moonshotai/kimi-k2.6
प्रकाशन तिथि: 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.
मूल्य निर्धारण
उपयोग के अनुसार भुगतान
कोई अग्रिम लागत नहीं, केवल उतना ही भुगतान करें जितना आप उपयोग करते हैं
मॉडल आज़माएं
API उपयोग
हमारे API के साथ एकीकृत करने के लिए निम्नलिखित कोड उदाहरणों का उपयोग करें:
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;
}मॉडल परिचय
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
जानकारी
समर्थित कार्यक्षमता
API एक्सेस गाइड
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.
इनपुट
$0.95 /M
आउटपुट
$4 /M
कॉन्टेक्स्ट
262K
अधिकतम आउटपुट
262K
विज़न
समर्थित
टूल उपयोग
समर्थित
WaveSpeedAI पर Kimi K2.6 आज़माएं
हमारे एकीकृत API के ज़रिए Kimi K2.6 तक पहुंचें — OpenAI-कम्पैटिबल, कोई कोल्ड स्टार्ट नहीं, पारदर्शी मूल्य निर्धारण।
Kimi K2.6 के बारे में अक्सर पूछे जाने वाले सवाल
API के ज़रिए Kimi K2.6 की लागत कितनी है?+
WaveSpeedAI पर मूल्य निर्धारण: प्रति मिलियन इनपुट टोकन $0.95 और प्रति मिलियन आउटपुट टोकन $4.00। प्रॉम्प्ट कैशिंग और बैच प्रोसेसिंग की बिलिंग अलग से होती है और लंबे, दोहराव वाले वर्कलोड पर प्रभावी लागत कम करती है।
Kimi K2.6 की कॉन्टेक्स्ट विंडो क्या है?+
Kimi K2.6 प्रति अनुरोध 262K टोकन तक के आउटपुट के साथ 262K टोकन तक के कॉन्टेक्स्ट को सपोर्ट करता है।
क्या Kimi K2.6 OpenAI-कम्पैटिबल है?+
WaveSpeedAI Kimi K2.6 को https://llm.wavespeed.ai/v1 पर OpenAI-कम्पैटिबल Chat Completions इंटरफ़ेस के ज़रिए उपलब्ध कराता है। अधिकांश OpenAI SDK क्लाइंट बेस URL और API कुंजी बदलकर काम करते हैं; वैकल्पिक फ़ील्ड चयनित मॉडल पर निर्भर करते हैं।
मैं Kimi K2.6 के साथ कैसे शुरू करूं?+
WaveSpeedAI में साइन इन करें, Access Keys में एक API कुंजी बनाएं, फिर ऊपर दिखाए गए मॉडल id के साथ https://llm.wavespeed.ai/v1/chat/completions पर एक अनुरोध भेजें। उपलब्धता, क्षमताओं और मूल्य निर्धारण के लिए वर्तमान मॉडल कैटलॉग देखें।