agentic execution<\/p>\n

            The Technical Architecture<\/h3>\n

            Orchestrator Agent<\/strong>: Trainable coordinator (not frozen)<\/p>\n

            Sub-Agents<\/strong>: Up to 100 dynamically instantiated (frozen during execution)<\/p>\n

            Task Decomposition<\/strong>: Breaking complex tasks into parallelizable subtasks<\/p>\n

            Dynamic Instantiation<\/strong>: Sub-agents created on-demand for specific roles<\/p>\n

            Example Roles<\/strong>:<\/p>\n

              \n
            • AI Researcher<\/li>\n
            • Physics Researcher<\/li>\n
            • Fact Checker<\/li>\n
            • Data Analyst<\/li>\n
            • Code Reviewer<\/li>\n<\/ul>\n

              No Predefined Workflows<\/strong>: Entirely self-directed coordination<\/p>\n

              Parallel-Agent Reinforcement Learning (PARL)<\/h3>\n

              The Challenge<\/strong>: Training reliable parallel orchestrator<\/p>\n

              Problem 1 – Delayed Feedback<\/strong>: Sparse rewards from independently running sub-agents<\/p>\n

              Problem 2 – Non-Stationary<\/strong>: Sub-agent behaviors changing during training<\/p>\n

              Problem 3 – Serial Collapse<\/strong>: Orchestrator defaulting to single-agent despite parallel capacity<\/p>\n

