I built a coding agent that gets 87% on benchmarks with a 4B parameter model, here's how

I was frustrated that every coding agent (OpenCode, Cursor, Claude Code) assumes you're running GPT-5.4 or Claude Opus. If you try them with a local model like Gemma or Qwen they fall apart. I find that often tool calls fail, context overflows, multi-step tasks collapse.

So I built SmallCode. It's designed from the ground up for small local models.

The result: 87/100 benchmark tasks pass with a Gemma 4 model that only activates 4B parameters per token. OpenCode scores ~75% with 14B models. The harness does the heavy lifting, not the model size.

How it works (the tricks that make small models reliable):

• Compound tools: Instead of making the model chain 4 tool calls (find file → read file → edit file → verify), SmallCode gives it one tool that does all 4. Small models lose coherence after 3+ sequential calls. This cuts failures in half.
• Improvement loop: Every time the model writes code, SmallCode instantly compiles/lints it. If it fails, it feeds the errors back automatically. The model doesn't need to be smart enough to get it right first try — it just needs to fix errors when shown them.
• Decompose on failure: If the model fails the same thing twice, SmallCode stops retrying and instead breaks the problem into smaller pieces. "Fix this 200-line file" becomes "fix line 45 only."
• Escalation: If even decompose fails and you have a Claude/OpenAI key configured, it auto-escalates to the bigger model for just that one task. You stay local 95% of the time, cloud 5%.
• Token budgeting: Small models have 32k-256k context. SmallCode never dumps a whole file in. It summarizes, truncates, and manages every token so the model never sees "..." truncation in the middle of important code.
• Code graph: Instead of grep-searching your codebase, SmallCode indexes your code into a symbol graph (functions, classes, who-calls-what). When you ask "how does auth work," it walks the graph and returns just the relevant connected code — not 15 random file snippets.

What it looks like:

Full-screen terminal UI (like OpenCode/vim), scrollable chat, command palette with /, plugin system, persistent memory across sessions.

What it doesn't do:

• No LSP integration (yet)
• No multi-session (yet)
• No desktop app
• Doesn't compete with Claude Code for frontier model users

Install:

npm install -g smallcode cd your-project smallcode 

Point it at LM Studio, Ollama, or any OpenAI-compatible endpoint.

MIT licensed, everything's on GitHub: https://github.com/Doorman11991/smallcode

Happy to answer questions about the architecture or benchmark methodology.