Providers

Using any LLM provider in OpenCode.

OpenCode uses the AI SDK and Models.dev to support for 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.

{
  "$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. Learn more.

Run the /connect command in the TUI, select opencode, and head to opencode.ai/auth.
```
/connect
```
Sign in, add your billing details, and copy your API key.
Paste your API key.
```
┌ API key
│
│
└ enter
```
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 it.

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.

Amazon Bedrock

To use Amazon Bedrock with OpenCode:

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.
You’ll need either to set one of the following environment variables:
- AWS_ACCESS_KEY_ID: You can get this by creating an IAM user and generating an access key for it.
- AWS_PROFILE: First login through AWS IAM Identity Center (or AWS SSO) using aws sso login. Then get the name of the profile you want to use.
- AWS_BEARER_TOKEN_BEDROCK: You can generate a long-term API key from the Amazon Bedrock console.
Once you have one of the above, set it while running opencode.
Terminal window
```
AWS_ACCESS_KEY_ID=XXX opencode
```
Or add it to your bash profile.
~/.bash_profile
```
export AWS_ACCESS_KEY_ID=XXX
```
Run the /models command to select the model you want.
```
/models
```

Anthropic

We recommend signing up for Claude Pro or Max.

Once you’ve signed up, run the /connect command and select Anthropic.
```
/connect
```
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
└
```
Now all the the Anthropic models should be available when you use the /models command.
```
/models
```

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.

Azure OpenAI

Head over to the Azure portal and create an Azure OpenAI resource. You’ll need:
- Resource name: This becomes part of your API endpoint (https://RESOURCE_NAME.openai.azure.com/)
- API key: Either KEY 1 or KEY 2 from your resource
Go to Azure AI Foundry and deploy a model.

The deployment name must match the model name for opencode to work properly.
Run the /connect command and search for Azure.
```
/connect
```
Enter your API key.
```
┌ API key
│
│
└ enter
```
Set your resource name as an environment variable:
Terminal window
```
AZURE_RESOURCE_NAME=XXX opencode
```
Or add it to your bash profile:
~/.bash_profile
```
export AZURE_RESOURCE_NAME=XXX
```
Run the /models command to select your deployed model.
```
/models
```

Azure Cognitive Services

Head over to the Azure portal and create an Azure OpenAI resource. You’ll need:
- Resource name: This becomes part of your API endpoint (https://AZURE_COGNITIVE_SERVICES_RESOURCE_NAME.cognitiveservices.azure.com/)
- API key: Either KEY 1 or KEY 2 from your resource
Go to Azure AI Foundry and deploy a model.

The deployment name must match the model name for opencode to work properly.
Run the /connect command and search for Azure Cognitive Services.
```
/connect
```
Enter your API key.
```
┌ API key
│
│
└ enter
```
Set your resource name as an environment variable:
Terminal window
```
AZURE_COGNITIVE_SERVICES_RESOURCE_NAME=XXX opencode
```
Or add it to your bash profile:
~/.bash_profile
```
export AZURE_COGNITIVE_SERVICES_RESOURCE_NAME=XXX
```
Run the /models command to select your deployed model.
```
/models
```

Baseten

Head over to the Baseten, create an account, and generate an API key.
Run the /connect command and search for Baseten.
```
/connect
```
Enter your Baseten API key.
```
┌ API key
│
│
└ enter
```
Run the /models command to select a model.
```
/models
```

Cerebras

Head over to the Cerebras console, create an account, and generate an API key.
Run the /connect command and search for Cerebras.
```
/connect
```
Enter your Cerebras API key.
```
┌ API key
│
│
└ enter
```
Run the /models command to select a model like Qwen 3 Coder 480B.
```
/models
```

Cortecs

Head over to the Cortecs console, create an account, and generate an API key.
Run the /connect command and search for Cortecs.
```
/connect
```
Enter your Cortecs API key.
```
┌ API key
│
│
└ enter
```
Run the /models command to select a model like Kimi K2 Instruct.
```
/models
```

DeepSeek

Head over to the DeepSeek console, create an account, and click Create new API key.
Run the /connect command and search for DeepSeek.
```
/connect
```
Enter your DeepSeek API key.
```
┌ API key
│
│
└ enter
```
Run the /models command to select a DeepSeek model like DeepSeek Reasoner.
```
/models
```

Deep Infra

Head over to the Deep Infra dashboard, create an account, and generate an API key.
Run the /connect command and search for Deep Infra.
```
/connect
```
Enter your Deep Infra API key.
```
┌ API key
│
│
└ enter
```
Run the /models command to select a model.
```
/models
```

Fireworks AI

Head over to the Fireworks AI console, create an account, and click Create API Key.
Run the /connect command and search for Fireworks AI.
```
/connect
```
Enter your Fireworks AI API key.
```
┌ API key
│
│
└ enter
```
Run the /models command to select a model like Kimi K2 Instruct.
```
/models
```

GitHub Copilot

To use your GitHub Copilot subscription with opencode:

Run the /connect command and search for GitHub Copilot.
```
/connect
```

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...

