Providers

OpenCode uses the AI SDK and Models.dev to support 75+ LLM providers and it supports running local models. To add a provider you need to:

Add the API keys for the provider using the /connect command.
Configure the provider in your OpenCode config.

Credentials

When you add a provider’s API keys with the /connect command, they are stored in ~/.local/share/opencode/auth.json.

Config

You can customize the providers through the provider section in your OpenCode config.

Base URL

You can customize the base URL for any provider by setting the baseURL option. This is useful when using proxy services or custom endpoints.

opencode.json

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "anthropic": {
      "options": {
        "baseURL": "https://api.anthropic.com/v1"
      }
    }
  }
}

OpenCode Zen

OpenCode Zen is a list of models provided by the OpenCode team that have been tested and verified to work well with OpenCode.

If you are new, we recommend starting with OpenCode Zen.

Run the /connect command

Run the /connect command in the TUI, select opencode, and head to opencode.ai/auth.

/connect

Paste your API key

┌ API key
│
│
└ enter

Select a model

Run /models in the TUI to see the list of models we recommend.

/models

It works like any other provider in OpenCode and is completely optional to use.

Provider Directory

Let’s look at some of the providers in detail. If you’d like to add a provider to the list, feel free to open a PR.

Don’t see a provider here? Submit a PR.

Anthropic

Run /connect

Once you’ve signed up, run the /connect command and select Anthropic.

/connect

Select auth method

Here you can select the Claude Pro/Max option and it’ll open your browser and ask you to authenticate.

┌ Select auth method
│
│ Claude Pro/Max
│ Create an API Key
│ Manually enter API Key
└

Access models

Now all the Anthropic models should be available when you use the /models command.

/models

Using your Claude Pro/Max subscription in OpenCode is not officially supported by Anthropic.

Using API keys

You can also select Create an API Key if you don’t have a Pro/Max subscription. It’ll also open your browser and ask you to login to Anthropic and give you a code you can paste in your terminal. Or if you already have an API key, you can select Manually enter API Key and paste it in your terminal.

Amazon Bedrock

To use Amazon Bedrock with OpenCode:

Request model access

Head over to the Model catalog in the Amazon Bedrock console and request access to the models you want.

You need to have access to the model you want in Amazon Bedrock.

Configure authentication

Choose one of the following methods:

Environment Variables (Quick Start)

Set one of these environment variables while running opencode:

# Option 1: Using AWS access keys
AWS_ACCESS_KEY_ID=XXX AWS_SECRET_ACCESS_KEY=YYY opencode

# Option 2: Using named AWS profile
AWS_PROFILE=my-profile opencode

# Option 3: Using Bedrock bearer token
AWS_BEARER_TOKEN_BEDROCK=XXX opencode

Or add them to your bash profile:

~/.bash_profile

export AWS_PROFILE=my-dev-profile
export AWS_REGION=us-east-1

Configuration File (Recommended)

For project-specific or persistent configuration, use opencode.json:

opencode.json

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "amazon-bedrock": {
      "options": {
        "region": "us-east-1",
        "profile": "my-aws-profile"
      }
    }
  }
}

Available options:

region - AWS region (e.g., us-east-1, eu-west-1)
profile - AWS named profile from ~/.aws/credentials
endpoint - Custom endpoint URL for VPC endpoints (alias for generic baseURL option)

Configuration file options take precedence over environment variables.

Authentication Methods

AWS_ACCESS_KEY_ID / AWS_SECRET_ACCESS_KEY: Create an IAM user and generate access keys in the AWS Console
AWS_PROFILE: Use named profiles from ~/.aws/credentials. First configure with aws configure --profile my-profile or aws sso login
AWS_BEARER_TOKEN_BEDROCK: Generate long-term API keys from the Amazon Bedrock console
AWS_WEB_IDENTITY_TOKEN_FILE / AWS_ROLE_ARN: For EKS IRSA (IAM Roles for Service Accounts) or other Kubernetes environments with OIDC federation

Authentication Precedence

Amazon Bedrock uses the following authentication priority:

Bearer Token - AWS_BEARER_TOKEN_BEDROCK environment variable or token from /connect command
AWS Credential Chain - Profile, access keys, shared credentials, IAM roles, Web Identity Tokens (EKS IRSA), instance metadata

When a bearer token is set (via /connect or AWS_BEARER_TOKEN_BEDROCK), it takes precedence over all AWS credential methods including configured profiles.

Select a model

Run the /models command to select the model you want.

