Introducing North Mini Code: Cohere's First Model For Developers
Cohere released North Mini Code, its first model built specifically for software developers and agentic coding work. The open-weight model packs 30 billion total parameters but activates only 3 billion per query through a Mixture-of-Experts design, and it ships free under the permissive Apache 2.0 license. It scores 80.2% pass@10 on the SWE-Bench Verified benchmark, beating several models four times its size while running on a single H100 GPU.
Key Takeaways
- North Mini Code is the first model in Cohere's new family of models, and is specifically designed and trained for agentic software engineering tasks.
Figure 1: North Mini Code's performance in agentic coding tasks and complex code generation benchmarks, compared to leading open-source models of similar size.
- Try North Mini Code in OpenCode Real-world code agents depend on model quality and robustness across agent harnesses.
We trained North Mini Code using multiple scaffolds rather than optimizing for a single one.
- The feed-forward block is an MoE block with 128 experts, of which 8 are activated per token.
- Our first stage SFT data focuses on coding capabilities that are integrated within a wider mix for robustness and usability.
The datamix includes programming, reasoning, and instruction following across a large variety of domains where the code datasets correspond to 70% of trainable tokens, 43% agentic tool-use data, and 27% single-turn competitive or scientific programming data.
- In total, we used over 70k verifiable tasks across ~5k unique repositories.
Stats & Key Facts
- #The open-weight model packs 30 billion total parameters but activates only 3 billion per query through a Mixture-of-Experts design, and it ships free under the permissive Apache 2.0 license.
- #It scores 80.2% pass@10 on the SWE-Bench Verified benchmark, beating several models four times its size while running on a single H100 GPU.
- #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).
- #The feed-forward block is an MoE block with 128 experts, of which 8 are activated per token.
North Mini Code is the first model in Cohere's new family of models, and is specifically designed and trained for agentic software engineering tasks. Figure 1: North Mini Code's performance in agentic coding tasks and complex code generation benchmarks, compared to leading open-source models of similar size. See here for the details of our benchmarking methodology.
North Mini Code is optimized for complex software engineering workflows, terminal-based agentic tasks, and high-quality code generation. 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). 1 It ranks among the strongest open-source coding models in its size class.
Try North Mini Code in OpenCode Real-world code agents depend on model quality and robustness across agent harnesses. We trained North Mini Code using multiple scaffolds rather than optimizing for a single one. This approach enables North Mini Code to serve as a reliable foundation for coding agents such as OpenCode.
For more details please read the original article at Hugging Face.
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