---
title: "kimi-k2.6"
publisher: "moonshotai"
type: "endpoint"
updated: "2026-05-01T21:33:42.644Z"
description: "1T multimodal MoE for long-horizon coding, agentic tool use, and image/video understanding."
canonical: "https://build.nvidia.com/moonshotai/kimi-k2.6"
---

# Kimi-K2.6

## Description
Kimi-K2.6 is an open-source native multimodal agentic model developed by Moonshot AI. Built on a Mixture-of-Experts (MoE) architecture with 1 trillion total parameters (32B active), it delivers long-horizon coding capabilities across Rust, Go, Python, frontend, and DevOps workflows. The model supports agentic task orchestration scaling to 300 sub-agents executing up to 4,000 coordinated steps, and accepts multimodal inputs including text, images, and video via the MoonViT (400M) vision encoder.

*This model is ready for commercial/non-commercial use.*

## Third-Party Community Consideration:
This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA [Kimi-K2.6 Model Card](https://huggingface.co/moonshotai/Kimi-K2.6)

## License and Terms of Use:
**GOVERNING TERMS:** This trial service is governed by the [NVIDIA API Trial Terms of Service](https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf). Use of this model is governed by the [NVIDIA Open Model Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-agreement/). Additional Information: [Modified MIT License](https://huggingface.co/moonshotai/Kimi-K2.6/blob/main/LICENSE). Kimi K2.6.

## Deployment Geography:
Global

## Use Case:
**Use Case:** Kimi-K2.6 is designed for developers and researchers requiring advanced multimodal agentic AI capabilities. Primary use cases include long-horizon coding workflows (frontend, backend, DevOps, performance optimization), autonomous agent orchestration with proactive background task execution, visual reasoning with image and video inputs, and complex multi-step problem-solving requiring hundreds of sequential tool invocations.

## Release Date:
**build.nvidia.com:** April 29, 2026 via [link](https://build.nvidia.com/moonshotai/kimi-k2.6)
**Hugging Face:** April 29, 2026 via [link](https://huggingface.co/moonshotai/Kimi-K2.6)

## Reference(s):
**References:**
- [Kimi-K2.6 on Hugging Face](https://huggingface.co/moonshotai/Kimi-K2.6)
- [Kimi K2.6 Technical Report](https://arxiv.org/pdf/2602.02276)
- [Moonshot AI Platform](https://platform.moonshot.ai)
- [Modified MIT License](https://huggingface.co/moonshotai/Kimi-K2.6/blob/main/LICENSE)
- [Third Party Notices](https://huggingface.co/moonshotai/Kimi-K2.6/blob/main/THIRD_PARTY_NOTICES.md)

## Model Architecture:
**Architecture Type:** Transformer
**Network Architecture:** Mixture-of-Experts (MoE)
**Total Parameters:** 1T
**Active Parameters:** 32B
**Layers:** 61 (including 1 dense layer)
**Number of Experts:** 384
**Selected Experts per Token:** 8
**Shared Experts:** 1
**Attention Mechanism:** MLA (Multi-head Latent Attention)
**Attention Hidden Dimension:** 7168
**MoE Hidden Dimension per Expert:** 2048
**Attention Heads:** 64
**Vocabulary Size:** 160K
**Context Length:** 256K
**Activation Function:** SwiGLU
**Vision Encoder:** MoonViT (400M parameters)

### Input:
**Input Types:** Text, Image, Video
**Input Formats:** String, Image (JPEG/PNG), Video frames
**Input Parameters:** Text: One-Dimensional (1D); Image: Two-Dimensional (2D); Video: Three-Dimensional (3D)
**Other Input Properties:** Text is tokenized with a 160K-vocabulary tokenizer. Images and video frames are encoded via MoonViT (400M). Supports multi-turn conversations with system prompts, user messages, tool definitions in JSON schema format, and native tool-use orchestration.
**Input Context Length (ISL):** 256K tokens

### Output:
**Output Types:** Text
**Output Format:** String
**Output Parameters:** One Dimensional (1D)
**Other Output Properties:** Generated text can include structured tool call requests, agent coordination directives, and coding artifacts. Supports JSON-structured outputs for agentic workflows.

__Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.__

## Software Integration:
**Runtime Engine(s):** vLLM

**Supported Hardware Microarchitecture Compatibility:**
- NVIDIA Blackwell
- NVIDIA Hopper

**Operating Systems:** Linux

__The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.__

## Model Version(s)
Kimi-K2.6 (2026)

## Training, Testing, and Evaluation Datasets:

### Training Dataset
**Data Modality:** Text, Image, Video
**Text Training Data Size:** Undisclosed
**Training Data Collection:** Undisclosed
**Training Labeling:** Undisclosed
**Training Properties:** Undisclosed

### Testing Dataset
**Testing Data Collection:** Undisclosed
**Testing Labeling:** Undisclosed
**Testing Properties:** Undisclosed

### Evaluation Dataset
**Evaluation Benchmark Score:**

| Benchmark | Score |
|-----------|-------|
| **Agentic** | |
| HLE-Full w/ tools (Pass@1) | 54.0% |
| BrowseComp (Pass@1) | 83.2% |
| BrowseComp Agent Swarm (Pass@1) | 86.3% |
| SWE-Bench Pro (Resolved) | 58.6% |
| SWE-Bench Verified (Resolved) | 80.2% |
| SWE-Bench Multilingual (Resolved) | 76.7% |
| Terminal-Bench 2.0 (Acc) | 66.7% |
| OSWorld-Verified (Acc) | 73.1% |
| **Coding** | |
| LiveCodeBench v6 (Pass@1) | 89.6% |
| **Reasoning & Knowledge** | |
| AIME 2026 (Pass@1) | 96.4% |
| HMMT 2026 Feb (Pass@1) | 92.7% |
| GPQA Diamond (Pass@1) | 90.5% |
| IMO-AnswerBench (Pass@1) | 86.0% |
| **Vision** | |
| MMMU-Pro | 79.4% |
| MathVision | 87.4% |
| CharXiv Reasoning Questions | 80.4% |

**Evaluation Data Collection:** Automated
**Evaluation Labeling:** Human
**Evaluation Properties:** Evaluated on agentic task completion, coding, mathematical reasoning, and vision benchmarks.

## Inference
**Acceleration Engine(s):** vLLM
**Test Hardware:** GB200x4

## Additional Details

### Key Capabilities

**1. Long-Horizon Coding**
Supports production-level coding tasks in Rust, Go, Python, front-end frameworks, DevOps pipelines, and performance optimization. Transforms natural language prompts and visual mockups into production-ready code.

**2. Agentic Orchestration**
Scales to 300 parallel sub-agents executing up to 4,000 coordinated steps. Supports 24/7 background autonomous task execution with proactive orchestration.

**3. Multimodal Input**
Native support for text, images, and video inputs via MoonViT (400M vision encoder). Enables visual-to-code workflows and image-grounded reasoning.

**4. Open Orchestration**
Compatible with open agent frameworks. Supports function/tool calling with structured JSON schema definitions.

## Ethical Considerations
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.

Users are responsible for model inputs and outputs. Users are responsible for ensuring safe integration of this model, including implementing guardrails as well as other safety mechanisms, prior to deployment.

Please make sure you have proper rights and permissions for all input image and video content; if image or video includes people, personal health information, or intellectual property, the image or video generated will not blur or maintain proportions of image subjects included.

Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/)

## Capabilities

- **Function Calling:** Supported

## Prototype

```python
import requests

invoke_url = "https://integrate.api.nvidia.com/v1/chat/completions"

headers = {
"Authorization": "Bearer ",
"Accept": "application/json",
}

payload = {
"messages": [
{
"role": "user",
"content": ""
}
]
}

# re-use connections
session = requests.Session()

response = session.post(invoke_url, headers=headers, json=payload)

response.raise_for_status()
response_body = response.json()
print(response_body)
```

```python
from langchain_nvidia_ai_endpoints import ChatNVIDIA

client = ChatNVIDIA(
model="",
api_key="$NVIDIA_API_KEY",
temperature=,
top_p=,
max_completion_tokens=,
)

lc_messages = [{"role":"user","content":""}]

response = client.invoke(lc_messages)
if response.additional_kwargs and "reasoning_content" in response.additional_kwargs:
print(response.additional_kwargs["reasoning_content"])
print(response.content)
```

```javascript
import fetch from "node-fetch";

const invokeUrl = "https://integrate.api.nvidia.com/v1/chat/completions"

const headers = {
"Authorization": "Bearer ",
"Accept": "application/json",
}

const payload = {
"messages": [
{
"role": "user",
"content": ""
}
]
}

let response = await fetch(invokeUrl, {
method: "post",
body: JSON.stringify(payload),
headers: { "Content-Type": "application/json", ...headers }
});

let response_body = await response.json()

console.log(JSON.stringify(response_body))
```

```bash
invoke_url='https://integrate.api.nvidia.com/v1/chat/completions'

authorization_header='Authorization: Bearer '
accept_header='Accept: application/json'
content_type_header='Content-Type: application/json'

data=$'{
"messages": [
{
"role": "user",
"content": ""
}
]
}'

response=$(curl --silent -i -w "\n%{http_code}" --request POST \
--url "$invoke_url" \
--header "$authorization_header" \
--header "$accept_header" \
--header "$content_type_header" \
--data "$data"
)

echo "$response"
```