/models

For custom inference profiles, use the model and provider name in the key and set the id property to the arn. This ensures correct caching:

opencode.json

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "amazon-bedrock": {
      "models": {
        "anthropic-claude-sonnet-4.5": {
          "id": "arn:aws:bedrock:us-east-1:xxx:application-inference-profile/yyy"
        }
      }
    }
  }
}

OpenAI

We recommend signing up for ChatGPT Plus or Pro.

Run /connect

Once you’ve signed up, run the /connect command and select OpenAI.

/connect

Select auth method

Here you can select the ChatGPT Plus/Pro option and it’ll open your browser and ask you to authenticate.

┌ Select auth method
│
│ ChatGPT Plus/Pro
│ Manually enter API Key
└

Access models

Now all the OpenAI models should be available when you use the /models command.

/models

Using API keys

If you already have an API key, you can select Manually enter API Key and paste it in your terminal.

GitHub Copilot

To use your GitHub Copilot subscription with opencode:

Some models might need a Pro+ subscription to use.

Run /connect

Run the /connect command and search for GitHub Copilot.

/connect

Authorize with GitHub

Navigate to github.com/login/device and enter the code.

┌ Login with GitHub Copilot
│
│ https://github.com/login/device
│
│ Enter code: 8F43-6FCF
│
└ Waiting for authorization...

Select a model

Now run the /models command to select the model you want.

/models

Google Vertex AI

To use Google Vertex AI with OpenCode:

Check model availability

Head over to the Model Garden in the Google Cloud Console and check the models available in your region.

You need to have a Google Cloud project with Vertex AI API enabled.

Set environment variables

Set the required environment variables:

GOOGLE_CLOUD_PROJECT: Your Google Cloud project ID
VERTEX_LOCATION (optional): The region for Vertex AI (defaults to global)
Authentication (choose one):
- GOOGLE_APPLICATION_CREDENTIALS: Path to your service account JSON key file
- Authenticate using gcloud CLI: gcloud auth application-default login

Set them while running opencode:

GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account.json GOOGLE_CLOUD_PROJECT=your-project-id opencode

Or add them to your bash profile:

~/.bash_profile

export GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account.json
export GOOGLE_CLOUD_PROJECT=your-project-id
export VERTEX_LOCATION=global

The global region improves availability and reduces errors at no extra cost. Use regional endpoints (e.g., us-central1) for data residency requirements.

Select a model

Run the /models command to select the model you want.

/models

DeepSeek

Create API key

Head over to the DeepSeek console, create an account, and click Create new API key.

Run /connect

Run the /connect command and search for DeepSeek.

/connect

Enter API key

Enter your DeepSeek API key.

┌ API key
│
│
└ enter

Select a model

Run the /models command to select a DeepSeek model like DeepSeek Reasoner.

/models

Local Models

Ollama

You can configure opencode to use local models through Ollama.

Ollama can automatically configure itself for OpenCode. See the Ollama integration docs for details.

opencode.json

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "ollama": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "Ollama (local)",
      "options": {
        "baseURL": "http://localhost:11434/v1"
      },
      "models": {
        "llama2": {
          "name": "Llama 2"
        }
      }
    }
  }
}

In this example:

ollama is the custom provider ID. This can be any string you want.
npm specifies the package to use for this provider. Here, @ai-sdk/openai-compatible is used for any OpenAI-compatible API.
name is the display name for the provider in the UI.
options.baseURL is the endpoint for the local server.
models is a map of model IDs to their configurations. The model name will be displayed in the model selection list.

If tool calls aren’t working, try increasing num_ctx in Ollama. Start around 16k - 32k.

LM Studio

You can configure opencode to use local models through LM Studio.

opencode.json

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "lmstudio": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "LM Studio (local)",
      "options": {
        "baseURL": "http://127.0.0.1:1234/v1"
      },
      "models": {
        "google/gemma-3n-e4b": {
          "name": "Gemma 3n-e4b (local)"
        }
      }
    }
  }
}

llama.cpp

You can configure opencode to use local models through llama.cpp’s llama-server utility.

opencode.json

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "llama.cpp": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "llama-server (local)",
      "options": {
        "baseURL": "http://127.0.0.1:8080/v1"
      },
      "models": {
        "qwen3-coder:a3b": {
          "name": "Qwen3-Coder: a3b-30b (local)",
          "limit": {
            "context": 128000,
            "output": 65536
          }
        }
      }
    }
  }
}

Custom Provider

To add any OpenAI-compatible provider that’s not listed in the /connect command:

You can use any OpenAI-compatible provider with opencode. Most modern AI providers offer OpenAI-compatible APIs.

