Cohere Open-Sources North Mini Code, a Coding Agent for Agentic Pipelines

North Mini Code, a Coding Agent for Agentic Pipelines">

Engineering teams building agentic coding pipelines now have a concrete open-source alternative to managed models like Claude Fable 5 — one that runs on a single H100. The tradeoff: Cohere's North Mini Code, which launched Tuesday, generated three times the output tokens of comparable models in independent testing, a verbosity cost that compounds in high-volume production workloads. The new open-source model is a 30 billion parameter mixture-of-experts (MoE) model with 3 billion parameters active per token, built for agentic software engineering including sub-agent orchestration, architecture mapping, code review and terminal work.

The model supports a 256,000 token context window with a 64,000 token maximum generation length, and is available on Hugging Face under an Apache 2.0 license. North Mini Code targets the full agentic coding stack. Software engineering.

Cohere built North Mini Code specifically for agentic software engineering, not adapted from a general-purpose base. It has integrated tool-use capabilities and supports interleaved thinking, which Cohere says improves performance across multi-step agentic work. Architecture mapping and code review.

North Mini Code can analyze and map systems architecture, surface dependencies and perform code review across large codebases. With a 256,000 token context window, it can hold substantial multi-file projects in a single context pass. Terminal-based agentic tasks.

The model is trained for terminal environments, handling shell interactions, package scripts and command-line tooling. Cohere benchmarked it on Terminal-Bench v2, which tests agents in real terminal environments rather than synthetic code generation tasks. North Mini Code is a sparse mixture-of-experts model with 128 experts, of which 8 activate per token.

The compute requirement at inference time is closer to a 3 billion parameter model despite 30 billion total parameters. Nick Frosst, co-founder of Cohere, demoed it running on a Mac Studio via MLX at around 20 gigabytes of RAM, the same machine he uses for his own local coding work. Cohere trained the model through two stages of supervised fine-tuning followed by reinforcement learning with verifiable rewards across more than 70,000 verifiable tasks spanning approximately 5,000 repositories, deduplicated against SWE-Bench.

Rather than optimizing against a single agent scaffold, Cohere trained across three. SWE-Agent uses a rich CLI with specialized commands. Mini-SWE-Agent uses a single bash tool with raw shell output.

OpenCode uses individually typed tools returning structured JSON. Cohere reports a 10 percentage point gain on OpenCode evaluation from the multi-harness approach while maintaining SWE-Agent performance. North Mini Code enters a market that now includes Mistral Devstral Small 2, GitHub Copilot, Cursor, and Claude Fable 5 — each with distinct cost and deployment tradeoffs.