How to Choose an Open-Source Coding Model: License, Size and Context Explained

Not all "open" coding models are equal. Before you wire one into your workflow or product, check these four things.

1. License: can you actually ship it?

"Open weights" does not always mean "free for commercial use."

• Permissive (Apache 2.0, MIT): use it anywhere. Examples are Qwen3-Coder, Devstral and Granite Code.
• Custom with conditions: commercial use is allowed with restrictions, as with the Llama 2 Community License or OpenRAIL-M.
• Non-production: research and testing only. Codestral's open weights are an example.

Each model page on CoderBaba shows a Commercial use badge so you can see this at a glance.

2. Size: dense vs. mixture of experts

A dense model uses every parameter for every token. A mixture-of-experts (MoE) model routes each token through a subset of "experts." For example, DeepSeek-Coder-V2 has 236B total parameters but only 21B active. MoE models give you big-model quality at lower inference cost, but you still need enough memory to load all the weights.

3. Context window

The context window is how much code the model can see at once. 8K to 16K tokens is fine for single-file completion. For repository-level work and agents, look for 128K or more.

4. Task fit

• Autocomplete: small models with fill-in-the-middle (FIM) support
• Chat and refactoring: mid-size instruction-tuned models
• Autonomous agents: models trained for tool use and multi-step tasks

Quick checklist

1. Is the license compatible with your use?
2. Does it fit your hardware or budget?
3. Is the context window big enough for your task?
4. Was it trained for your task (FIM, chat or agentic)?

Compare models side by side in the directory.