Run /connect and select Other

Run the /connect command and scroll down to Other.

$ /connect

┌  Add credential
│
◆  Select provider
│  ...
│  ● Other
└

Enter provider ID

Enter a unique ID for the provider.

$ /connect

┌  Add credential
│
◇  Enter provider id
│  myprovider
└

Choose a memorable ID, you’ll use this in your config file.

Enter API key

Enter your API key for the provider.

$ /connect

┌  Add credential
│
▲  This only stores a credential for myprovider - you will need to configure it in opencode.json, check the docs for examples.
│
◇  Enter your API key
│  sk-...
└

Configure in opencode.json

Create or update your opencode.json file in your project directory:

opencode.json

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "myprovider": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "My AI ProviderDisplay Name",
      "options": {
        "baseURL": "https://api.myprovider.com/v1"
      },
      "models": {
        "my-model-name": {
          "name": "My Model Display Name"
        }
      }
    }
  }
}

Here are the configuration options:

npm: AI SDK package to use, @ai-sdk/openai-compatible for OpenAI-compatible providers
name: Display name in UI
models: Available models
options.baseURL: API endpoint URL
options.apiKey: Optionally set the API key, if not using auth
options.headers: Optionally set custom headers

Select your model

Run the /models command and your custom provider and models will appear in the selection list.

Example with Advanced Options

Here’s an example setting the apiKey, headers, and model limit options:

opencode.json

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "myprovider": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "My AI ProviderDisplay Name",
      "options": {
        "baseURL": "https://api.myprovider.com/v1",
        "apiKey": "{env:ANTHROPIC_API_KEY}",
        "headers": {
          "Authorization": "Bearer custom-token"
        }
      },
      "models": {
        "my-model-name": {
          "name": "My Model Display Name",
          "limit": {
            "context": 200000,
            "output": 65536
          }
        }
      }
    }
  }
}

Configuration details:

apiKey: Set using env variable syntax
headers: Custom headers sent with each request
limit.context: Maximum input tokens the model accepts
limit.output: Maximum tokens the model can generate

The limit fields allow OpenCode to understand how much context you have left. Standard providers pull these from models.dev automatically.

Troubleshooting

If you are having trouble with configuring a provider, check the following:

Check auth setup

Run opencode auth list to see if the credentials for the provider are added to your config.This doesn’t apply to providers like Amazon Bedrock, that rely on environment variables for their auth.

For custom providers, verify config

Check the opencode config and:

Make sure the provider ID used in the /connect command matches the ID in your opencode config
The right npm package is used for the provider. For example, use @ai-sdk/cerebras for Cerebras. And for all other OpenAI-compatible providers, use @ai-sdk/openai-compatible
Check correct API endpoint is used in the options.baseURL field

Get Started

Core Concepts

Configuration

Integrations

Extensibility

Interfaces

Advanced

Enterprise

Credentials

Config

Base URL

OpenCode Zen

Provider Directory

Anthropic

Using API keys

Amazon Bedrock

Environment Variables (Quick Start)

Configuration File (Recommended)

Authentication Methods

Authentication Precedence

OpenAI

Using API keys

GitHub Copilot

Google Vertex AI

DeepSeek

Local Models

Ollama

LM Studio

llama.cpp

Custom Provider

Example with Advanced Options

Troubleshooting

Get Started

Core Concepts

Configuration

Integrations

Extensibility

Interfaces

Advanced

Enterprise

​Credentials

​Config

​Base URL

​OpenCode Zen

​Provider Directory

​Anthropic

​Using API keys

​Amazon Bedrock

​Environment Variables (Quick Start)

​Configuration File (Recommended)

​Authentication Methods

​Authentication Precedence

​OpenAI

​Using API keys

​GitHub Copilot

​Google Vertex AI

​DeepSeek

​Local Models

​Ollama

​LM Studio

​llama.cpp

​Custom Provider

​Example with Advanced Options

​Troubleshooting

Credentials

Config

Base URL

OpenCode Zen

Provider Directory

Anthropic

Using API keys

Amazon Bedrock

Environment Variables (Quick Start)

Configuration File (Recommended)

Authentication Methods

Authentication Precedence

OpenAI

Using API keys

GitHub Copilot

Google Vertex AI

DeepSeek

Local Models

Ollama

LM Studio

llama.cpp

Custom Provider

Example with Advanced Options

Troubleshooting