moonshotai/kimi-k2.6
Veröffentlichungsdatum: 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.
Preise
Pay-per-Use
Keine Vorabkosten, zahlen Sie nur, was Sie nutzen
Modell ausprobieren
API-Nutzung
Verwenden Sie die folgenden Codebeispiele zur Integration mit unserer 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;
}Modelleinführung
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
Unterstützte Funktionen
API-Zugriffsanleitung
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.
Eingabe
$0.95 /M
Ausgabe
$4 /M
Kontext
262K
Max. Ausgabe
262K
Vision
Unterstützt
Tool-Nutzung
Unterstützt
Kimi K2.6 auf WaveSpeedAI testen
Zugriff auf Kimi K2.6 über unsere einheitliche API — OpenAI-kompatibel, keine Kaltstarts, transparente Preise.
Häufige Fragen zu Kimi K2.6
Wie viel kostet die Kimi K2.6-API?+
Preise auf WaveSpeedAI: $0.95 pro Million Input-Tokens und $4.00 pro Million Output-Tokens. Prompt-Caching und Batch-Verarbeitung werden separat berechnet und reduzieren die effektiven Kosten bei langen, sich wiederholenden Workloads.
Wie groß ist das Kontextfenster von Kimi K2.6?+
Kimi K2.6 unterstützt bis zu 262K Kontext-Tokens und bis zu 262K Output-Tokens pro Anfrage.
Ist Kimi K2.6 OpenAI-kompatibel?+
WaveSpeedAI stellt Kimi K2.6 unter https://llm.wavespeed.ai/v1 über die OpenAI-kompatible Chat-Completions-Schnittstelle bereit. Bei den meisten OpenAI-SDK-Clients reichen Base-URL und API-Schlüssel; optionale Felder hängen vom Modell ab.
Wie starte ich mit Kimi K2.6?+
Melden Sie sich bei WaveSpeedAI an, erstellen Sie unter Access Keys einen API-Schlüssel und senden Sie eine Anfrage mit der oben gezeigten Modell-ID an https://llm.wavespeed.ai/v1/chat/completions. Verfügbarkeit, Fähigkeiten und Preise finden Sie im aktuellen Modellkatalog.