fabro/docs/core-concepts/models.mdx

---
title: "Models"
description: "How Fabro routes tasks to LLM models and providers"
---

No single model is best at everything. Fabro lets you assign the right model to each workflow step — cheap, fast models for boilerplate, frontier models for hard reasoning, and a different provider for cross-critique so the reviewer brings fresh eyes. When a provider goes down, Fabro can fail over automatically.

<Frame>
  <img src="/images/ensemble-workflow.svg" alt="Ensemble workflow: fan out to Opus and Gemini Pro, merge, then synthesize" />
</Frame>

## Model catalog

| Model | Provider | Aliases | Context | Cost (in/out per Mtok) | Speed |
|---|---|---|---|---|---|
| `claude-opus-4-6` | anthropic | `opus`, `claude-opus` | 1M | $15.00 / $75.00 | 25 tok/s |
| `claude-sonnet-4-6` | anthropic | `sonnet`, `claude-sonnet` | 200K | $3.00 / $15.00 | 50 tok/s |
| `claude-sonnet-4-5` | anthropic | | 200K | $3.00 / $15.00 | 50 tok/s |
| `claude-haiku-4-5` | anthropic | `haiku`, `claude-haiku` | 200K | $0.80 / $4.00 | 100 tok/s |
| `gpt-5.2` | openai | `gpt5` | 1M | $1.80 / $14.00 | 65 tok/s |
| `gpt-5-mini` | openai | `gpt5-mini` | 1M | $0.20 / $2.00 | 70 tok/s |
| `gpt-5.2-codex` | openai | | 1M | $1.80 / $14.00 | 100 tok/s |
| `gpt-5.3-codex` | openai | `codex` | 1M | $1.80 / $14.00 | 100 tok/s |
| `gpt-5.3-codex-spark` | openai | `codex-spark` | 128K | n/a | 1000 tok/s |
| `gpt-5.4` | openai | `gpt54` | 1M | $2.50 / $15.00 | 70 tok/s |
| `gpt-5.4-mini` | openai | `gpt54-mini` | 400K | $0.75 / $4.50 | 140 tok/s |
| `gpt-5.4-pro` | openai | `gpt54-pro` | 1M | $30.00 / $180.00 | 20 tok/s |
| `gemini-3.1-pro-preview` | gemini | `gemini-pro` | 1M | $2.00 / $12.00 | 85 tok/s |
| `gemini-3.1-pro-preview-customtools` | gemini | `gemini-customtools` | 1M | $2.00 / $12.00 | 85 tok/s |
| `gemini-3-flash-preview` | gemini | `gemini-flash` | 1M | $0.50 / $3.00 | 150 tok/s |
| `gemini-3.1-flash-lite-preview` | gemini | `gemini-flash-lite` | 1M | $0.20 / $1.50 | 200 tok/s |
| `kimi-k2.5` | kimi | `kimi` | 262K | $0.60 / $3.00 | 50 tok/s |
| `glm-4.7` | zai | `glm`, `glm4` | 203K | $0.60 / $2.20 | 100 tok/s |
| `minimax-m2.5` | minimax | `minimax` | 197K | $0.30 / $1.20 | 45 tok/s |
| `mercury-2` | inception | `mercury` | 131K | $0.20 / $0.80 | 1000 tok/s |

Each provider requires its own API key set via environment variable (e.g. `ANTHROPIC_API_KEY`, `OPENAI_API_KEY`, `GEMINI_API_KEY`). See the [Quick Start](/getting-started/quick-start) for setup.

## Default models

When no model or provider is specified, Fabro auto-detects the provider by checking which API keys are configured, using precedence order Anthropic > OpenAI > Gemini. If no keys are found, it falls back to Anthropic. Each provider has a default model:

| Provider | Default model |
|---|---|
| `anthropic` | `claude-sonnet-4-6` |
| `openai` | `gpt-5.4` |
| `gemini` | `gemini-3.1-pro-preview` |
| `kimi` | `kimi-k2.5` |
| `zai` | `glm-4.7` |
| `minimax` | `minimax-m2.5` |
| `inception` | `mercury` |

## Using models in workflows

Assign models to workflow nodes using [model stylesheets](/workflows/stylesheets), which use a CSS-like syntax:

```dot title="example.fabro"
digraph Example {
    graph [
        model_stylesheet="
            *        { model: claude-haiku-4-5; }
            .coding  { model: claude-sonnet-4-5; reasoning_effort: high; }
            #review  { model: gemini-3.1-pro-preview; }
        "
    ]

    spec     [label="Write Spec"]
    implement [label="Implement", class="coding"]
    review   [label="Review"]
}
```

This routes the spec node to Haiku (the default), implementation to Sonnet, and review to Gemini Pro.

## Overriding the default model

Model stylesheets set per-node models inside the workflow graph, but you can also override the default model for an entire run. This is useful for quick experimentation or when you want to swap models without editing the Graphviz file.

### CLI flags

Pass `--model` and optionally `--provider` to `fabro run`:

```bash
fabro run files-internal/demo/01-hello.fabro --model claude-opus-4-6
fabro run files-internal/demo/04-pipeline.fabro --model gemini-3.1-pro-preview
```

These flags set the default model for all nodes that don't have an explicit model assigned via a stylesheet. The provider is automatically inferred from the model catalog — you only need `--provider` for models not in the catalog or to force a specific provider.

### Run config TOML

For repeatable runs, set the model in a run config file:

```toml title="run.toml"
version = 1
goal = "Implement the feature"
graph = "implement.fabro"

[llm]
model = "claude-sonnet-4-5"

[llm.fallbacks]
anthropic = ["gemini", "openai"]
gemini = ["anthropic", "openai"]
```

Then launch with:

```bash
fabro run run.toml
```

The `[llm.fallbacks]` table is optional. It maps each provider to an ordered list of fallback providers to try when the primary is unavailable.

<Note>
The precedence order is: node-level stylesheet > run config TOML > CLI flags > server defaults. More specific settings always win.
</Note>

## CLI commands

### List models

View all available models, or filter by provider:

```bash
fabro model list
fabro model list --provider anthropic
fabro model list --query codex
```

### Test models

Verify that your API keys are working by sending a test prompt to each configured provider:

```bash
fabro model test
fabro model test --model claude-sonnet-4-5
fabro model test --provider openai
```

This is useful for confirming connectivity after setup or when adding a new provider key.