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Coding agents are a natural fit for Tokios because they can keep a stable API configuration while you change the local model behind it. Register a deployment once, point the agent at Tokios, then switch models by changing the model value.

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Pick the API shape

Use your Tokios API key as the client API key. Use the deployment name you registered as the client model name.
1

Start with one known-good backend

Run one local backend first, such as Ollama, llama.cpp, vLLM, or LM Studio. Confirm it serves an OpenAI-compatible /v1/chat/completions endpoint before adding the coding agent.
2

Register one deployment

Give the deployment a short, stable name such as qwen-coder-local or gemma-tunnel. This is the value the coding agent sends in the model field.
3

Configure the agent

Set the base URL for the API shape the agent uses. OpenAI-style clients usually use https://api.tokios.com/v1; Anthropic-style clients use https://api.tokios.com.
4

Run a small task first

Ask the agent to explain a small file or make a tiny change before sending a larger refactor. This confirms routing, streaming, and model behavior.

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What to compare

When you test coding agents with local models, compare the full workflow rather than only raw model speed.
A faster model is not always the better coding model. For agent workflows, instruction following and repository understanding matter as much as generation speed.

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Next steps

Use Claude Code

Configure ANTHROPIC_BASE_URL and ANTHROPIC_API_KEY.

Use OpenAI Codex

Add Tokios as a Codex model provider in config.toml.

Benchmark your deployment

Compare latency, streaming, and task behavior across deployments.

Route multiple models

Keep one endpoint and switch models with the model field.