AI Briefing#open-weight-model
The latest AI and developer news about #open-weight-model, with the original source and a short summary.
The latest AI and developer news about #open-weight-model, with the original source and a short summary.
The 65B parameter model achieves a TerminalBench 2.1 score of 80.52, outperforming larger competitors like Reflection Beam 501B and Meta Muse Spark Open 816B.
Beam is a low-latency open-weight reasoning model with a 1M-token context window and Apache 2.0 license.
The open-weight reasoning model supports text and image input with a 1M-token context window for coding and agentic workflows.
The model features 42B active parameters per token, a 1M-token context window, and supports text, image, audio, and video inputs.
The model features 49 billion active parameters and will have its weights released by the end of October 2026.
Reflection AI has introduced Beam, a 501B-parameter open-weight MoE model trained on 10.5K NVIDIA GB300 GPUs, claiming high inference efficiency compared to larger models like GLM 5.2 and Kimi K3.
The MiMo-V2.6-Pro released by Xiaomi recorded higher intelligence scores compared to similar-sized open-weight models and features competitive pricing, though its output speed was found to be slower than average.
DeepSeek cut V4-Pro pricing by 75% and also lowered API cache-hit costs to one-tenth of the previous rate.