Meta’s newly open‑sourced 30‑billion‑parameter Muse Glimmer agent model runs on a single consumer‑grade GPU, outperforms Gemma‑4 and Qwen‑3.6 on multiple Agent benchmarks, uses a perception encoder for multimodal input, and fits into a 20 GB memory envelope through quantization and a lightweight drafter.
Kimi K2.6, an open-source 1-trillion-parameter MoE model, expands Agent capabilities with 256K context, multimodal inputs, and the ability to coordinate 300 sub-Agents over 4,000 steps, achieving top scores on benchmarks like Terminal-Bench 2.0, SWE-Bench Pro, and BrowseComp, while offering flexible deployment via vLLM, SGLang, and KTransformers.
Kimi's open‑source K2‑Thinking model, a 1‑trillion‑parameter agent with native INT4 quantization and 256k context, achieves SOTA performance on benchmarks like Humanity’s Last Exam, BrowseComp and SEAL‑0, outperforms GPT‑5 and Grok‑4, and demonstrates complex tool‑driven reasoning through real‑world examples.
