Cohere just made its first move into open-source coding models, and it's a meaningful one. North Mini Code is a 30-billion-parameter Mixture-of-Experts (MoE) model -- a type of architecture where only a fraction of the network is active at any time -- that activates just 3 billion parameters per token. The result is a model that punches above its weight class in both performance and speed, available today under an Apache 2.0 license.

This is Cohere's first open-source model aimed squarely at developers, and it signals a strategic shift: the company known for enterprise-grade closed APIs is now betting on community-driven development to shape its next generation of models.

What it's built for

North Mini Code is optimized for code generation, agentic software engineering, and terminal tasks. The emphasis on "agentic" here is deliberate -- this isn't just a code-completion model. It is specifically designed and trained for agentic software engineering tasks , meaning it's meant to operate inside multi-step agent loops: understanding a codebase, writing and executing code, running terminal commands, and orchestrating sub-agents.

The practical use cases Cohere highlights include:

• Autonomous software engineering workflows (fixing GitHub issues, implementing features)
• Terminal-based agentic tasks (navigating filesystems, running shell commands)
• Complex code generation for scientific and algorithmic problems
• Code review and system architecture mapping

Benchmarks: competitive at its size class

On Artificial Analysis' Coding Index, North Mini Code achieves a score of 33.4, outperforming Qwen3.5 (35B-A3B), Gemma 4 (26B-A4B), Devstral Small 2 (24B Dense), and even substantially larger models such as Nemotron 3 Super (120B-A12B), Mistral Small 4 (119B-A6B), and Devstral 2 (123B). That last part is the headline: a 3B-active-parameter model beating models with 10-40x more active compute is a strong signal of training efficiency.