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

Laguna XS 2.1 is a 33B Mixture-of-Experts model with 3B activated parameters per token, tuned for agentic coding and long-horizon tasks on local machines. It outperforms XS.2 on SWE-bench Multilingual by 5.4 points, adds support for major runtimes, offers FP8/INT4/NVFP4 checkpoints, and runs with a permissive OpenMDW-1.1 license. You can access it via API, OpenRouter, or locally with quantized or DFlash-boosted builds.

• Laguna XS 2.1 jumps from 57.7% to 63.1% on SWE-bench Multilingual, beating Qwen 3.6 (35B) and North Mini Code (30B) despite only 3B active params per token
• DFlash speculator models double token throughput versus stock XS 2.1 when run locally
• Same API pricing as XS.2 ($0.10 input/$0.20 output/$0.05 cache-read per million tokens), now served at 256K context
• XS.2 leaves the API in one week, pushing users to migrate to 2.1 (though it remains available on Baseten's Model Library)