---
title: "deepseek-v4-pro-0813"
publisher: "deepseek-ai"
type: "endpoint"
updated: "2026-08-26T22:41:56.191Z"
description: "DeepSeek V4 scales to 1M-token context windows with efficient MoE architecture for coding tasks."
canonical: "https://build.nvidia.com/deepseek-ai/deepseek-v4-pro-0813"
---

# DeepSeek-V4-Pro-0813

## Description
DeepSeek-V4-Pro-0813 is DeepSeek-AI's official DeepSeek-V4-Pro release. It supersedes the preview model, adds a DSpark speculative-decoding module, and is designed for text generation, reasoning, coding, and agentic tool-use workflows.

*This model is ready for commercial or 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 [DeepSeek AI DeepSeek-V4-Pro-0813 Model Card](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813).

## 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: [MIT](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main/LICENSE).

## Deployment Geography:
Global

## Use Case:
**Use Case:** Text generation, reasoning, coding, and agentic tool-use workflows.

## Release Date:
**Build.NVIDIA.com:** 08/24/2026 via [link](https://build.nvidia.com/deepseek-ai/deepseek-v4-pro-0813)  
**Huggingface:** 08/13/2026 via [link](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813)

## Reference(s):
**References:**
- [DeepSeek-V4-Pro-0813 Model Page](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813)
- [DeepSeek-V4 Technical Report](https://arxiv.org/abs/2606.19348)
- [DeepSeek Chat Interface](https://chat.deepseek.com/)
- [vLLM Recipe for DeepSeek-V4-Pro](https://recipes.vllm.ai/deepseek-ai/DeepSeek-V4-Pro)
- [SGLang Cookbook for DeepSeek-V4](https://docs.sglang.io/cookbook/autoregressive/DeepSeek/DeepSeek-V4#hw=gb300&variant=flash-official&quant=fp4&strategy=low-latency&nodes=single)

## Model Architecture:
**Architecture Type:** Transformer  
**Network Architecture:** Sparse Mixture of Experts with hybrid Compressed Sparse Attention and Heavily Compressed Attention, Manifold-Constrained Hyper-Connections, and an attached DSpark speculative-decoding module  
**Total Parameters:** 1.65T  
**Active Parameters:** 49B  
**Vocabulary Size:** 129,280

### Input:
**Input Types:** Text  
**Input Formats:** String  
**Input Parameters:** One-Dimensional (1D)  
**Other Input Properties:** Supports multi-turn messages encoded in OpenAI-compatible format and low, high, and max reasoning-effort levels.  
**Input Context Length (ISL):** 1,000,000

### Output:
**Output Types:** Text  
**Output Format:** String  
**Output Parameters:** One-Dimensional (1D)  
**Other Output Properties:** Generates text completions and reasoning content.

__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:**
- **vLLM**
- **SGLang**

**Supported Hardware:**
- **NVIDIA Blackwell:** B200, GB300

**Preferred 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)
DeepSeek-V4-Pro-0813 v1.0

## Training, Testing, and Evaluation Datasets:

### Training Dataset
**Data Modality:** Text  
**Text Training Data Size:** More than 10 Trillion Tokens  
**Training Data Collection:** Undisclosed  
**Training Labeling:** Undisclosed  
**Training Properties:** The DeepSeek-V4 family was pretrained on more than 32 trillion diverse tokens and then post-trained for reasoning and agentic capabilities.

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

### Evaluation Dataset
**Evaluation Data Collection:** Hybrid: Automated, Manually-Collected  
**Evaluation Labeling:** Hybrid: Automated, Manually-Labeled  
**Evaluation Properties:** Evaluated on coding-agent, repository, cybersecurity, software-engineering, and tool-use benchmarks.

**Evaluation Benchmark Score:** DeepSeek-V4-Pro-0813 was evaluated across ten coding, repository, cybersecurity, software-engineering, and agentic tool-use benchmarks. Selected results include HLE with tools at 60.0, Terminal Bench 2.1 at 87.9, NL2Repo at 61.5, Cybergym at 83.3, DeepSWE at 62.7, and Toolathlon-Verified at 74.1.

