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📦 Hash-sum → f1fab531799ed1fb0d015e84fcf2976a | 📌 Updated on 2026-07-15
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DeepSeek-V4-Pro, a revolutionary breakthrough in sparse-attention architecture, has dramatically reduced compute costs while maintaining its ability to model long-range contexts. With a staggering parameter count exceeding 1.5 trillion weights, this model delivers superior multilingual capabilities and nuanced reasoning. The training dataset, meticulously curated from over 5 trillion tokens, encompasses code repositories, scientific papers, and diverse conversational sources. This comprehensive dataset has enabled the model to outperform earlier architectures by double-digit margins in various benchmarking tasks.
| Description | Value |
|---|---|
| Parameters | 1.5 Trillion Weights |
| Training Tokens | 5 Trillion Tokens |
| Context Length | 8 Kilobytes |
| FLOPs per Token | 2.3 × 10^12 Flops per Token |
| Metric | Value || — | — || Reasoning Accuracy | 92.5% || Coding Performance | 95.2% || Factual QA Correctness | 93.8% |
With its groundbreaking architecture and extensive training dataset, DeepSeek-V4-Pro is poised to revolutionize various applications, including but not limited to:* Conversational AI* Code Review and Analysis* Factual Knowledge Retrieval
DeepSeek-V4-Pro has set a new benchmark in sparse-attention architectures, offering unparalleled performance and efficiency. Its potential applications are vast and varied, making it an exciting development in the field of artificial intelligence.
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