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Available now on ModelsLab · Language Model

MoonshotAI: Kimi K2 ThinkingReasoning. Agents. Scale.

Try MoonshotAI: Kimi K2 ThinkingAPI Documentation

Think Deeper. Execute Faster.

Transparent Reasoning

See How It Thinks

Expose reasoning trajectories through dedicated API fields for explainable problem-solving.

Agentic Tool Use

200-300 Sequential Tool Calls

Autonomously orchestrate complex workflows without manual intervention between steps.

Efficient Scale

1T Parameters, 32B Active

Massive model power with MoE efficiency and 256K token context window.

Examples

See what MoonshotAI: Kimi K2 Thinking can create

Copy any prompt below and try it yourself in the playground.

Data Analysis Pipeline

“Analyze this quarterly financial dataset: retrieve relevant market benchmarks, calculate variance metrics, generate comparative visualizations, and produce an executive summary with actionable insights.”

Code Generation

“Build a REST API endpoint that validates user input, queries a PostgreSQL database, implements caching logic, and returns paginated JSON responses with proper error handling.”

Research Synthesis

“Research the latest advances in transformer architectures, compare three competing approaches, evaluate their trade-offs, and synthesize findings into a technical overview.”

Workflow Automation

“Create an automated workflow that monitors email inboxes, extracts structured data from messages, updates a CRM system, and generates weekly activity reports.”

For Developers

A few lines of code.
Reasoning. Three lines.

ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.

  • Serverless: scales to zero, scales to millions
  • Pay per token, no minimums
  • Python and JavaScript SDKs, plus REST API
API Documentation 
import requests


response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())

FAQ

Common questions about MoonshotAI: Kimi K2 Thinking

Read the docs

K2 Thinking combines transparent reasoning with autonomous tool orchestration, executing 200-300 sequential tool calls without human intervention. Its MoE architecture delivers 1 trillion parameter capacity while activating only 32 billion per token, balancing power with inference efficiency.

The Mixture-of-Experts design uses 384 experts with 8 active per token, enabling the model to specialize across domains while maintaining computational efficiency. This allows K2 to handle complex reasoning tasks at lower inference costs than dense alternatives.

Yes, access the MoonshotAI: Kimi K2 Thinking API directly through Moonshot's platform or via OpenRouter, which provides unified gateway access alongside other leading models with a single API key.

Kimi K2 Thinking supports a 256,000-token context window, allowing you to process entire codebases, long documents, and extensive conversation histories without chunking.

The MoonshotAI: Kimi K2 Thinking model can execute 200-300 sequential tool calls autonomously, making it ideal for complex agentic workflows and multi-step automation tasks.

K2 Thinking excels at code generation, mathematical reasoning, knowledge-based QA, autonomous task execution, and complex problem-solving requiring transparent reasoning and tool orchestration.

Ready to create?

Start generating with MoonshotAI: Kimi K2 Thinking on ModelsLab.

Try MoonshotAI: Kimi K2 ThinkingAPI Documentation