              The Solution – Staged Reward Shaping<\/strong>:<\/p>\n

              Reward Function<\/strong>:<\/p>\n

              R_t = \u03bb_aux(e) \u00b7 r_parallel + (1 - \u03bb_aux(e)) \u00b7 (I[success] \u00b7 Q(\u03c4))\r\n      \u2191                        \u2191\r\n  instantiation reward    task-level outcome\r\n<\/code><\/pre>\n
              Annealing Schedule<\/strong>: \u03bb_aux decreases from 0.1 \u2192 0.0 over training<\/p>\n
              Early Training<\/strong>: Auxiliary reward r_parallel incentivizes sub-agent instantiation<\/p>\n
              Late Training<\/strong>: Focus shifts to end-to-end task quality Q(\u03c4)<\/p>\n
              Prevents<\/strong>: Degenerate solutions where parallelism exists nominally but not effectively<\/p>\n
              The Critical Steps Metric<\/h3>\n
              Traditional Metric<\/strong>: Total steps counted<\/p>\n
              Problem<\/strong>: Doesn't capture parallel execution benefits<\/p>\n
              Critical Steps Definition<\/strong>:<\/p>\n
              CriticalSteps = \u03a3(S_main(t) + max_i S_sub,i(t))\r\n<\/code><\/pre>\n
              Components<\/strong>:<\/p>\n
                \n
              • S_main(t): Orchestration overhead at time t<\/li>\n
              • max_i S_sub,i(t): Slowest sub-agent at time t<\/li>\n<\/ul>\n
                Inspiration<\/strong>: Critical path in parallel computation theory<\/p>\n
                Forcing Function<\/strong>: Spawning more subtasks only helps if shortening critical path<\/p>\n
                Result<\/strong>: Genuine parallel strategies emerge during training<\/p>\n
                Performance Improvements<\/h3>\n
                End-to-End Runtime Reduction<\/strong>: Up to 80%<\/p>\n
                Speedup Factor<\/strong>: 3x\u20134.5x compared to single-agent execution<\/p>\n
                Critical Steps Reduction<\/strong>: 3x\u20134.5x fewer steps to achieve target performance<\/p>\n
                Scaling Behavior<\/strong>: Savings increase as task complexity rises<\/p>\n
                Wall-Clock Impact<\/strong>: 4.5x time reduction via parallelization<\/p>\n
                Complex Workloads<\/strong>: Enables longer-horizon tasks previously impractical<\/p>\n
                Execution Capacity<\/h3>\n
                Maximum Sub-Agents<\/strong>: 100 concurrent<\/p>\n
                Maximum Tool Calls<\/strong>: 1,500 coordinated steps<\/p>\n
                Coordination Complexity<\/strong>: Automatic orchestration without manual workflow design<\/p>\n
                Benchmark Performance<\/strong>: Strong results on HLE, BrowseComp, SWE-Verified<\/p>\n
                Cost Efficiency<\/strong>: Fraction of competitor costs while maintaining performance<\/p>\n
                Training Progress Visualization<\/h3>\n
                Smooth Reward Increase<\/strong>: Gradual improvement throughout training<\/p>\n
                Parallelism Level<\/strong>: Gradually increasing agent coordination<\/p>\n
                Convergence<\/strong>: Stable final performance without collapse<\/p>\n
                Reliability<\/strong>: Production-ready coordination mechanisms<\/p>\n
                Part IV: Office Productivity Revolution<\/h2>\n
                Real-World Knowledge Work<\/h3>\n
                Target<\/strong>: High-density, large-scale office tasks<\/p>\n
                End-to-End Handling<\/strong>: From input to finished deliverable<\/p>\n
                Output Formats<\/strong>:<\/p>\n
                  \n
                • Microsoft Word documents<\/li>\n
                • Excel spreadsheets<\/li>\n
                • PDF files<\/li>\n
                • PowerPoint slide decks<\/li>\n<\/ul>\n
                  Interface<\/strong>: All through natural conversation<\/p>\n
                  Advanced Office Capabilities<\/h3>\n
                  Word Processing<\/strong>:<\/p>\n
                    \n
                  • Adding annotations<\/li>\n
                  • Complex formatting<\/li>\n
                  • Long-form content (10,000+ words)<\/li>\n<\/ul>\n
                    Spreadsheet Mastery<\/strong>:<\/p>\n
                      \n
                    • Financial model construction<\/li>\n
                    • Pivot Table creation<\/li>\n
                    • Advanced formulas<\/li>\n<\/ul>\n
                      PDF Generation<\/strong>:<\/p>\n
                        \n
                      • LaTeX equation writing<\/li>\n
                      • Professional formatting<\/li>\n
                      • 100+ page documents<\/li>\n<\/ul>\n
                        Presentation Creation<\/strong>:<\/p>\n
                          \n
                        • Slide deck generation<\/li>\n
                        • Visual design<\/li>\n
