DeepSeek has officially released the DeepSeek-V4.1-Flash model, which serves as the smallest member of its new architecture family and features native multimodal visual understanding. This new architecture is designed to provide a higher capability ceiling, faster inference speeds, higher throughput, and better scaling potential for larger models.
DeepSeek has released V4.1-Flash, a new open-weight flagship model designed for extreme inference efficiency and low cost. The model utilizes a causal encoder-decoder architecture to significantly reduce active compute and KV/cache costs.
DeepSeek has launched DeepSeek v4.1-Flash, a new open-weight flagship model featuring a novel causal encoder-decoder architecture designed to maximize inference efficiency and reduce costs.
DeepSeek AI has released DeepSeek-V4.1-Flash, a multimodal Mixture-of-Experts model featuring a 1-million-token context window and an FP4 KV cache that reduces global cache footprint to 890 bytes per token. The model utilizes a causal encoder-decoder architecture and Compressed Sparse Attention 2 (CSA2) to significantly lower memory requirements while maintaining performance.
Chinese regulators are investigating DeepSeek and Moonshot AI regarding potential data leaks to the US-based company Anthropic. The probe follows rumors concerning Kimi executives, though the situation is currently described as an investigation rather than confirmed arrests.
DeepSeek is currently training a new model with 2 trillion parameters, with plans to eventually build an 8 trillion parameter model.
Researchers introduce CodeMidas, an agentic pipeline that constructs reinforcement learning environments from implemented functionality in open-source codebases, using source code as the sole input. The method allocates agentic compute to explore functionality, construct execution-grounded tests, and validate tasks through repeated rollouts, resulting in a dataset of 5,545 training tasks across 23 programming languages. Training MiMo-V2.5 on these tasks with GRPO improves performance on five diverse benchmarks, including DeepSWE (+11.7%), ProgramBench (+17%), and Terminal-Bench v2.1 (+8.5%). Trajectory analysis indicates the RL-trained agent exhibits better behaviors such as increased codebase exploration and more diverse self-verification. These results establish source code as a scalable foundation for constructing RL environments that improve coding agents across diverse software tasks.
DeepSeek has released DeepSeek-V4.1-Flash, a multimodal Mixture-of-Experts model designed to address the high costs of long-context computation and large KV caches in agentic workloads.
Atria Dawn Preview is a foundation agentic language model designed for scientific research and engineering workflows, trained via a Verifiable Experience Pipeline that connects tool-mediated interactions to executable environments. Across 16 benchmarks spanning real-world research, engineering, and digital work, the model is competitive with frontier agents and achieves the highest reported score on five of them.
DeepSeek V4.1 Flash has taken first place on the new Astra benchmark within the Artificial Analysis Intelligence Index v4.3 update.
A user has developed a custom implementation that allows the DeepSeek V4.1 model to run on NVIDIA A100 GPUs with performance exceeding the official API.
The LiveBench benchmarking platform has added the DeepSeek v4.1 Flash model to its evaluation suite.
The DeepSeek V4.1 Flash model is now accessible through the HuggingChat platform.
A user has published a video generated using the newly released DeepSeek V4.1 Flash model to benchmark its capabilities in motion video synthesis.
DeepSeek has released its new model, DeepSeek V4-1 Flash. It is a multimodal Mixture-of-Experts (MoE) model featuring a 552 billion parameter backbone.
DeepSeek has officially released the DeepSeek V4.1 Flash model, the smallest entry in its new architecture series featuring native multimodal visual understanding. Built on a Causal-Encoder-Decoder design, it is a 552B-parameter Mixture of Experts (MoE) model that activates only 8B parameters on the input side and 16B on the output side.
DeepSeek has soft retired the DeepSeek V4 Pro model. This action marks the end of availability for this specific version of their AI system.
DeepSeek has opened internal beta testing for an intermediate version of its DeepSeek V4.1 Flash model, which is now available via API.
DeepSeek has published the DeepSeek-V4-Flash-Vision-Exp model checkpoint on Hugging Face. The repository is now available for public access.
A Reddit post titled "Qwen3.8-Flash-Next better then DeepSeek V4 Pro" has been submitted by user /u/Normal-Phone7762 to the r/LocalLLaMA community.