# DeepSeek V4 Pro 0813 URL: https://interfaze.ai/models/deepseek-aideepseek-v4-pro-0813 [All models](https://interfaze.ai/models) DeepSeek V4 Pro 0813 by deepseek-ai, a text-generation model. Understand and compare features, benchmarks, and capabilities. ## Comparison | Feature | DeepSeek V4 Pro 0813 | Interfaze | | --- | --- | --- | | Input Modalities | text | image, text, audio, video, document | | Native OCR | No | Yes | | Long Document Processing | Yes | Yes | | Language Support | 30 partial | 162+ | | Native Speech-to-Text | No | Yes | | Native Object Detection | No | Yes | | Guardrail Controls | No | Yes | | Context Input Size | 1M | 1M | | Tool Calling | Yes | Tool calling supported + built in browser, code execution and web search | ### Scaling | Feature | DeepSeek V4 Pro 0813 | Interfaze | | --- | --- | --- | | Scaling | Self-hosted/Provider-hosted with quantization | Unlimited | [Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs) View model card on [Hugging Face](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813) ## Introduction **DeepSeek-V4-Pro-0813** is the official release of **DeepSeek-V4-Pro**, superseding the preview version, with greatly enhanced agentic capabilities and performance improvements that are especially pronounced in production environments. It is built on the DeepSeek-V4-Pro (Preview) model structure, with a DSpark speculative decoding module attached. DeepSeek-V4-Pro-0813 outperforms DeepSeek-V4-Pro (Preview) on the benchmarks listed below, and is broadly competitive with the strongest proprietary models available. | 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 | Notes: 1. For the code-agent tasks among the public benchmarks above, DeepSeek-V4-Pro-0813 is evaluated with the minimal mode of DeepSeek Harness as the agent framework, using the `max` reasoning effort level with `temperature = 1.0, top_p = 0.95`. 2. † DSBench-FullStack is an internal full-stack development test set; DSBench-Hard is an internal test set of difficult coding-agent problems. ## Chat Template This release does not include a Jinja-format chat template. Instead, we provide a dedicated `encoding` folder with Python scripts and test cases demonstrating how to encode messages in OpenAI-compatible format into input strings for the model, and how to parse the model's text output. Please refer to the [`encoding`](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813/blob/main/encoding/README.md) folder for full documentation. The `reasoning_effort` parameter now supports three levels — `low`, `high`, and `max` — which control how much deliberation the model spends before answering. A brief example: ``` from encoding_dsv4 import encode_messages, parse_message_from_completion_text messages = [ {"role": "user", "content": "hello"}, {"role": "assistant", "content": "Hello! I am DeepSeek.", "reasoning_content": "thinking..."}, {"role": "user", "content": "1+1=?"} ] prompt = encode_messages(messages, thinking_mode="thinking", reasoning_effort="max") import transformers tokenizer = transformers.AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-V4-Pro-0813") tokens = tokenizer.encode(prompt) ``` ## How to Run with vLLM DSpark speculative decoding is enabled with a single flag — add --speculative-config with method: dspark to your vLLM launch command: `--speculative-config '{"method":"dspark","num_speculative_tokens":7,"draft_sample_method":"greedy"}'` For example, the command below serves the model with vLLM on a single 4×GB300 node. See the [vLLM recipe](https://recipes.vllm.ai/deepseek-ai/DeepSeek-V4-Pro) for detailed instructions and other hardware configurations. ``` vllm serve deepseek-ai/DeepSeek-V4-Pro-0813 \ --trust-remote-code --kv-cache-dtype fp8 --block-size 256 \ --data-parallel-size 4 --enable-expert-parallel \ --moe-backend deep_gemm_mega_moe \ --attention-config '{"use_fp4_indexer_cache": true}' \ --speculative-config '{"method":"dspark","num_speculative_tokens":7,"draft_sample_method":"greedy"}' ``` ## How to Run with SGLang Enable DSpark with `--speculative-algorithm DSPARK` and do not set a separate `--speculative-draft-model-path` as the target and draft weights therefore come from the same checkpoint. See the [SGLang cookbook](https://docs.sglang.io/cookbook/autoregressive/DeepSeek/DeepSeek-V4#hw=gb300&variant=flash-official&quant=fp4&strategy=low-latency&nodes=single) for detailed instructions, benchmarks and other hardwares configurations. ``` sglang serve \ --trust-remote-code \ --model-path deepseek-ai/DeepSeek-V4-Pro-0813 \ --tp 4 \ --moe-runner-backend flashinfer_mxfp4 \ --speculative-algorithm DSPARK \ --mem-fraction-static 0.90 \ --chunked-prefill-size 4096 \ --swa-full-tokens-ratio 0.1 \ ``` ## How to Run Locally Please refer to the [inference](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813/blob/main/inference/README.md) folder for detailed instructions on running DeepSeek-V4 locally, including model weight conversion and interactive chat demos. For local deployment, we recommend setting the sampling parameters to `temperature = 1.0`, with `top_p = 0.95` for agentic scenarios and `top_p = 1.0` otherwise. For the `high` and `max` reasoning effort levels, we recommend a maximum output length of **384K** tokens. ## License This repository and the model weights are licensed under the [MIT License](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813/blob/main/LICENSE). ## Citation `@misc{deepseekai2026deepseekv4, title={DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence}, author={DeepSeek-AI}, year={2026}, }` ## Contact If you have any questions, please raise an issue or contact us at [service@deepseek.com](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813/blob/main/service@deepseek.com). ## Want more deterministic results? [Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)