---
title: "kimi-k2-instruct-0905"
publisher: "moonshotai"
type: "endpoint"
updated: "2025-09-19T16:08:06.098Z"
description: "Follow-on version of Kimi-K2-Instruct with longer context window and enhanced reasoning capabilities."
canonical: "https://build.nvidia.com/moonshotai/kimi-k2-instruct-0905"
---

# Kimi-K2-Instruct-0905

## Description
Kimi-K2-Instruct-0905 is the latest, most capable version of Kimi K2, a state-of-the-art Mixture-of-Experts (MoE) language model with 1 trillion total parameters and 32 billion active parameters. It delivers enhanced agentic coding intelligence, improved frontend coding experience, and supports extended context lengths of 256k tokens, enabling long-horizon tasks, tool calling, and chat completion.

*This model is ready for 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-Instruct-0905 Model Card](https://huggingface.co/moonshotai/Kimi-K2-Instruct-0905).

## License and Terms of Use:
**GOVERNING TERMS:** The 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 License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/). Additional Information: [Modified MIT License](https://huggingface.co/moonshotai/Kimi-K2-Instruct-0905/blob/main/LICENSE).

## Deployment Geography:
**Deployment Geography:** Global

## Use Case:
- Agentic coding intelligence
- Real-world coding agent tasks
- Frontend programming
- Long-horizon tasks
- Tool calling
- Chat completion
- General language understanding and generation

## Release Date:
**Build.NVIDIA.com 09/22/2025 via [link](https://build.nvidia.com/moonshotai/kimi-k2-instruct-0905)**  
**Huggingface 09/05/2024 via [link](https://huggingface.co/moonshotai/Kimi-K2-Instruct-0905)**

## Reference(s):
**References:** 
* [Moonshot AI Platform](https://platform.moonshot.ai)
* [Kimi K2 Technical Documentation](https://huggingface.co/moonshotai/Kimi-K2-Instruct-0905)

## Model Architecture:
**Architecture Type:** Mixture-of-Experts (MoE) language model  
**Network Architecture:** Transformer-based with MLA attention mechanism  
**Total Parameters:** 1T (1 trillion)  
**Active Parameters:** 32B (32 billion)  
**Vocabulary Size:** 160K  
**Base Model:** Kimi K2

### Input:
**Input Types:** Text  
**Input Formats:** Natural language prompts, conversational messages, tool calling requests  
**Input Parameters:** [One-Dimensional (1D)]  
**Other Input Properties:** Max Input Tokens: 256K, Support for tool calling, chat completion, extended context processing  
**Input Context Length (ISL):** 256K tokens

### Output:
**Output Type:** Text  
**Output Format:** Natural language responses, structured tool calls, code generation  
**Output Parameters:** [One-Dimensional (1D)]  
**Other Output Properties:** Max Output Tokens: Configurable, Tool calling capabilities, Code generation, Conversational responses  
**Output Context Length (OSL):** Configurable based on remaining context

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 Engines:** SGLang

**Supported Hardware:** 

- NVIDIA Blackwell/ B200  

**Operating Systems:** Linux

## Model Version(s)
Kimi-K2-Instruct-0905

## Training, Testing, and Evaluation Datasets:

### Training Dataset
**Data Modality:** Text
**Training Data Collection:** Undisclosed <br>
**Training Labeling:** Undisclosed  <br>
**Training Properties:** Pre-trained on large-scale text corpora with mixture-of-experts architecture, enhanced for agentic coding intelligence and tool calling capabilities

### Testing Dataset
**Testing Data Collection:** Undisclosed  <br>
**Testing Labeling:** Undisclosed <br>
**Testing Properties:** Regular testing on coding benchmarks and agentic intelligence tasks

### Evaluation Dataset
**Evaluation Benchmark Score:** SWE-Bench verified: 69.2 ± 0.63, SWE-Bench Multilingual: 55.9 ± 0.72, Multi-SWE-Bench: 33.5 ± 0.28, Terminal-Bench: 44.5 ± 2.03, SWE-Dev: 66.6 ± 0.72  
**Evaluation Data Collection:** Undisclosed <br>
**Evaluation Labeling:** Undisclosed <br>
**Evaluation Properties:** Evaluated on coding benchmarks with mean ± std over five independent runs

## Inference
**Acceleration Engine:** SGLang <br>
**Test Hardware:** H100

## Additional Details
Key features include:
- Enhanced agentic coding intelligence with significant improvements on public benchmarks
- Improved frontend coding experience with advancements in aesthetics and practicality
- Extended context length from 128k to 256k tokens for better long-horizon task support
- Strong tool-calling capabilities with autonomous tool invocation
- Mixture-of-experts architecture with 384 experts and 8 selected experts per token
- MLA attention mechanism with SwiGLU activation function
- Block-fp8 format for efficient storage and deployment
- OpenAI/Anthropic-compatible API available on Moonshot AI platform

## 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.

Please report model quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://app.intigriti.com/programs/nvidia/nvidiavdp/detail)

## Prototype

```python
from openai import OpenAI
import json

client = OpenAI(
base_url="https://integrate.api.nvidia.com/v1",
api_key="$NVIDIA_API_KEY"
)

completion = client.chat.completions.create(
model="",
messages=[{"role":"user","content":""}],
temperature=,
top_p=,
max_tokens=,
stream=False
)

message = completion.choices[0].message

if message.content:
print(message.content)
```

```python
from langchain_nvidia_ai_endpoints import ChatNVIDIA

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

response = client.invoke([{"role":"user","content":""}])
if response.additional_kwargs and "reasoning_content" in response.additional_kwargs:
print(response.additional_kwargs["reasoning_content"])
print(response.content)
```

```javascript
import OpenAI from 'openai';

const openai = new OpenAI({
apiKey: '$NVIDIA_API_KEY',
baseURL: 'https://integrate.api.nvidia.com/v1',
});

async function main() {
const completion = await openai.chat.completions.create({
model: "",
messages: [{"role":"user","content":""}],
temperature: ,
top_p: ,
max_tokens: ,
stream: false
});

const message = completion.choices[0]?.message;

if (message?.content) {
console.log(message.content);
}

}

main().catch(console.error);
```

```bash
curl 'https://integrate.api.nvidia.com/v1/chat/completions' \
-H 'Content-Type: application/json' \
-H "Authorization: Bearer $NVIDIA_API_KEY" \
-d @- <<'EOF'
{"model":"moonshotai/kimi-k2-instruct-0905","messages":[{"role":"user","content":""}]}
EOF
```