moonshotai/kimi-k2.6
Fecha de lanzamiento: 2026-04-20
262,144 context · $0.95/M input tokens · $4.00/M output tokens
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 supports text and image inputs, a 262K-token context window, thinking mode, preserve-thinking workflows, function calling, and structured outputs. It is especially strong for complex end-to-end coding tasks across Python, Rust, Go, front-end engineering, DevOps, performance optimization, and agentic workflow automation.
Precios
Pago por uso
Sin costos iniciales, paga solo por lo que uses
Probar el modelo
Uso de API
Usa los siguientes ejemplos de código para integrar con nuestra API:
import OpenAI from 'openai';
if (!process.env.WAVESPEED_API_KEY) throw new Error('Set WAVESPEED_API_KEY');
const client = new OpenAI({
apiKey: process.env.WAVESPEED_API_KEY,
baseURL: 'https://llm.wavespeed.ai/v1',
timeout: 120_000,
maxRetries: 2,
});
try {
const response = await client.chat.completions.create({
model: 'moonshotai/kimi-k2.6',
messages: [{ role: 'user', content: 'Hello!' }],
});
console.log(response.choices[0]?.message?.content ?? '');
} catch (error) {
console.error('LLM request failed:', error);
process.exitCode = 1;
}import OpenAI from 'openai';
if (!process.env.WAVESPEED_API_KEY) throw new Error('Set WAVESPEED_API_KEY');
const client = new OpenAI({
apiKey: process.env.WAVESPEED_API_KEY,
baseURL: 'https://llm.wavespeed.ai/v1',
timeout: 120_000,
maxRetries: 2,
});
try {
const response = await client.chat.completions.create({
model: 'moonshotai/kimi-k2.6',
messages: [{ role: 'user', content: 'Hello!' }],
});
console.log(response.choices[0]?.message?.content ?? '');
} catch (error) {
console.error('LLM request failed:', error);
process.exitCode = 1;
}Introducción del modelo
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
| Specification | Value |
|---|---|
| Provider | moonshot |
| Model Type | Chat Completions model |
| Architecture | Mixture-of-Experts |
| Parameters | 1T total / 32B active |
| Experts | 384 experts, 8 selected per token |
| Attention | MLA |
| Vision Encoder | MoonViT |
| Context Window | 262,144 tokens |
| Input | Text, Image |
| Output | Text |
| Vision | Supported |
| Function Calling | Supported |
| Structured Outputs | Supported |
| Thinking Mode | Supported |
Pricing
| Token Type | Cost |
|---|---|
| Input | $0.73 per million tokens |
| Output | $3.49 per million tokens |
| Cached Input | $0.25 per million tokens |
How to Use
- Write your prompt - describe the task, provide context, and specify the desired output format.
- 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
Información
Funcionalidades compatibles
Guía de acceso a la API
Kimi K2.6 API
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 supports text and image inputs, a 262K-token context window, thinking mode, preserve-thinking workflows, function calling, and structured outputs. It is especially strong for complex end-to-end coding tasks across Python, Rust, Go, front-end engineering, DevOps, performance optimization, and agentic workflow automation.
Entrada
$0.95 /M
Salida
$4 /M
Contexto
262K
Salida máx.
262K
Visión
Compatible
Uso de herramientas
Compatible
Prueba Kimi K2.6 en WaveSpeedAI
Accede a Kimi K2.6 mediante nuestra API unificada — compatible con OpenAI, sin arranques en frío, precios transparentes.
Preguntas frecuentes sobre Kimi K2.6
¿Cuánto cuesta Kimi K2.6 a través de la API?+
Precios en WaveSpeedAI: $0.95 por millón de tokens de entrada y $4.00 por millón de tokens de salida. El prompt caching y el procesamiento por lotes se facturan por separado y reducen el coste efectivo en cargas largas y repetitivas.
¿Cuál es la ventana de contexto de Kimi K2.6?+
Kimi K2.6 admite hasta 262K tokens de contexto y hasta 262K tokens de salida por solicitud.
¿Es Kimi K2.6 compatible con OpenAI?+
WaveSpeedAI ofrece Kimi K2.6 en https://llm.wavespeed.ai/v1 mediante la interfaz Chat Completions compatible con OpenAI. En la mayoría de clientes del SDK de OpenAI basta con cambiar la URL base y la clave API; los campos opcionales dependen del modelo.
¿Cómo empiezo con Kimi K2.6?+
Inicia sesión en WaveSpeedAI, crea una clave API en Access Keys y envía una solicitud a https://llm.wavespeed.ai/v1/chat/completions con el id de modelo mostrado arriba. Consulta el catálogo actual para conocer disponibilidad, capacidades y precios.