MoonshotAI: Kimi K2.6

Kimi K2.6 is Moonshot AI’s open-source native multimodal agentic model, designed for long-horizon coding, coding-driven UI/UX generation, proactive autonomous execution, and multi-agent orchestration. Built on a 1T-parameter Mixture-of-Experts architecture with 32B active parameters, it is optimized for complex coding, visual understanding, tool use, and large-scale agent workflows.

Why It Looks Great

• Open-source native multimodal agentic model from Moonshot AI
• 1T-parameter Mixture-of-Experts architecture with 32B active parameters
• 262K-token context window for long prompts, large codebases, documents, and multi-turn workflows
• Strong long-horizon coding performance across Python, Rust, Go, front-end, DevOps, and optimization tasks
• Excellent fit for coding-driven UI/UX generation, including full-stack apps and polished interfaces
• Agent Swarm capabilities for decomposing and coordinating complex multi-agent workflows
• Vision input support for screenshots, mockups, diagrams, and multimodal document understanding
• Thinking mode and preserve-thinking support for multi-step reasoning and coding agent scenarios
• Function calling and tool-use support for agentic application workflows
• Structured output support for JSON responses and schema-constrained generation

Key Features

• Architecture: Mixture-of-Experts
• Total Parameters: 1T
• Active Parameters: 32B
• Context Window: 262,144 tokens
• Max Input: Not listed
• Max Output: Not listed
• Input: Text, Image
• Output: Text
• Vision: Supported
• Function Calling: Supported
• Structured Outputs: Supported
• Thinking Mode: Supported
• Preserve Thinking: Supported
• Image Generation: Not listed
• Audio Input: Not listed
• Supported Parameters: frequency_penalty, include_reasoning, logit_bias, logprobs, max_tokens, min_p, parallel_tool_calls, presence_penalty, reasoning, reasoning_effort, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_logprobs, top_p

Specifications

Pricing

How to Use

1. Write your prompt - describe the task, provide context, and specify the desired output format.
2. Submit - the model processes your request and returns the response.

API Integration

Base URL: https://llm.wavespeed.ai/v1
API Endpoint: chat/completions
Model ID: moonshotai/kimi-k2.6

API Usage

Python SDK

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://llm.wavespeed.ai/v1"
)

response = client.chat.completions.create(
    model="moonshotai/kimi-k2.6",
    messages=[{"role": "user", "content": "Hello!"}]
)

print(response.choices[0].message.content)

cURL

curl https://llm.wavespeed.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "moonshotai/kimi-k2.6",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Notes

• Model: moonshotai/kimi-k2.6
• Provider: moonshot
• Best suited for long-horizon coding, UI/UX generation, visual understanding, tool use, multi-agent orchestration, and autonomous workflow execution