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We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token. To achieve efficient inference and cost-effective 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. 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. 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. In addition, its training process is remarkably stable. Throughout the entire training process, we did not experience any irrecoverable loss spikes or perform any rollbacks. **NOTE: The total size of DeepSeek-V3 models on HuggingFace is 685B, which includes 671B of the Main Model weights and 14B of the Multi-Token Prediction (MTP) Module weights.** To ensure optimal performance and flexibility, we have partnered with open-source communities and hardware vendors to provide multiple ways to run the model locally. For step-by-step guidance, check out Section 6: How_to Run_Locally.

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DeepSeek V4.1 Flash is a sparse mixture-of-experts model from DeepSeek, and the first built on the company's Causal Encoder-Decoder (CED) architecture. It activates 8B parameters on input and 16B on...

Text generationOpen weights
Input Text · Image→ Output Text
$0.045 / M input tokensOpenRouter · deepseek/deepseek-v4.1-flash
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DeepSeek V4.1 Flash is a sparse mixture-of-experts model from DeepSeek, and the first built on the company's Causal Encoder-Decoder (CED) architecture. It activates 8B parameters on input and 16B on...

Text generationOpen weights
Input Text · Image→ Output Text
$0.05 / M input tokensOpenRouter · deepseek/deepseek-v4.1-flash:batch
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DeepSeek V4 Flash is an efficiency-optimized Mixture-of-Experts model from DeepSeek with 284B total parameters and 13B activated parameters, supporting a 1M-token context window. It is designed for fast inference and...

Text generationOpen weights
Input Text→ Output Text
$0.091 / M input tokensOpenRouter · deepseek/deepseek-v4-flash
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DeepSeek V4 Flash 0731 is a sparse mixture-of-experts model from DeepSeek, with 13B active parameters out of 284B total. This re-post-trained revision is suited for coding, reasoning, and agent workflows....

Text generationOpen weights
Input Text→ Output Text
$0.119 / M input tokensOpenRouter · deepseek/deepseek-v4-flash-0731
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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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**Note: DeepSeek-V4-Flash-DSpark** is **not** a new model. It is the same checkpoint with an additional speculative decoding module attached. A minimal inference example is available in the `inference` folder. For more details, refer to: https://github.com/deepseek-ai/DeepSpec 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:

Text generation
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DeepSeek V4 Flash Vision Exp is an experimental vision-enabled version of DeepSeek V4 Flash 0731 from DeepSeek, adding image understanding while matching the base model on text capabilities including agents,...

Text generationOpen weights
Input Text · Image→ Output Text
$0.2156 / M input tokensOpenRouter · deepseek/deepseek-v4-flash-vision-exp
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DeepSeek V4 Pro is a large-scale Mixture-of-Experts model from DeepSeek with 1.6T total parameters and 49B activated parameters, supporting a 1M-token context window. It is designed for advanced reasoning, coding,...

Text generationOpen weights
Input Text→ Output Text
$1.6 / M input tokensOpenRouter · deepseek/deepseek-v4-pro
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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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