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Bolt is model-agnostic by design. Under the hood it uses the Vercel AI SDK to speak to providers, which means any provider the SDK supports — Anthropic, OpenAI, Google, Amazon Bedrock, OpenRouter, and more — works out of the box. The provider and model catalog is sourced from models.dev, a community-maintained database of provider metadata and pricing.

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Managing provider credentials

Use bolt providers (aliased as bolt auth) to add, list, and remove provider credentials.

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Add or update credentials

This launches an interactive prompt to select a provider and enter credentials. To skip the interactive flow, pass flags directly:

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List configured providers

Displays all stored credentials and any provider API keys detected from environment variables.

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Remove credentials

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Environment variable authentication

For CI pipelines or headless environments, set the provider’s API key environment variable directly — no interactive login required. Common examples:
Bolt reads these variables automatically at startup. bolt providers list will surface which environment variables it detected.

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Browsing available models

bolt models lists every model available across your configured providers.
Run bolt models --verbose to see input/output token pricing for every model. This makes it easy to compare cost trade-offs before committing to a model for a long-running task.

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Model identifier format

Models are always referenced in provider/model format:
Use this format anywhere Bolt accepts a model — CLI flags, config files, and agent definitions.

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Selecting a model at runtime

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--model / -m flag

The --model flag is available on bolt run, bolt commit, and bolt review:

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--variant — controlling reasoning effort

Some models support configurable reasoning effort levels (e.g., Anthropic’s extended thinking, OpenAI’s reasoning models). Use --variant to select the effort tier:
The available variant values depend on the provider and model. Common values are minimal, low, medium, high, and max, but the exact set varies. Check your provider’s documentation or use bolt models --verbose to see supported variants for a given model.

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--thinking — show reasoning blocks

When using a model that supports extended thinking, pass --thinking to surface the model’s chain-of-thought in the output:

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Best-of-N parallel runs

--best-of fires the same prompt at multiple models in parallel, then uses a judge model to rank the responses and keep the best one:
The judge defaults to the model passed via --model, or to the first entry in the --best-of list if --model is not set. The flag accepts a comma-separated list of provider/model identifiers.
Best-of-N is especially useful for complex algorithmic problems or architecture decisions where different models may bring genuinely different approaches. Let Bolt run them in parallel and pick the winner automatically.

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Arena — parallel runs in isolated worktrees

bolt arena takes the same idea further and races several agents on the same task. Each contender gets its own git worktree, so they can freely edit files without stepping on each other. When they finish, a judge model ranks them on the final answer plus the actual git diff — not just the text they wrote.
Reach for arena instead of --best-of when the task involves editing files (refactors, feature work, test writing) rather than pure Q&A. The winning worktree stays on disk so you can inspect it, run tests, or merge the branch. The scoreboard and worktree paths print to stderr; the winner’s answer goes to stdout.
  • --models accepts 2+ provider/model entries. Duplicates are allowed — racing a model against itself in separate worktrees is a legitimate setup.
  • --judge picks the ranking model (defaults to the first entry in --models).
  • --cleanup deletes losing worktrees and their branches after judging. Without it, every worktree sticks around for you to review.
  • --format json emits a single JSON payload with the winner, ranking, and every candidate for scripting.
See bolt arena in the command reference for the full flag list.