Description
Kimi K3.1 Pro represents a significant advancement in multimodal agentic models, offering an open-weight solution with a massive 2.8 trillion parameters. As the inaugural model in the K3.1 series, it builds upon the Kimi K3 architecture, enhanced through extensive joint reinforcement learning across hundreds of downstream domains. This rigorous training has resulted in a model that excels in a variety of complex tasks.
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A core strength of Kimi K3.1 Pro is its impressive 1-million-token context window, enabling it to process and understand exceptionally long and intricate inputs. This extended context capability is crucial for demanding applications such as advanced coding, where understanding large codebases is essential, and detailed financial research, which often involves analyzing extensive documents and data sets. The model also shows substantial improvements in 3D creation and overall factual accuracy, making it a versatile tool for developers and researchers.
Under the hood, Kimi K3.1 Pro employs a Mixture-of-Experts (MoE) architecture, with 104 billion activated parameters and 93 layers, including 69 KDA and 24 Gated DSA attention layers for efficient long-context modeling. Its vision encoder, MoonViT-V2, contributes 401 million parameters, facilitating its natively multimodal capabilities, processing both text and image inputs. The model supports MXFP4 weights and MXFP8 activations through quantization-aware training, optimizing performance.
Kimi K3.1 Pro has undergone extensive evaluation across numerous benchmarks, demonstrating competitive performance against other leading models in categories like coding (e.g., Terminal-Bench 4.0, SWE Marathon), agentic tasks (e.g., HLE-Full w/ tools, BrowseComp), reasoning and knowledge (e.g., GPQA-Diamond, MMMU-Pro), and cybersecurity (e.g., ExploitBench, ExploitGym). These evaluations highlight its robust capabilities in complex problem-solving and information processing.
Deployment and accessibility are streamlined, with Kimi K3.1 Pro's API available on the platform.kimi.ai, offering OpenAI/Anthropic-compatible API access. It is recommended for use with inference engines such as vLLM, SGLang, and TokenSpeed. Users can configure the model's thinking effort via the `reasoning_effort` request field, with options for "low," "high," and "max." The model is released under the Kimi K3.1 Pro License, and support is available via [email protected].
- CategoryAI Models & LLMs
- DeploymentCloud
- PlatformsWeb
Kimi K3.1 Pro Highlights
Open-weight, natively multimodal agentic model
2.8 trillion total parameters
1-million-token context window
Mixture-of-Experts (MoE) architecture
Enhanced coding capabilities
Improved 3D creation abilities
Advanced financial research support
High factual accuracy
Image and text modality support
Quantization-aware training (MXFP4 weights / MXFP8 activations)
API available with OpenAI/Anthropic compatibility
Configurable reasoning effort (low, high, max)
Supports multi-turn conversations and tool calls
Evaluated across extensive coding and agentic benchmarks
Released under Kimi K3.1 Pro License
Getting Started with Kimi K3.1 Pro
Access Model API: Navigate to platform.kimi.ai and select 'kimi-k3.1-pro' to access the API.
Configure Inference Engine: Run the model on recommended inference engines like vLLM, SGLang, or TokenSpeed.
Set Reasoning Effort: Configure the 'reasoning_effort' request field to 'low', 'high', or 'max' based on task requirements.
Send Prompts: Utilize the API to send text and image inputs for processing.
Handle Multi-Turn Conversations: Pass the complete assistant message, including 'reasoning_content' and 'tool_calls', back to the messages for continuous interaction.
Explore Advanced Features: Refer to the Kimi K3.1 Pro Quickstart for guides on vision input, structured output, and dynamic tool loading.
Kimi K3.1 Pro's Use Cases
- Advanced Coding
- 3D Content Creation
- Financial Research
- Factual Accuracy Enhancement
- Multimodal Agentic Tasks
- Long-Context Understanding
- Cybersecurity Analysis
- General Reasoning & Knowledge
