Uncensored models for coding agents

A coding agent is a tool loop, not a chat. The model has to call functions, read results, respect a context budget and recover from mistakes. Refusal behaviour matters, but tool reliability matters more.

Last updated: 2026-10-23

What the model actually does

In an agent, the model chooses the next action: read a file, run a command, edit code or ask for input. That requires reliable structured output, an understanding of tool results and enough context to keep the task state. An uncensored model can help when a policy refusal would otherwise interrupt a legitimate task, but it does not replace a capable coding model.

Choose for the loop, not the label

• Tool support. Start with models that advertise tools. Test a multi-turn loop with a failing command and a retry.
• Context. A large context window is useful, but cost grows with tokens. Summarise and prune rather than sending the whole repository.
• Instruction following. The model must follow file-edit and command conventions, not just write plausible code.
• Latency. A slower model can be fine for planning and painful for a tight edit loop. Measure both.
• Fallback. Keep a second model for 404s and upstream failures.

Models with tool support

A practical setup

1. Use a frontier model for the hard turns. Planning, architecture and difficult debugging benefit from stronger reasoning.
2. Use an uncensored model for the open-ended turns. Creative scaffolding, refactoring prose and tasks where a refusal would break flow.
3. Keep one key and one balance. Model choice is a per-request decision, not a separate account.
4. Cap output. Set max_tokens so the worst-case hold is predictable. A large context plus a large output limit can exceed a small balance.
5. Log metadata, not prompts. The API does not store message bodies; your own application should decide what it needs.

Cost control

Agent fan-out multiplies requests. A single task can trigger planning, tool calls, retries and summarisation. Use a key with a daily cap, watch the dashboard, and choose a cheaper model for routine steps. The pricing calculator is useful for a rough per-request estimate; real usage depends on the loop.

Safety

An agent can run commands and edit files. Treat model output as untrusted input to your own permission system. Sandbox the workspace, require approval for destructive operations, and never give an agent credentials it does not need. Uncensored behaviour is not a licence to skip those controls.

Related

Read the quickstart, the abliterated models guide, or the OpenCode setup for a one-command client install.

Questions