moonshotai/kimi-k2.6
Tanggal rilis: 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.
Harga
Bayar sesuai pemakaian
Tanpa biaya di muka, bayar hanya sesuai penggunaan
Coba model
Penggunaan API
Gunakan contoh kode berikut untuk integrasi dengan API kami:
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;
}Pengenalan Model
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
Info
Fitur yang Didukung
Panduan Akses 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.
Input
$0.95 /M
Output
$4 /M
Konteks
262K
Output Maks.
262K
Vision
Didukung
Penggunaan Tool
Didukung
Coba Kimi K2.6 di WaveSpeedAI
Akses Kimi K2.6 melalui API terpadu kami — kompatibel dengan OpenAI, tanpa cold start, harga transparan.
Pertanyaan Umum tentang Kimi K2.6
Berapa biaya Kimi K2.6 melalui API?+
Harga di WaveSpeedAI: $0.95 per juta token input dan $4.00 per juta token output. Prompt caching dan batch processing ditagih terpisah dan mengurangi biaya efektif pada beban kerja yang panjang dan berulang.
Berapa context window Kimi K2.6?+
Kimi K2.6 mendukung hingga 262K token konteks dengan hingga 262K token output per permintaan.
Apakah Kimi K2.6 kompatibel dengan OpenAI?+
WaveSpeedAI menyediakan Kimi K2.6 di https://llm.wavespeed.ai/v1 melalui antarmuka Chat Completions yang kompatibel dengan OpenAI. Sebagian besar klien OpenAI SDK cukup mengganti base URL dan API key; field opsional bergantung pada model.
Bagaimana memulai dengan Kimi K2.6?+
Masuk ke WaveSpeedAI, buat API key di Access Keys, lalu kirim permintaan ke https://llm.wavespeed.ai/v1/chat/completions dengan model id yang ditampilkan di atas. Lihat katalog model terbaru untuk ketersediaan, kemampuan, dan harga.