> ## Documentation Index
> Fetch the complete documentation index at: https://docs.cascadeflow.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# ModelConfig

> Configuration dataclass for defining models in a cascade — provider, cost, capabilities, and routing metadata.

# ModelConfig

Defines a model in the cascade. Models are sorted by cost — cheaper models are tried first as drafters, more expensive models serve as verifiers.

## Definition

```python theme={null}
from cascadeflow import ModelConfig

model = ModelConfig(
    name="gpt-4o-mini",
    provider="openai",
    cost=0.000375,
    supports_tools=True,
)
```

## Fields

| Field | Type | Default | Description |
| - | - | - | - |
| `name` | `str` | required | Model name (e.g., `"gpt-4o-mini"`) |
| `provider` | `str` | required | Provider name (e.g., `"openai"`, `"anthropic"`) |
| `cost` | `float` | `0.0` | Cost per 1K tokens in USD |
| `keywords` | `list[str]` | `[]` | Keywords for domain routing |
| `domains` | `list[str]` | `[]` | Domain tags for routing |
| `supports_tools` | `bool` | `False` | Whether model supports tool calling |
| `supports_vision` | `bool` | `False` | Whether model supports vision input |
| `max_tokens` | `int` | `2000` | Max generation tokens |
| `latency_ms` | `float` | `100.0` | Estimated latency in milliseconds |
| `temperature` | `float` | `0.7` | Default temperature |
| `top_p` | `float` | `1.0` | Top-p sampling |
| `frequency_penalty` | `float` | `0.0` | Frequency penalty |

## Providers

| Provider | Value | Models |
| - | - | - |
| OpenAI | `"openai"` | gpt-4o, gpt-4o-mini, gpt-5, gpt-5-mini |
| Anthropic | `"anthropic"` | claude-opus-4.5, claude-sonnet-4, claude-haiku-3.5 |
| Groq | `"groq"` | llama-3.3-70b, mixtral-8x7b |
| Ollama | `"ollama"` | Any locally served model |
| vLLM | `"vllm"` | Any self-hosted model |
| OpenRouter | `"openrouter"` | Any OpenRouter model |
| Together | `"together"` | Any Together AI model |

## Examples

### Two-Model Cascade

```python theme={null}
from cascadeflow import CascadeAgent, ModelConfig

agent = CascadeAgent(models=[
    ModelConfig(name="gpt-4o-mini", provider="openai", cost=0.000375),
    ModelConfig(name="gpt-4o", provider="openai", cost=0.00625),
])
```

### Multi-Provider Cascade

```python theme={null}
agent = CascadeAgent(models=[
    ModelConfig(name="llama-3.3-70b", provider="groq", cost=0.00059),
    ModelConfig(name="gpt-4o-mini", provider="openai", cost=0.000375),
    ModelConfig(name="claude-sonnet-4", provider="anthropic", cost=0.009),
])
```

### With Domain Routing

```python theme={null}
legal_model = ModelConfig(
    name="gpt-4o",
    provider="openai",
    cost=0.00625,
    domains=["legal", "compliance"],
    keywords=["contract", "regulation", "statute"],
)
```

### Local Model

```python theme={null}
local = ModelConfig(
    name="llama3:8b",
    provider="ollama",
    cost=0.0,  # Free
    latency_ms=50.0,
)
```


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