Try Kimi K2.5
Native multimodal action agent model • 256K context • instant & thinking modes
Kimi K2.5 Assistant
256K context • vision + text reasoning • agent swarm
Fast Response
Get instant answers powered by our optimized infrastructure
Privacy First
Your conversations are secure and never used for training
Premium Features
Sign in to unlock API access and unlimited conversations
Kimi K2.5 at a glance
A concise snapshot of the scale, context window, and multimodal stack highlighted in the deep research report on Kimi K2.5.
Total Parameters
1T
Sparse Mixture-of-Experts capacity for large-scale reasoning
Activated Parameters
32B
Per-token active compute for efficiency
Context Window
256K
Long-context processing for complex tasks
Vision Encoder
400M
MoonViT backbone for high-resolution vision
Native multimodal agentic design
Kimi K2.5 is positioned as a native multimodal vision agent model that treats images, video, and text as first-class inputs. The report frames the 2026 release as a strategic pivot toward action agents that coordinate tools and sub-agents to solve complex problems in parallel. Pretraining on ~15T mixed visual and text tokens provides the foundation for coding with vision, agentic search, and long-horizon execution without sacrificing efficiency. Below are the core capabilities summarized from the research report.
Native Multimodality with MoonViT
Kimi K2.5 integrates the MoonViT vision encoder (about 400M parameters) to process high-resolution images and videos natively. The report specifies support up to 4K images (4096×2160) and 2K video (2048×1080), with common formats such as png, jpeg, webp, gif and video formats including mp4, mpeg, mov, avi, flv, mpg, webm, wmv, and 3gpp. Inputs are provided via base64 or file upload (URLs are not supported), and vision features are pooled spatially and temporally before projection into the language model.
Coding with Vision
The report highlights a coding with vision workflow in which Kimi K2.5 converts UI screenshots or screen recordings into functional front-end code. It can translate visual mockups into React or HTML/CSS, and is described as capable of generating richer UI aesthetics, including animation behaviors such as scroll-triggered effects. This capability is presented as a practical bridge between visual specifications and executable software artifacts.
Autonomous Visual Debugging
Kimi K2.5 is described as able to visually inspect its own output by comparing rendered screenshots against the original design, then iterating to fix discrepancies. This closes the loop between perception and generation, enabling a model-in-the-loop debugging cycle for front-end fidelity and visual correctness. It supports iterative refinement that is hard to achieve with text-only models.
Visual Logic Reasoning
Beyond aesthetics, the report notes that Kimi K2.5 can reason over complex images. In a 4.5-megapixel maze test, the model identifies start and end points, writes an algorithmic solution such as BFS, and visualizes the route on the image. This reflects a stronger integration of perception, algorithmic reasoning, and tool-driven post-processing.
Agent Swarm Orchestration
A defining feature is the Agent Swarm paradigm, which allows Kimi K2.5 to coordinate up to 100 sub-agents for parallel execution. The report attributes this to Parallel-Agent Reinforcement Learning (PARL) to address serial collapse and to a critical steps metric that rewards reduced wall-clock latency. Reported outcomes include up to 80% end-to-end runtime reduction and as much as 4.5× execution efficiency in swarm mode.
Agentic Benchmark Leadership
The research report highlights Kimi K2.5’s agentic and reasoning performance on benchmarks such as BrowseComp and HLE. Reported scores include BrowseComp accuracy of 78.4% (Swarm Mode), HLE-Full with tools at 50.2%, and AIME 2025 at 96.1%. These numbers are presented as evidence that Kimi K2.5 closes the gap with frontier closed-source models on agent-centric tasks.
Model Summary
Kimi K2.5 uses a highly optimized sparse MoE transformer architecture designed to balance trillion-parameter capacity with efficient inference. The report emphasizes that only a small subset of experts is activated per token while maintaining a large overall parameter budget. Architectural details below are drawn directly from the model summary in the report and provide a technical baseline for understanding Kimi K2.5’s performance characteristics.
Trillion-Parameter Sparse MoE
The model is described as a transformer-based sparse MoE with 1T total parameters and 32B activated parameters per token. This sparsity enables large capacity without proportional compute costs, which is central to Kimi K2.5’s efficiency claims and its ability to scale to long-context tasks.
