DeepSeek Coder

DeepSeek-V4.1-Flash

DeepSeek

DeepSeek-V4.1-Flash is a natively multimodal MoE model trained from scratch on 45 trillion tokens. It uses a causal encoder-decoder layout, Compressed Sparse Attention 2, Engram conditional memory, and DSpark speculative decoding to reduce inference and KV-cache cost. Its backbone activates different parameter counts during prefill and decoding, while a separate 196B conditional-memory table is sparsely accessed. The model accepts images and text and generates text, supports million-token contexts and adjustable reasoning effort, and powers the official deepseek-flash API. MIT weights and model-specific inference code support self-hosting.

Tiny Aya Thinker

Cohere

Tiny Aya Thinker is Cohere Labs’ 3.35B multilingual reasoning research release, published with the Building Multilingual Bridges paper. L2-Thinker produces reasoning in the user’s language across 60 languages, while En-Thinker provides an English-reasoning comparison. The models build on a 32K Tiny Aya base checkpoint. Downloadable research weights and a public demonstration support multilingual reasoning experiments. The paper publication precedes the October 5 explanatory blog; that later blog is not the first release.

DeepSeek-V4-Flash-Vision-Exp

DeepSeek

DeepSeek-V4-Flash-Vision-Exp is the experimental vision-language specialization of the production V4 Flash model. It adds image understanding for agent tasks while retaining the text capabilities of V4 Flash, enabling workflows that inspect interfaces, diagrams, and other visual context. It was publicly accessible through a dedicated DeepSeek API model name before downloadable MIT weights were released. Following the September 10 V4.1 Flash launch, the experimental endpoint was retired and its alias redirected; the original checkpoint remains available for self-hosted research.