browser-use/docs/supported-models.mdx

---
title: "Supported Models"
description: "Choose your favorite LLM"
icon: "microchip-ai"

---

### Browser Use [example](https://github.com/browser-use/browser-use/blob/main/examples/models/browser_use_llm.py)

`ChatBrowserUse()` is our optimized in-house model, matching the accuracy of top models while completing tasks **3-5x** faster. [See our blog post→](https://browser-use.com/posts/speed-matters)

```python
from browser_use import Agent, ChatBrowserUse

# Initialize the model
llm = ChatBrowserUse()

# Create agent with the model
agent = Agent(
    task="...", # Your task here
    llm=llm
)
```

Required environment variables:

```bash .env
BROWSER_USE_API_KEY=
```

Get your API key from the [Browser Use Cloud](https://cloud.browser-use.com/new-api-key). New signups get \$10 free credit via OAuth or \$1 via email.

#### Pricing

ChatBrowserUse offers competitive pricing per 1 million tokens:

| Token Type | Price per 1M tokens |
|------------|---------------------|
| Input tokens | $0.50 |
| Output tokens | $3.00 |
| Cached tokens | $0.10 |

<Note>
  Cached tokens provide significant cost savings on repeated context, reducing input costs by 80%.
</Note>

### Google Gemini [example](https://github.com/browser-use/browser-use/blob/main/examples/models/gemini.py)

<Warning>
`GEMINI_API_KEY` is deprecated and should be named `GOOGLE_API_KEY` as of 2025-05.
</Warning>

```python
from browser_use import Agent, ChatGoogle
from dotenv import load_dotenv

# Read GOOGLE_API_KEY into env
load_dotenv()

# Initialize the model
llm = ChatGoogle(model='gemini-flash-latest')

# Create agent with the model
agent = Agent(
    task="Your task here",
    llm=llm
)
```

Required environment variables:

```bash .env
GOOGLE_API_KEY=
```


### OpenAI [example](https://github.com/browser-use/browser-use/blob/main/examples/models/gpt-4.1.py)

`O3` model is recommended for best accuracy.

```python
from browser_use import Agent, ChatOpenAI

# Initialize the model
llm = ChatOpenAI(
    model="o3",
)

# Create agent with the model
agent = Agent(
    task="...", # Your task here
    llm=llm
)
```

Required environment variables:

```bash .env
OPENAI_API_KEY=
```

<Info>
  You can use any OpenAI compatible model by passing the model name to the
  `ChatOpenAI` class using a custom URL (or any other parameter that would go
  into the normal OpenAI API call).
</Info>

### Anthropic [example](https://github.com/browser-use/browser-use/blob/main/examples/models/claude-4-sonnet.py)

```python
from browser_use import Agent, ChatAnthropic

# Initialize the model
llm = ChatAnthropic(
    model="claude-sonnet-4-0",
)

# Create agent with the model
agent = Agent(
    task="...", # Your task here
    llm=llm
)
```

And add the variable:

```bash .env
ANTHROPIC_API_KEY=
```

### Azure OpenAI [example](https://github.com/browser-use/browser-use/blob/main/examples/models/azure_openai.py)

```python
from browser_use import Agent, ChatAzureOpenAI
from pydantic import SecretStr
import os

# Initialize the model
llm = ChatAzureOpenAI(
    model="o4-mini",
)

# Create agent with the model
agent = Agent(
    task="...", # Your task here
    llm=llm
)
```

Required environment variables:

```bash .env
AZURE_OPENAI_ENDPOINT=https://your-endpoint.openai.azure.com/
AZURE_OPENAI_API_KEY=
```

### AWS Bedrock [example](https://github.com/browser-use/browser-use/blob/main/examples/models/aws.py)

AWS Bedrock provides access to multiple model providers through a single API. We support both a general AWS Bedrock client and provider-specific convenience classes.

#### General AWS Bedrock (supports all providers)

```python
from browser_use import Agent, ChatAWSBedrock

# Works with any Bedrock model (Anthropic, Meta, AI21, etc.)
llm = ChatAWSBedrock(
    model="anthropic.claude-3-5-sonnet-20240620-v1:0",  # or any Bedrock model
    aws_region="us-east-1",
)

# Create agent with the model
agent = Agent(
    task="Your task here",
    llm=llm
)
```

#### Anthropic Claude via AWS Bedrock (convenience class)

```python
from browser_use import Agent, ChatAnthropicBedrock

# Anthropic-specific class with Claude defaults
llm = ChatAnthropicBedrock(
    model="anthropic.claude-3-5-sonnet-20240620-v1:0",
    aws_region="us-east-1",
)

# Create agent with the model
agent = Agent(
    task="Your task here",
    llm=llm
)
```

#### AWS Authentication

Required environment variables:

```bash .env
AWS_ACCESS_KEY_ID=
AWS_SECRET_ACCESS_KEY=
AWS_DEFAULT_REGION=us-east-1
```