                        • Content organization<\/li>\n<\/ul>\n
                          Internal Expert Productivity Benchmarks<\/h3>\n
                          AI Office Benchmark<\/strong>: Evaluates end-to-end Office output quality<\/p>\n
                          General Agent Benchmark<\/strong>: Measures multi-step production workflows against human experts<\/p>\n
                          K2.5 vs K2 Thinking Improvements<\/strong>:<\/p>\n
                            \n
                          • 59.3% improvement<\/strong> on AI Office Benchmark<\/li>\n
                          • 24.3% improvement<\/strong> on General Agent Benchmark<\/li>\n<\/ul>\n
                            Real-World Focus<\/strong>: Tasks professionals actually perform daily<\/p>\n
                            Expert-Level Output<\/strong>: Matching or exceeding human professional quality<\/p>\n
                            Time Compression<\/h3>\n
                            Previous Reality<\/strong>: Tasks taking hours or days<\/p>\n
                            K2.5 Performance<\/strong>: Minutes to completion<\/p>\n
                            Productivity Multiplier<\/strong>: 10x-100x time savings potential<\/p>\n
                            Workflow Integration<\/strong>: Seamlessly fitting into existing processes<\/p>\n
                            Professional Impact<\/strong>: Redefining knowledge worker productivity<\/p>\n
                            Part V: Benchmark Performance Deep Dive<\/h2>\n
                            Coding Benchmarks<\/h3>\n
                            SWE-Bench Series<\/strong> (Verified, Multilingual, Pro):<\/p>\n
                              \n
                            • Minimal toolset (bash, createfile, insert, view, strreplace, submit)<\/li>\n
                            • Tailored system prompts<\/li>\n
                            • Non-thinking mode optimal<\/li>\n
                            • Averaged over 5 independent runs<\/li>\n<\/ul>\n
                              Terminal-Bench 2.0<\/strong>:<\/p>\n
                                \n
                              • Default Terminus-2 agent framework<\/li>\n
                              • JSON parser provided<\/li>\n
                              • Non-thinking mode for compatibility<\/li>\n<\/ul>\n
                                CyberGym<\/strong>: Claude Opus 4.5 comparison under non-thinking setting<\/p>\n
                                Kimi Code Bench<\/strong>: Strong improvements across all task categories<\/p>\n
                                Vision Benchmarks<\/h3>\n
                                MMMU-Pro<\/strong>: Official protocol, input order preserved, images prepended<\/p>\n
                                WorldVQA<\/strong>: Atomic vision-centric world knowledge evaluation (github.com\/MoonshotAI\/WorldVQA)<\/p>\n
                                OmniDocBench<\/strong>: Score = (1 – normalized Levenshtein distance) \u00d7 100<\/p>\n
                                ZeroBench (with tools)<\/strong>: Multi-step reasoning with 24k tokens per step, 30 max steps<\/p>\n
                                Averaging<\/strong>: 3 runs (avg@3) for consistency<\/p>\n
                                Agentic Search Benchmarks<\/h3>\n
                                Tools Equipped<\/strong>: Search, code-interpreter, web-browsing<\/p>\n
                                Context Management<\/strong>: No management except BrowseComp (discard-all strategy)<\/p>\n
                                Context Overflow<\/strong>: Tasks exceeding limit counted as failed<\/p>\n
                                System Prompts<\/strong>: Emphasizing deep and proactive tool use<\/p>\n
                                Averaging<\/strong>: 4 runs (avg@4) for Seal-0 and WideSearch<\/p>\n
                                Reasoning Benchmarks<\/h3>\n
                                HLE<\/strong> (Text & Image):<\/p>\n
                                  \n
                                • Full set: Text 31.5, Image 21.3 (without tools)<\/li>\n
                                • Full set: Text 51.8, Image 39.8 (with tools)<\/li>\n
                                • 96k token completion budget<\/li>\n
                                • Hugging Face access blocked (prevent data leakage)<\/li>\n<\/ul>\n
                                  AIME 2025<\/strong>: 96k token budget, avg@32 (32 runs)<\/p>\n
                                  HMMT 2025 (Feb)<\/strong>: 96k token budget, avg@32<\/p>\n
                                  GPQA-Diamond<\/strong>: 96k token budget, avg@8<\/p>\n
                                  IMO-AnswerBench<\/strong>: 96k token budget<\/p>\n
                                  Long-Context Performance<\/h3>\n