384 Experts with Shared Routing
Kimi K2.5 uses 384 experts and selects 8 experts per token, plus 1 shared expert. The report notes that the higher expert count increases representational density and specialization across domains, improving both reasoning and tool-oriented behaviors.
61 Layers with 1 Dense Layer
The architecture includes 61 total layers with a single dense layer, a structure designed to keep the model stable while retaining MoE flexibility. This configuration supports both large-scale capacity and reliable optimization.
MLA Attention and Head Count
The attention mechanism is Multi-head Latent Attention (MLA) with 64 heads and a 7168 attention hidden dimension. The report emphasizes MLA as a key component for maintaining coherence over long contexts.
MoE Hidden Dimension 2048
Each expert operates with a hidden dimension of 2048. This per-expert size is tuned to maintain efficiency while enabling specialization across coding, vision reasoning, and agentic task patterns.
160K Vocabulary and 256K Context
The model summary specifies a 160K vocabulary and a 256K context window. This combination supports long-document understanding and multimodal tokenization for vision-text workflows, enabling Kimi K2.5 to handle extensive repositories or complex visual reasoning chains in a single session.
SwiGLU Activation
SwiGLU is listed as the activation function, a choice often associated with strong stability and performance at scale. In the report, this detail appears alongside MLA and MoE routing as part of the core architectural stack.
Benchmarks & Validation
The report highlights Kimi K2.5's strength on agentic, reasoning, and multimodal evaluations. These figures are presented as early-2026 results and emphasize broad capability across web navigation, reasoning with tools, and visual understanding.
BrowseComp (Swarm Mode) 78.4%
Reported accuracy on BrowseComp, a benchmark for continuous web navigation and synthesis, showcasing the Agent Swarm approach.
HLE-Full (with tools) 50.2%
Performance on Humanity's Last Exam with tool use enabled, reflecting long-horizon reasoning under tooling constraints.
AIME 2025 96.1%
High accuracy on AIME 2025, indicating strong mathematical reasoning in structured evaluations.
OCRBench 92.3
Document intelligence and visual text understanding benchmark emphasizing OCR robustness.
MMMU-Pro 78.5 & VideoMMMU 86.6
Multimodal understanding across image and video reasoning tasks as listed in the report.
MathVision 84.2
Visual math reasoning performance demonstrating image-grounded problem solving.
Industry Applications
The report documents early production use cases where Kimi K2.5's multimodal perception and agentic orchestration are applied to domain workflows.
Financial Research
Platforms such as AlphaEngine reportedly use K2.5 for chart analysis, 300-step tool calls, and automated macroeconomic reports, reducing costs by around 60% according to the report.
Life Sciences
Teams like XtalPi use K2.5 to read chemical formulas and extract key evidence from scientific literature to accelerate discovery pipelines.
Legal & Office Intelligence
The model is applied to dense document workflows, including contract review and risk analysis, generating deliverables such as PDFs, slides, and spreadsheets.
Visual Frontend Engineering
K2.5 converts visual specs into working UI code and iteratively aligns output with design references, reducing handoff friction between design and engineering.
Agentic Search Workflows
Swarm-mode coordination enables parallel research and verification steps, improving turnaround time for multi-source synthesis.
Developer Tooling
Kimi Code integrates with editors like VS Code, Cursor, and Zed, enabling image/video-guided agent workflows inside IDEs.
Deployment & Optimization
The report covers a broad deployment picture: open-source availability, API access, and local inference options with quantization. It also outlines practical constraints for multimodal inputs and the ecosystem tools built around Kimi K2.5 for real-world usage in engineering teams.
Native INT4 Quantization
Kimi K2.5 supports native INT4 quantization, which the report associates with up to 2× generation speedups on consumer-grade hardware. This is positioned as a practical pathway to deploy a trillion-parameter MoE without datacenter-only infrastructure.
Local Deployment Profiles
The report lists reference profiles for local inference. Full FP16/BF16 runs are associated with 4× NVIDIA H200 (or more) and >40 tokens/s. A 4-bit dynamic GGUF configuration targets ~10–20 tokens/s with 256GB unified memory. A 1.8-bit configuration (Unsloth) targets ~10 tokens/s with a single 24GB GPU and MoE offload. Minimum disk space is listed as >240GB for quantized weights.
