IBM has released Granite 4.1, a family of open-source language models designed for enterprise use, featuring three sizes and trained on 15 trillion tokens. The 8B model utilizes a dense architecture without mixture of experts (MoE) techniques and outperforms Granite 4.0-H-Small across various benchmarks.

The Kimi Vendor Verifier (KVV) project has been open-sourced alongside the Kimi K2.6 model to assist users in verifying the accuracy of their inference implementations. KVV aims to ensure that open-source models run correctly across different environments.

Duplicating a block of seven middle layers in Qwen2-72B without weight changes or training produced a top model on the HuggingFace Open LLM Leaderboard. Since mid-2024, several strong open-source models have emerged, including Qwen3.5, MiniMax, and GLM-4.

Sarvam 30B and Sarvam 105B are open-source reasoning models trained from scratch on large-scale, high-quality datasets. The training was conducted in India under the IndiaAI mission, optimizing various aspects including tokenization, model architecture, and execution kernels.