                                  AA-LCR<\/strong>: Averaged over 3 runs (avg@3)<\/p>\n
                                  LongBench-V2<\/strong>: Identical prompts, input standardized to ~128k tokens<\/p>\n
                                  Context Length<\/strong>: 256k tokens supported<\/p>\n
                                  Part VI: Access and Availability<\/h2>\n
                                  Four Modes Available<\/h3>\n
                                  K2.5 Instant<\/strong>: Fast responses for simple queries<\/p>\n
                                  K2.5 Thinking<\/strong>: Extended reasoning for complex problems<\/p>\n
                                  K2.5 Agent<\/strong>: Tool-augmented execution with preconfigured capabilities<\/p>\n
                                  K2.5 Agent Swarm (Beta)<\/strong>: Multi-agent parallel coordination<\/p>\n
                                  Beta Access<\/strong>: Agent Swarm with free credits for high-tier paid users<\/p>\n
                                  Platform Options<\/h3>\n
                                  Kimi.com<\/strong>: Web-based interface with all four modes<\/p>\n
                                  Kimi App<\/strong>: Mobile\/desktop application<\/p>\n
                                  API<\/strong>: platform.moonshot.ai for developer integration<\/p>\n
                                  Kimi Code<\/strong>: Terminal-based coding assistant<\/p>\n
                                  Open Source<\/strong>: Kimi Code released as open-source project<\/p>\n
                                  Configuration Details<\/h3>\n
                                  Temperature<\/strong>: 1.0 (default)<\/p>\n
                                  Top-p<\/strong>: 0.95 (default)<\/p>\n
                                  Context Length<\/strong>: 256k tokens<\/p>\n
                                  Reproducibility<\/strong>: Official API recommended for benchmark recreation<\/p>\n
                                  Vendor Verification<\/strong>: Kimi Vendor Verifier (KVV) for third-party services<\/p>\n
                                  Part VII: The Road to AGI<\/h2>\n
                                  Meaningful Step Forward<\/h3>\n
                                  For Open-Source Community<\/strong>: Most powerful model demonstrating real-world capability<\/p>\n
                                  Under Real Constraints<\/strong>: Strong performance within practical limitations<\/p>\n
                                  Production Readiness<\/strong>: Suitable for actual knowledge work deployment<\/p>\n
                                  The Future Direction<\/h3>\n
                                  Continued Advancement<\/strong>: Pushing further into agentic intelligence frontier<\/p>\n
                                  Boundary Redefinition<\/strong>: Challenging assumptions about AI capabilities in knowledge work<\/p>\n
                                  Research Focus<\/strong>: Expanding parallel coordination and visual reasoning<\/p>\n
                                  Open Ecosystem<\/strong>: Contributing to accessible AI advancement<\/p>\n
                                  Conclusion: Visual Agentic Intelligence Arrives<\/h2>\n
                                  The Three Pillars<\/h3>\n
                                  1. Coding with Vision<\/strong>: Native multimodal architecture enabling visual debugging and image-to-code workflows<\/p>\n
                                  2. Agent Swarm<\/strong>: Self-directed parallel coordination with up to 100 sub-agents and 1,500 tool calls<\/p>\n
                                  3. Office Productivity<\/strong>: Expert-level document\/spreadsheet\/PDF\/slide generation through conversation<\/p>\n
                                  The Performance Story<\/h3>\n
                                  59.3% improvement<\/strong> on AI Office Benchmark over K2 Thinking<\/p>\n
                                  24.3% improvement<\/strong> on General Agent Benchmark<\/p>\n
                                  4.5x speedup<\/strong> through agent swarm parallelization<\/p>\n
                                  State-of-the-art<\/strong> coding and vision capabilities<\/p>\n
                                  Fraction of cost<\/strong> compared to proprietary competitors<\/p>\n
                                  The Technical Innovation<\/h3>\n
                                  15 trillion tokens<\/strong> of vision-text joint pretraining<\/p>\n
                                  PARL training<\/strong> with staged reward shaping<\/p>\n
                                  Critical-steps metric<\/strong> forcing genuine parallelism<\/p>\n
                                  No predefined workflows<\/strong> in agent orchestration<\/p>\n