You can also use AWS profiles or IAM roles instead of environment variables. The implementation supports:

- Environment variables (`AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, `AWS_DEFAULT_REGION`)
- AWS profiles and credential files
- IAM roles (when running on EC2)
- Session tokens for temporary credentials
- AWS SSO authentication (`aws_sso_auth=True`)

## Groq [example](https://github.com/browser-use/browser-use/blob/main/examples/models/llama4-groq.py)

```python
from browser_use import Agent, ChatGroq

llm = ChatGroq(model="meta-llama/llama-4-maverick-17b-128e-instruct")

agent = Agent(
    task="Your task here",
    llm=llm
)
```

Required environment variables:

```bash .env
GROQ_API_KEY=
```

## Oracle Cloud Infrastructure (OCI) [example](https://github.com/browser-use/browser-use/blob/main/examples/models/oci_models.py)

OCI provides access to various generative AI models including Meta Llama, Cohere, and other providers through their Generative AI service.

```python
from browser_use import Agent, ChatOCIRaw

# Initialize the OCI model
llm = ChatOCIRaw(
    model_id="ocid1.generativeaimodel.oc1.us-chicago-1.amaaaaaask7dceya...",
    service_endpoint="https://inference.generativeai.us-chicago-1.oci.oraclecloud.com",
    compartment_id="ocid1.tenancy.oc1..aaaaaaaayeiis5uk2nuubznrekd...",
    provider="meta",  # or "cohere"
    temperature=0.7,
    max_tokens=800,
    top_p=0.9,
    auth_type="API_KEY",
    auth_profile="DEFAULT"
)

# Create agent with the model
agent = Agent(
    task="Your task here",
    llm=llm
)
```

Required setup:
1. Set up OCI configuration file at `~/.oci/config`
2. Have access to OCI Generative AI models in your tenancy
3. Install the OCI Python SDK: `uv add oci` or `pip install oci`

Authentication methods supported:
- `API_KEY`: Uses API key authentication (default)
- `INSTANCE_PRINCIPAL`: Uses instance principal authentication
- `RESOURCE_PRINCIPAL`: Uses resource principal authentication

## Ollama

1. Install Ollama: https://github.com/ollama/ollama
2. Run `ollama serve` to start the server
3. In a new terminal, install the model you want to use: `ollama pull llama3.1:8b` (this has 4.9GB)

```python
from browser_use import Agent, ChatOllama

llm = ChatOllama(model="llama3.1:8b")
```

## Langchain

[Example](https://github.com/browser-use/browser-use/blob/main/examples/models/langchain) on how to use Langchain with Browser Use.

## Qwen [example](https://github.com/browser-use/browser-use/blob/main/examples/models/qwen.py)

Currently, only `qwen-vl-max` is recommended for Browser Use. Other Qwen models, including `qwen-max`, have issues with the action schema format.
Smaller Qwen models may return incorrect action schema formats (e.g., `actions: [{"navigate": "google.com"}]` instead of `[{"navigate": {"url": "google.com"}}]`). If you want to use other models, add concrete examples of the correct action format to your prompt.

```python
from browser_use import Agent, ChatOpenAI
from dotenv import load_dotenv
import os

load_dotenv()

# Get API key from https://modelstudio.console.alibabacloud.com/?tab=playground#/api-key
api_key = os.getenv('ALIBABA_CLOUD')
base_url = 'https://dashscope-intl.aliyuncs.com/compatible-mode/v1'

llm = ChatOpenAI(model='qwen-vl-max', api_key=api_key, base_url=base_url)

agent = Agent(
    task="Your task here",
    llm=llm,
    use_vision=True
)
```

Required environment variables:

```bash .env
ALIBABA_CLOUD=
```

## ModelScope [example](https://github.com/browser-use/browser-use/blob/main/examples/models/modelscope_example.py)

```python
from browser_use import Agent, ChatOpenAI
from dotenv import load_dotenv
import os

load_dotenv()

# Get API key from https://www.modelscope.cn/docs/model-service/API-Inference/intro
api_key = os.getenv('MODELSCOPE_API_KEY')
base_url = 'https://api-inference.modelscope.cn/v1/'

llm = ChatOpenAI(model='Qwen/Qwen2.5-VL-72B-Instruct', api_key=api_key, base_url=base_url)

agent = Agent(
    task="Your task here",
    llm=llm,
    use_vision=True
)
```

Required environment variables:

```bash .env
MODELSCOPE_API_KEY=
```

## Other models (DeepSeek, Novita, X...)

We support all other models that can be called via OpenAI compatible API. We are open to PRs for more providers.

**Examples available:**
- [DeepSeek](https://github.com/browser-use/browser-use/blob/main/examples/models/deepseek-chat.py)
- [Novita](https://github.com/browser-use/browser-use/blob/main/examples/models/novita.py)
- [OpenRouter](https://github.com/browser-use/browser-use/blob/main/examples/models/openrouter.py)