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DeepSeek V4 Pro

DeepSeek V4 Pro is an open-weights large language model from DeepSeek-AI, released under an MIT license and publicly hosted on the DeepSeek-AI Hugging Face organization, with an NVIDIA NGC NIM container available for enterprise deployment. The newer 0813 checkpoint is presented as the official release that supersedes an earlier preview, reusing the preview model structure and attaching a DSpark speculative decoding module to accelerate inference. A linked technical report on arXiv (identifier 2606.19348) accompanies the release and documents the architecture and evaluation methodology behind the family.

The 0813 release emphasizes enhanced agentic capability and production-grade performance, with the model card reporting benchmark results that show it outperforming the V4-Pro preview on tasks such as HLE (with and without tools), Terminal-Bench 2.1, and NL2Repo. These gains position it as broadly competitive with strong proprietary systems on tool-assisted reasoning and code-oriented agent benchmarks, making it a practical fit for coding assistants, tool-using agents, and other production workflows where speculative decoding can meaningfully improve throughput.

Alibaba Token Plan (China)deepseek-v4-prodeepseek-thinking

Quick Info

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Provider
Alibaba Token Plan (China)
Model key
deepseek-v4-pro
Release date
Apr 24, 2026
Last updated
Apr 24, 2026
Knowledge cutoff
2025-05
Input modalities
Output modalities
Capabilities

Cost

A provider subscription or plan supersedes token-based pricing for this model.

Limits

Output tokens
384,000 tokens
Context window
1,000,000 tokens

Latest news about DeepSeek V4 Pro

Alibaba Token Plan (China)

Coverage

DeepSeek's official API changelog records a 2026-08-13 GA release of DeepSeek-V4-Pro, marketed as a major upgrade to agent capabilities with particular gains in production environments. Concrete benchmark figures reported include HLE without/with tools at 42.7/60.0, Terminal-Bench 2.1 at 87.9, NL2Repo at 61.5, Cybergym The same changelog entry adds that the API now natively supports the OpenAI Responses API format with one-click Codex configuration, and that V4-Pro (alongside V4-Flash) exposes three thinking-effort levels โ€” low, high, and max โ€” giving developers more control over reasoning depth. The API model identifier remains `dee

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