                                  Autonomous visual debugging<\/strong> capability<\/p>\n
                                  The Accessibility<\/h3>\n
                                  Open-source model<\/strong> pushing frontier forward<\/p>\n
                                  Multiple access points<\/strong>: Web, app, API, terminal<\/p>\n
                                  Four operational modes<\/strong> for different use cases<\/p>\n
                                  Beta features<\/strong> with free credits for experimentation<\/p>\n
                                  The Paradigm Shift<\/h3>\n
                                  From sequential to parallel<\/strong> agent execution<\/p>\n
                                  From text-only to native multimodal<\/strong> reasoning<\/p>\n
                                  From hours to minutes<\/strong> for complex knowledge work<\/p>\n
                                  From predefined to self-directed<\/strong> workflow coordination<\/p>\n
                                  \n
                                  Get Started<\/strong>:<\/p>\n
                                    \n
                                  • Web<\/strong>: https:\/\/www.kimi.com<\/li>\n
                                  • API<\/strong>: https:\/\/platform.moonshot.ai<\/li>\n
                                  • Code<\/strong>: https:\/\/www.kimi.com\/code<\/li>\n
                                  • Modes<\/strong>: Instant, Thinking, Agent, Agent Swarm (Beta)<\/li>\n<\/ul>\n
                                    Technical Report<\/strong>: Full details including prompts and methodology forthcoming<\/p>\n
                                    Vendor Verification<\/strong>: https:\/\/kimi.com\/blog\/kimi-vendor-verifier.html<\/p>\n
                                    WorldVQA Benchmark<\/strong>: https:\/\/github.com\/MoonshotAI\/WorldVQA<\/p>\n
                                    \n
                                    The Bottom Line<\/strong>: Kimi K2.5 represents the most powerful open-source model to date, achieving breakthrough performance through native multimodal architecture (15T vision-text tokens), self-directed agent swarm coordination (100 sub-agents, 1,500 tool calls, 4.5x speedup), state-of-the-art coding with vision (autonomous visual debugging), and expert-level office productivity (59.3% AI Office improvement, 24.3% General Agent improvement). The combination of visual agentic intelligence with PARL-trained parallel orchestration marks meaningful step toward AGI for open-source community, demonstrating strong capability on real-world tasks under real-world constraints at fraction of proprietary model costs. Access via Kimi.com, app, API, and open-source Kimi Code terminal tool across four modes (Instant\/Thinking\/Agent\/Agent Swarm Beta). The future of agentic intelligence is parallel, visual, and open.<\/p>\n
                                    Try Agent Swarm Beta<\/strong>: Experience 100-agent coordination redefining knowledge work efficiency. \ud83e\udd9e\u2728<\/p>","protected":false},"excerpt":{"rendered":"
                                    The Breakthrough: 100 Sub-Agents, 1,500 Tool Calls, 4.5x Speed Increase\u2014Native Multimodal AI Redefining Agentic Intelligence Kimi K2.5 represents the most […]<\/p>","protected":false},"author":11214,"featured_media":135527,"menu_order":0,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[468],"tags":[],"class_list":["post-135519","aitools","type-aitools","status-publish","format-standard","has-post-thumbnail","hentry","category-best-post"],"acf":[],"_links":{"self":[{"href":"https:\/\/legacy.vertu.com\/ar\/wp-json\/wp\/v2\/aitools\/135519","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/legacy.vertu.com\/ar\/wp-json\/wp\/v2\/aitools"}],"about":[{"href":"https:\/\/legacy.vertu.com\/ar\/wp-json\/wp\/v2\/types\/aitools"}],"author":[{"embeddable":true,"href":"https:\/\/legacy.vertu.com\/ar\/wp-json\/wp\/v2\/users\/11214"}],"version-history":[{"count":2,"href":"https:\/\/legacy.vertu.com\/ar\/wp-json\/wp\/v2\/aitools\/135519\/revisions"}],"predecessor-version":[{"id":135529,"href":"https:\/\/legacy.vertu.com\/ar\/wp-json\/wp\/v2\/aitools\/135519\/revisions\/135529"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/legacy.vertu.com\/ar\/wp-json\/wp\/v2\/media\/135527"}],"wp:attachment":[{"href":"https:\/\/legacy.vertu.com\/ar\/wp-json\/wp\/v2\/media?parent=135519"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/legacy.vertu.com\/ar\/wp-json\/wp\/v2\/categories?post=135519"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/legacy.vertu.com\/ar\/wp-json\/wp\/v2\/tags?post=135519"}],"curies":[{"name":"\u0648\u0648\u0631\u062f\u0628\u0631\u064a\u0633","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}