On May 20, Cohere released Command A+ — its first fully open-source-weight frontier-tier model: 218B total parameters, a sparse MoE architecture activating only 25B per token, Apache 2.0 licensed, open-sourced on Hugging Face, with FP8 and W4A4 quantization versions. The recommended setup is just two H100 GPUs. Compared to the previous-generation Command A Reasoning, throughput improves by roughly 63% — under W4A4 quantization (4-bit for expert parameters, near-lossless compression with the attention path kept at high precision), it reaches about 375 output tokens per second, with 113ms first-token latency. MoE speculative decoding adds another 1.5-1.6× speedup.
On benchmarks, Command A+ achieves qualitative leaps across multiple tasks: the agent task τ²-Bench jumps from 37% to 85%; agent coding Terminal-Bench Hard climbs from 3% to 25%; multimodal reasoning MMMU hits 75.1% and MathVista 80.6%. Notably, the model's training data cutoff is June 2024 — it does not rely on larger frontier-scale data, yet achieves significant gains, indicating that the optimization space in architecture and training efficiency remains vast.
Open-source weights, private deployment, and W4A4 quantization make a 200B+ model available on enterprise-grade hardware for the first time — significant for industries with data-sovereignty requirements like finance and healthcare. Command A+ represents an important step for open-source large models in 2026: no longer chasing parameter stacking, but using sparse MoE architecture and extreme quantization to make powerful models truly accessible. The MoE + quantization combo is rewriting the cost structure of large models; the era of small teams running SOTA models is arriving faster than expected.