DeepSeek-V4-Flash introduces a 284B parameter MoE architecture with 13B activation, 1M context window support, and three distinct reasoning modes. Unsloth provides optimized GGUF quantizations with Q8 at 162GB.
DeepSeek-V4-Flash is a new 284B-parameter MoE model with 13B activated parameters, featuring hybrid attention architecture that reduces inference costs by 73% at million-token context lengths. Introduces three reasoning effort modes and achieves competitive performance with frontier models on coding and mathematical reasoning.
Unsloth has released optimized GGUF quantizations of DeepSeek-V4-Flash, a 284B parameter Mixture-of-Experts model that activates 13B parameters and supports 1 million token context windows. The Q8 quantization (UD-Q8_K_XL) runs at 162GB with claimed lossless precision, only 7GB larger than the Q4 variant.
DeepSeek has released V4 Flash, a Mixture-of-Experts model with 284B total parameters and 13B activated parameters per forward pass. The model supports a 1M-token context window and is available free through OpenRouter, targeting high-throughput coding and chat applications.
DeepSeek has released V4 Flash, a Mixture-of-Experts model with 284B total parameters and 13B activated parameters per request. The model supports a 1,048,576-token context window and is priced at $0.14 per million input tokens and $0.28 per million output tokens.
DeepSeek released two Mixture-of-Experts models: V4-Flash with 284B total parameters (13B activated) and V4-Pro with 1.6T parameters (49B activated). Both models support one million token context windows and use a hybrid attention architecture that requires only 27% of the inference FLOPs compared to DeepSeek-V3.2 at 1M token context.