DeepSeek-V4 introduces two advanced MoE language models, DeepSeek-V4-Pro and DeepSeek-V4-Flash, featuring up to 1.6T and 284B parameters respectively, both capable of processing one million tokens efficiently.
With significant architectural upgrades and a new Muon optimizer, these models achieve state-of-the-art performance in long-context tasks while drastically reducing computational costs compared to their predecessor, DeepSeek-V3.2.
We present a preview version of DeepSeek-V4 series, including two strong Mixture-of-Experts (MoE) language models -- DeepSeek-V4-Pro with 1. 6T parameters (49B activated) and DeepSeek-V4-Flash with 284B parameters (13B activated) -- both supporting a context length of one million tokens. DeepSeek-V4 series incorporate several key upgrades in architecture and optimization: (1) a hybrid attention architecture that combines Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA) to
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TriAgent introduces a cost-efficient multi-agent system for financial sentiment analysis, combining VADER, FinBERT, and Qwen2.5. It achieves an F1 score of ~0.87 with significant savings of $9.3M/year at a 10M-user scale compared to GPT-4o-mini, while also detecting hallucinations with an AUC of 0.90.
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