DeepSeek released V4.1-Flash, introducing a new Causal Encoder-Decoder architecture with native visual understanding capabilities, six weeks after its July V4-Flash update which focused on post-training improvements.
DeepSeek released V4.1-Flash, a new AI model that significantly reduces memory requirements for AI agents by shrinking the KV cache to about a quarter of its predecessor's size. The model uses 552 billion parameters and employs techniques like splitting the architecture into encoder and decoder components to halve compute needs for input processing. Performance matches leading models on coding tasks, though weaknesses remain in scientific reasoning and image analysis.
DeepSeek AI released DeepSeek-V4.1-Flash, a multimodal mixture-of-experts model with 1 million token context window, featuring 552B main parameters plus 196B Engram parameters. The model uses FP4 KV cache and cross-layer attention reuse to reduce memory consumption to 890 bytes per token, approximately 1/4 of DeepSeek-V4-Flash and 1/437 of DeepSeek-V1.
DeepSeek released the V4.1 Flash model, a 552B parameter MoE model with native multimodal vision capabilities that outperforms V4 Pro across benchmarks while significantly reducing API pricing. The model features a new Causal-Encoder-Decoder architecture with dramatically reduced KV Cache requirements and improved inference speed, with V4 Pro being phased out in favor of the new model.
DeepSeek released V4.1 Flash, a 552B parameter mixture-of-experts multimodal model with a novel Causal-Encoder-Decoder architecture. The model achieves superior benchmark performance compared to flagship models including DeepSeek V4 Pro, with native visual understanding capabilities.
OpenAI released GPT-6 Astra, the next generation AI model for professional work scenarios. The model is available through ChatGPT Work, Codex, and API with pricing of $10 per million input tokens and $50 per million output tokens.
NYU mathematics professor Tristan Buckmaster accused OpenAI of using his unpublished research on the Navier-Stokes existence and smoothness problem to achieve a complete proof before his own work was released. Buckmaster and Anthropic mathematician Levent Alpöge had made initial progress on one of mathematics' seven Millennium Prize Problems, but OpenAI claimed to have solved it completely using a new model after learning of their approach, raising questions about whether OpenAI leveraged computational resources to race ahead.