OpenAI & Anthropic Compatible API
Kimi K2.5 is described as accessible via platform.moonshot.ai with OpenAI- and Anthropic-compatible interfaces. This allows existing applications to switch endpoints with minimal changes while retaining streaming and tool-call behavior.
Kimi Code Ecosystem
The report highlights Kimi Code, a CLI tool that integrates with VS Code, Cursor, and Zed. It is designed to accept images and videos as specifications, enabling multimodal agent workflows inside developer tooling rather than outside of it.
Vision Input Constraints
For multimodal usage, the report notes supported image formats (png, jpeg, webp, gif) and video formats (mp4, mpeg, mov, avi, flv, mpg, webm, wmv, 3gpp). Input methods are base64 and file upload; URLs are not supported. Images are processed up to 4K and videos up to 2K resolution.
Agentic Performance Context
Benchmarks cited in the report include OCRBench 92.3, MMMU-Pro 78.5, VideoMMMU 86.6, and MathVision 84.2, as well as SWE-Bench Verified at 76.8 and multilingual coding at 73.0. These results are presented to contextualize Kimi K2.5’s multimodal and coding capabilities in early 2026 evaluations.
Kimi K2.5 FAQ
A detailed, source-based summary for builders and researchers evaluating Kimi K2.5.
What is Kimi K2.5 and why is it called an action agent model?
The report frames Kimi K2.5 as a January 2026 release that marks a shift from conversational AI to action agents. The model is positioned to execute complex workflows in parallel rather than respond in a single linear thread. This is reinforced by the Agent Swarm design, which enables coordinated sub-agents and focuses on reducing wall-clock latency through parallel execution strategies.
How does Kimi K2.5 handle multimodal inputs?
Kimi K2.5 integrates MoonViT (about 400M parameters) for native vision processing. The report describes support for high-resolution images up to 4K and videos up to 2K, with common image formats (png, jpeg, webp, gif) and video formats (mp4, mpeg, mov, avi, flv, mpg, webm, wmv, 3gpp). Inputs are provided via base64 or file upload, and URLs are explicitly noted as not supported.
What makes the Agent Swarm approach different?
The Agent Swarm paradigm allows Kimi K2.5 to coordinate up to 100 sub-agents for parallel execution. The report links this to Parallel-Agent Reinforcement Learning (PARL) to mitigate serial execution and to a critical steps metric that rewards reduced latency. Reported outcomes include up to 80% end-to-end runtime reduction and up to 4.5× improvement in execution efficiency.
Which benchmark results are highlighted in the report?
For agentic reasoning, the report cites BrowseComp accuracy of 78.4% (Swarm Mode), HLE-Full with tools at 50.2%, and AIME 2025 at 96.1%. For multimodal reasoning, it lists OCRBench 92.3, MMMU-Pro 78.5, VideoMMMU 86.6, and MathVision 84.2. In software engineering, it reports SWE-Bench Verified at 76.8 and multilingual coding at 73.0, noting that some closed-source models still lead on specific benchmarks but Kimi K2.5 performs strongly across modalities.
What are the main deployment options for Kimi K2.5?
The report describes both API and local deployment paths. API access is offered via platform.moonshot.ai with OpenAI/Anthropic compatibility. For local inference, it highlights native INT4 quantization and provides reference configurations ranging from multi-H200 FP16/BF16 setups to 4-bit GGUF and 1.8-bit Unsloth configurations. It also notes a minimum disk requirement of more than 240GB for quantized weights.
Where does Kimi K2.5 show real-world impact?
The report cites industry adoption examples including financial research platforms (such as AlphaEngine) that run 300-step tool-call workflows and reportedly reduce business costs by around 60%, life-sciences teams (such as XtalPi) that extract data from scientific literature, and legal or office workflows that automate contract review and generate deliverables like PDFs, slides, and spreadsheets. These examples are used to illustrate the model’s applicability beyond chat.
Build with Kimi K2.5
Start with the API or explore pricing to scale usage.