<details>
<summary><b>View Detailed Benchmark Results</b></summary>

| Benchmark | DeepSeek-V4-Pro-0813 | DeepSeek-V4-Flash-0731 | DeepSeek-V4-Pro (Preview) | DeepSeek-V4-Flash (Preview) | GLM-5.2 | Kimi K3 | Opus-4.8 | Fable-5 (w/ fallback) |
| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| HLE (wo / w tools) | 42.7 / 60.0 | 37.8 / 51.5 | 37.7 / 48.2 | 34.8 / 45.1 | 40.5 / 54.7 | 43.5 / 56.0 | 49.8 / 57.9 | 53.3 / 63.0 |
| Terminal Bench 2.1 | 87.9 | 82.7 | 72.1 | 61.8 | 81.0 | 88.3 | 85.0 | 88.0 |
| NL2Repo | 61.5 | 54.2 | 38.5 | 39.4 | 48.9 | - | 69.7 | - |
| Cybergym | 83.3 | 76.7 | 52.7 | 38.7 | - | 80.0 | 78.3 | 83.1 |
| DeepSWE | 62.7 | 54.4 | 12.8 | 7.3 | 46.2 | 67.5 | 58.0 | 70.0 |
| Toolathlon-Verified | 74.1 | 70.3 | 55.9 | 49.7 | 59.9 | 76.5 | 76.2 | 77.9 |
| Agents' Last Exam | 25.7 | 25.2 | 16.5 | 15.8 | 23.8 | 27.6 | 25.7 | - |
| AutomationBench (Public) | 31.8 | 25.1 | 12.8 | 10.8 | 12.9 | 30.8 | 27.2 | 29.1 |
| DSBench-FullStack † | 71.1 | 68.7 | 41.8 | 37.0 | 61.8 | 73.7 | 71.6 | 77.2 |
| DSBench-Hard † | 67.2 | 59.6 | 31.1 | 25.8 | 54.5 | 63.0 | 71.7 | 68.3 |

</details>

**Evaluation Methodology Notes:**
- For the public code-agent benchmarks, DeepSeek-V4-Pro-0813 was evaluated with the minimal mode of DeepSeek Harness as the agent framework, using the max reasoning-effort level with `temperature = 1.0` and `top_p = 0.95`.
- DSBench-FullStack is an internal full-stack development test set; DSBench-Hard is an internal test set of difficult coding-agent problems.

## Inference
**Acceleration Engine:** vLLM, SGLang  
**Test Hardware:** NVIDIA Blackwell (B200)

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

## Specifications

- **Context Length:** 1,048,576 tokens
- **Parameters:** 1.65T
- **Input:** Text
- **Output:** Text

## Capabilities

- **Function Calling:** Supported
- **Structured Output:** Supported
- **Reasoning:** Supported

## Prototype

```python
from openai import OpenAI

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=,
extra_body={"chat_template_kwargs":{"thinking":False}},
stream=NaN
)

print(completion.choices[0].message.content)
```

```python
from langchain_nvidia_ai_endpoints import ChatNVIDIA

client = ChatNVIDIA(
model="",
api_key="$NVIDIA_API_KEY",
temperature=,
top_p=,
max_tokens=,
chat_template_kwargs={"thinking": False},
)

response = client.invoke([{"role":"user","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: ,
chat_template_kwargs: {"thinking":false},
stream: 
})

process.stdout.write(completion.choices[0]?.message?.content || '');

}

main();
```

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

payload=$(cat <<'JSON'
{
"model": "",
"messages": [{"role":"user","content":""}],
"temperature": ,
"top_p": ,
"max_tokens": ,
"chat_template_kwargs": {"thinking":false},
"stream": 
}
JSON
)

curl -sS \
--request POST \
--url "$invoke_url" \
--header "Authorization: Bearer $NVIDIA_API_KEY" \
--header "Content-Type: application/json" \
--data "$payload"
```