Similar to hgoel the model is managing the project's arc and writing + debugging code, but the underlying work is pretty compute intensive and all LLM output that is part of the final product is generated by local LLMs. Claude Code builds the pipeline that does the work, the pipeline runs fully on open-weights models and is reproducible top to bottom. There is a lot of detail to cost management. First is of course the Claude Code subscription is heavily discounted vs API costs. Then managing context size and turn count, which multiplied are basically what determine usage accounting (cached read is almost all of the cost). Auto compaction at 175k or 200k tokens (model the right number for your work), sub-agents with good model selection, tools to predict subtask difficulty so the sub-agents are correctly sized to complete under the compaction limit. Lots of focus on tooling to improve turn efficiency (e.g. the tilth utility by another user here for querying code). This started as a few scripts in one of my research projects but now is how I run all of my agent coding workspaces, and in another month will probably start replacing Claude Code itself for my purposes.