MoonshotAI: Kimi K2.6

Name: Kimi K2.6 API
Brand: moonshotai
Price: 0.95 USD
Availability: InStock

Utilisation de l'API

Utilisez les exemples de code suivants pour intégrer notre API :

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)

Introduction au modèle

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

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.

Questions fréquentes sur Kimi K2.6

Combien coûte l'API Kimi K2.6 ?+

Tarification sur WaveSpeedAI : $0.95 par million de tokens d'entrée et $4.00 par million de tokens de sortie. Le prompt caching et le traitement par batch sont facturés séparément et réduisent le coût effectif sur les charges longues et répétitives.

Quelle est la fenêtre de contexte de Kimi K2.6 ?+

Kimi K2.6 prend en charge jusqu'à 262K tokens de contexte et jusqu'à 262K tokens de sortie par requête.

Kimi K2.6 est-il compatible avec OpenAI ?+

Oui. WaveSpeedAI expose Kimi K2.6 via un endpoint compatible OpenAI à https://llm.wavespeed.ai/v1. Pointez le SDK officiel d'OpenAI vers cette base URL avec votre clé API WaveSpeedAI — aucune autre modification de code requise.

Comment démarrer avec Kimi K2.6 ?+

Connectez-vous à WaveSpeedAI, créez une clé API dans Access Keys, puis envoyez une requête à https://llm.wavespeed.ai/v1/chat/completions avec l'id du modèle affiché ci-dessus. Les nouveaux comptes reçoivent des crédits gratuits pour évaluer Kimi K2.6.

Tarification

Essayer le modèle

Utilisation de l'API

Introduction au modèle