Moonshot AI: Kimi K3

Kimi K3 is Moonshot AI's flagship open-weight multimodal reasoning model. It is designed for complex coding, knowledge work, and long-horizon agentic workflows, and is especially strong at large repository understanding, tool use, debugging, and iterative work across images, logs, tests, and runtime feedback.

WaveSpeed AI exposes moonshotai/kimi-k3 through an OpenAI-compatible API, so it can be used with standard OpenAI SDKs and existing chat-completions-based application flows.

Why Use Kimi K3

• Flagship Kimi model for advanced reasoning and software work
• Strong long-horizon coding performance across large repositories and multi-step tasks
• Well suited for agentic workflows, tool use, and structured outputs
• Native multimodal capability for image-based understanding and visual iteration
• Long-context support for document analysis, code review, and extended multi-turn sessions

Key Features

• Context Window: 1,000,000 tokens
• Max Output: up to 131,072 tokens by default
• Vision Input: Supported
• Function Calling: Supported
• Structured Outputs: Supported
• Reasoning: Enabled by default
• Best Fit: coding, reasoning, agents, multimodal workflows, long-context tasks

Specifications

Architecture Notes

Kimi K3 is built with KDA (Kimi Delta Attention) and Attention Residuals to improve computational efficiency at scale. It is positioned for demanding workflows such as long-horizon programming, knowledge-intensive tasks, and multimodal reasoning over both text and images.

How to Use

Chat Completions

Use Chat Completions when you want a straightforward OpenAI-compatible integration path for conversational, coding, and agent workflows.

Python

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-k3",
    messages=[
        {"role": "user", "content": "Review this bug report and identify the most likely root cause."}
    ]
)

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-k3",
    "messages": [
      {"role": "user", "content": "Review this bug report and identify the most likely root cause."}
    ]
  }'

Multimodal Example

Kimi K3 supports native visual understanding, making it a strong fit for tasks such as screenshot debugging, UI review, diagram analysis, and image-grounded reasoning.

Python

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-k3",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Describe the issue shown in this screenshot."},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "data:image/png;base64,BASE64_IMAGE_DATA"
                    }
                }
            ]
        }
    ]
)

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

Tool Use and Structured Output

Kimi K3 is well suited for applications that combine reasoning with tools and schema-constrained outputs.

Common use cases include:

• debugging agents that inspect logs, test output, and code
• repository assistants that navigate large codebases
• multimodal workflows that combine screenshots with implementation tasks
• structured extraction pipelines that require JSON output

Notes

• Official upstream model name is kimi-k3
• WaveSpeed model ID is drafted here as moonshotai/kimi-k3
• Reasoning is part of the model's default behavior
• Best paired with agentic and long-context workflows where tool use and iterative refinement matter