Cohere released North Small Translate, a sparse mixture-of-experts model with 25 billion active parameters designed for machine translation across 50 languages. The model scores 83.60 on WMT26 benchmarks and is available on Hugging Face under a non-commercial license.

• The model uses a sparse MoE architecture with 218B total parameters but only activates 25B per token, reducing computational overhead compared to dense models of similar capability
• It supports 50 languages including major ones like English, Spanish, Mandarin, and Hindi, plus less common ones like Filipino and Maltese
• Multiple quantization options (BF16, FP8, NVFP4) let you run it on different hardware—from 8 H100s down to 2 H100s depending on precision, with production deployment using greedy decoding

The article introduces Inkling-Small, a Mixture-of-Experts transformer with 276 B total and 12 B active parameters that matches full Inkling performance at one-quarter the size. It handles text, images, and audio with a 1 M-token context window and lets users trade compute for thinking effort. The open-weight model outperforms peers on tool use, coding, reasoning, and multimodal benchmarks and is available for fine-tuning and chat.

• Inkling-Small matches or beats full Inkling performance (e.g., 31.6% vs 29.7% on Humanity's Last Exam) while using only 12B active params out of 276B total—roughly a quarter the compute cost.
• It's natively multimodal (text, images via 40×40 patches, audio via dMel spectrograms) with a 1M-token context window and adjustable "thinking effort" for cost/performance tradeoffs.
• Safety numbers slightly lag full Inkling (71.6% vs 78.0% harmful-query refusal on FORTRESS) despite reusing the same safety recipe.
• Open-weight and available now for fine-tuning on Tinker and chat testing in the Tinker Playground.