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Model Reference

This page lists recommended models for AgentUse, organized by provider.
Note: Other models from these providers may also work. These are the ones we recommend and test against.
Auto-generated: Run pnpm generate:models to update.

Quick Reference

Default models:
  • Anthropic: anthropic:claude-sonnet-5 (balanced performance)
  • OpenAI: openai:gpt-5.6 (latest GPT)
  • OpenRouter: openrouter:z-ai/glm-5.3 (open source)
  • OpenCode Go: opencode-go:gpt-5.6-luna (open coding models)
  • Amazon Bedrock: bedrock:us.anthropic.claude-sonnet-4-5-20250929-v1:0

Version Aliases

Leave the version off a model id and you get whichever release is current, so an agent file does not need editing every time a new model ships:
Aliases follow the lineup below, which is refreshed per AgentUse release. An id that exists for real always wins, so nothing you pin can be reinterpreted. Run agentuse models to see what each alias resolves to on your install, and agentuse models unpin to convert pinned ids in your agent files into aliases. To name your own, add a models.aliases block to ~/.agentuse/config.json and reference it with the @ sigil (see Configuration Files):
Named aliases may also define ordered fallback candidates and an in-memory cooldown. See Model defaults and aliases.

Anthropic

OpenAI

OpenRouter

OpenCode Go

Amazon Bedrock

Bedrock model IDs are passed through unchanged and are not validated against the static registry. Use any model ID supported by your AWS account and region. See the Amazon Bedrock model catalog for the full list. Model availability depends on the AWS region and on the model access granted in your account. Authentication uses standard AWS environment variables (AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY / AWS_REGION, optional AWS_SESSION_TOKEN) or AWS_BEARER_TOKEN_BEDROCK. See the Model Configuration guide for details.

Custom Providers

In addition to the built-in providers above, Dashboard Settings can connect custom OpenAI Chat Completions, OpenAI Responses, or Anthropic Messages endpoints. It discovers and saves available models and verifies the selected protocol with a minimal completion. For OpenAI-compatible local endpoints, you can also use the CLI:
Then use any model available on those endpoints:
See Model Configuration for full setup details.

Usage

Specify a model in your agent file, pinned to a version or tracking the line:
Omit model entirely to use the configured default (models.default or AGENTUSE_MODEL). Or override via CLI, which accepts the same aliases: