Agentic coding is rapidly changing our ways of developing software. Not everyone can afford a subscription, though, but they shouldn't be excluded from the process of learning these new tools.

Just wanted to share a few tips on running near-frontier agentic coding setup almost for free.

1. APIs. Most of the agentic coding tools use two types of APIs - OpenAI or Anthropic compatible. OpenAI is must more common, but Anthropic is associated with Claude Code ecosystem. There are also OSS adapters to convert between the two as needed. Essentially, you need to find providers that serve inference for free.

1. OpenRouter. They always have a few models that are completely free at the expense of storing and using everything you send to them. There are frequent promotional periods after new model releases. You need to top up your account by ~$10, though, to avoid rate limits as they are applied based on your balance. After that, ensure to use Model IDs with `:free` postfix and your balance will not be consumed, you can use those indefinitely.

2. OpenCode. This is a great agentic harness (albeit its heavily tuned for larger models), its parent company also provides inference APIs. Due to the popularity, many LLM providers offer free tiers of the models there. Same caveat applies - you data will be stored and used.

3. Local inference. If you happened to have a ~6-8GB VRAM and ~32GB RAM - then you should be able to run staple ~30B-sized MoE models. GLM-4.7-Flash is currently the best one for using inside a harness, it's even capable enough to drive simple tasks in OpenCode, but I recommend simpler harnesses for better results.

4. What to expect. Most of these offerings come with a compromise in terms of data collection and/or inference quality. For example, OpenCode's free Kimi 2.5 is clearly different from the paid one from official provider. In general - do not trust any claims that compare smaller open weight models with the cloud offering, they are not there yet. However you can get really far and models like Kimi 2.5 are still very capable.

Thanks!