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Free access to DeepSeek-V3.1. Experience the intelligent model. DeepSeek, unravel the mystery of AGI with curiosity. Answer the essential question with long-termism.
We introduce an innovative methodology to distill reasoning capabilities from the long-Chain-of-Thought (CoT) model, specifically from one of the DeepSeek R1 series models, into standard LLMs, particularly DeepSeek-V3.
DeepSeek-V3.1 is the latest open source large language model released by DeepSeek on August 19, 2025, featuring incremental upgrades based on the original V3, with main improvements including expanded context window and enhanced reasoning capabilities.
Comprehensive evaluations reveal that DeepSeek-V3 outperforms other open-source models and achieves performance comparable to leading closed-source models. Despite its excellent performance, DeepSeek-V3 requires only 2.788M H800 GPU hours for its full training.
DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in DeepSeek-V2. Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free strategy for load balancing and sets a multi-token prediction training objective for stronger performance.
Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free strategy for load balancing and sets a multi-token prediction training objective for stronger performance. We pre-train DeepSeek-V3 on 14.8 trillion diverse and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to fully harness its capabilities.
In this article, we’ll explore why DeepSeek V3 is generating so much buzz, how it’s reshaping the open-source AI landscape, and what you need to know if you’re considering diving in.
DeepSeek-V3 betreedt nieuw terrein en implementeert een auxiliary-loss-free strategie voor load balancing en neemt een multi-token prediction objective aan, wat de algehele prestaties aanzienlijk verbetert.
Explore DeepSeek V3.1, the new 685B parameter open-source AI. This guide covers its hybrid model, and how to access it.
Model Architectures & Scale: DeepSeek‑V3 is a Mixture-of-Experts (MoE) Transformer with 671 billion total parameters (only ~37B “active” per token). It uses Multi‑Head Latent Attention and a novel multi-token prediction objective for efficiency.