Research

DeepSeek-V4.1

❖ We present DeepSeek-V4 series, including two strong Mixture-of-Experts (MoE) language models — DeepSeek-V4.1-Flash with 552B parameters (8B / 16B activated) and DeepSeek-V4-Pro with 1.6T parameters (49B activated) — both supporting a context length of one million tokens.

K2 Horizon

❖ K2-Horizon-375B-A23B is the flagship of the K2 Horizon family: a sparse Mixture-of-Experts model that has 375B total parameters and activates 23B per token, with a 512K context window. We have released the checkpoints, data, and training code. With full visibility into training and evaluation, strong agentic capabilities, and a low cost per task, K2 Horizon gives researchers and builders a dependable foundation for long-context applications at every scale.

Hy4

❖ Hy4 preview is a new-generation Mixture-of-Experts (MoE) flagship model developed by the Tencent Hy Team. The model comprises 770B total parameters, of which 49B are activated per token. We scaled Hy4 on three fronts: model size, context length, and training data.

❖ GLM-5.3 is our latest flagship model for coding and long-horizon tasks. It marks a substantial leap in long-horizon task capability over its predecessor GLM-5.1 and delivers that capability on a solid 1M-token context. It is pure open with an MIT open-source license — no regional limits, technical access without borders.

Qwen3.8

❖ We are officially releasing Qwen 3.8-Max, the most capable model in the Qwen family to date. This also marks the first time we will open-source the weights of a Qwen-Max-class model. Built upon the architectural foundation of Qwen 3.5, Qwen 3.8-Max scales to 2.4 trillion parameters, delivering comprehensive improvements across coding, work, research, and long-horizon tasks.

❖ We introduce Kimi K3 — our most capable model. Kimi K3 is a 2.8T-parameter model built on our Kimi Delta Attention and Attention Residuals, with native vision capabilities and a 1-million-token context window. It is the world’s first open 3T-class model, designed for frontier intelligence across long-horizon coding, knowledge work, and reasoning.

Inkling

❖ Inkling is a general-purpose multimodal model that accepts text, image and audio inputs and generates text outputs. It is intended for use in English and other languages, and across multiple coding languages. Inkling is released with open weights to support research, fine-tuning and integration into third-party products by downstream developers.

LongCat-2.0

❖ We introduce LongCat-2.0, a large-scale MoE language model with 1.6 trillion total parameters and ~48 billion activated per token — a substantial step up from previous LongCat models, accompanied by several architectural improvements. Both the full training run and the large-scale deployment are built entirely on AI ASIC superpods.