open-weight-models

Chinese lab AllSpark has released Iris-mini and Iris-pro, two open-weight search agents built on Qwen3 models that claim the top spot among open-weight systems in their size classes on four research benchmarks. The release includes model weights, an agent harness, and evaluation code, with training pipelines to follow.

Zhipu AI has released GLM-5.3-Flash, the first natively multimodal model in its GLM-5 series, built on a 320B-parameter mixture-of-experts architecture with only 18B active parameters. The company claims it outperforms GLM-5.2 while approaching Claude Opus 4.8 on coding and agentic benchmarks at a fraction of the cost. Unsloth has published quantized GGUF versions for local inference.

Alibaba's Qwen research lab has released Qwen 3.8 27B, an Apache 2.0 licensed, vision-capable model with a 262,144-token context window. Independent testing found the model's default 'xhigh' reasoning setting causes it to massively overthink simple prompts, turning quick tasks into 20-minute ordeals.

Alibaba's Qwen team has released Qwen3.8-2.4T-A95B, a 2.4-trillion-parameter mixture-of-experts model activating 95B parameters per token. The company claims it is the first open-weight release to reach Qwen-Max-class performance, with a hosted variant, Qwen3.8-Max, offering 1M-token context and vision input.

Nvidia will invest $26 billion over the next five years to build open-weight AI models, according to a 2025 financial filing confirmed by executives. The move signals a strategic shift from chipmaker to AI frontier lab, with the company releasing Nemotron 3 Super (128B parameters) and claiming it outperforms GPT-OSS on multiple benchmarks.

An SEC filing reveals Nvidia plans to spend $26 billion on open-weight AI models over the next five years. The investment targets the open-source gap left by OpenAI, Meta, and Anthropic while countering the rise of Chinese open-source models and deepening developer dependence on Nvidia hardware.