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

Google Vertex AI

To use Google Vertex AI with OpenCode:

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 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.
Terminal window
```
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
```

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

Groq

Head over to the Groq console, click Create API Key, and copy the key.
Run the /connect command and search for Groq.
```
/connect
```
Enter the API key for the provider.
```
┌ API key
│
│
└ enter
```
Run the /models command to select the one you want.
```
/models
```

Hugging Face

Hugging Face Inference Providers provides access to open models supported by 17+ providers.

Head over to Hugging Face settings to create a token with permission to make calls to Inference Providers.
Run the /connect command and search for Hugging Face.
```
/connect
```
Enter your Hugging Face token.
```
┌ API key
│
│
└ enter
```
Run the /models command to select a model like Kimi-K2-Instruct or GLM-4.6.
```
/models
```

Helicone is an LLM observability platform that provides logging, monitoring, and analytics for your AI applications. The Helicone AI Gateway routes your requests to the appropriate provider automatically based on the model.

Head over to Helicone, create an account, and generate an API key from your dashboard.
Run the /connect command and search for Helicone.
```
/connect
```
Enter your Helicone API key.
```
┌ API key
│
│
└ enter
```
Run the /models command to select a model.
```
/models
```

For more providers and advanced features like caching and rate limiting, check the Helicone documentation.

Optional Configs

In the event you see a feature or model from Helicone that isn’t configured automatically through opencode, you can always configure it yourself.

Here’s Helicone’s Model Directory, you’ll need this to grab the IDs of the models you want to add.

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "helicone": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "Helicone",
      "options": {
        "baseURL": "https://ai-gateway.helicone.ai",
      },
      "models": {
        "gpt-4o": {
          // Model ID (from Helicone's model directory page)
          "name": "GPT-4o", // Your own custom name for the model
        },
        "claude-sonnet-4-20250514": {
          "name": "Claude Sonnet 4",
        },
      },
    },
  },
}

Custom Headers

Helicone supports custom headers for features like caching, user tracking, and session management. Add them to your provider config using options.headers:

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "helicone": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "Helicone",
      "options": {
        "baseURL": "https://ai-gateway.helicone.ai",
        "headers": {
          "Helicone-Cache-Enabled": "true",
          "Helicone-User-Id": "opencode",
        },
      },
    },
  },
}

Session tracking

Helicone’s Sessions feature lets you group related LLM requests together. Use the opencode-helicone-session plugin to automatically log each OpenCode conversation as a session in Helicone.

npm install -g opencode-helicone-session

Add it to your config.

{
  "plugin": ["opencode-helicone-session"]
}

The plugin injects Helicone-Session-Id and Helicone-Session-Name headers into your requests. In Helicone’s Sessions page, you’ll see each OpenCode conversation listed as a separate session.

Common Helicone headers

Header	Description
`Helicone-Cache-Enabled`	Enable response caching (`true`/`false`)
`Helicone-User-Id`	Track metrics by user
`Helicone-Property-[Name]`	Add custom properties (e.g., `Helicone-Property-Environment`)
`Helicone-Prompt-Id`	Associate requests with prompt versions

See the Helicone Header Directory for all available headers.

llama.cpp

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

{
  "$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
          }
        }
      }
    }
  }
}

In this example:

llama.cpp 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.

IO.NET

IO.NET offers 17 models optimized for various use cases:

Head over to the IO.NET console, create an account, and generate an API key.
Run the /connect command and search for IO.NET.
```
/connect
```
Enter your IO.NET API key.
```
┌ API key
│
│
└ enter
```
Run the /models command to select a model.
```
/models
```

LM Studio

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

{
  "$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)"
        }
      }
    }
  }
}

In this example:

lmstudio 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.

Moonshot AI

To use Kimi K2 from Moonshot AI:

Head over to the Moonshot AI console, create an account, and click Create API key.
Run the /connect command and search for Moonshot AI.
```
/connect
```
Enter your Moonshot API key.
```
┌ API key
│
│
└ enter
```
Run the /models command to select Kimi K2.
```
/models
```

Nebius Token Factory

Head over to the Nebius Token Factory console, create an account, and click Add Key.
Run the /connect command and search for Nebius Token Factory.
```
/connect
```
Enter your Nebius Token Factory API key.
```
┌ API key
│
│
└ enter
```
Run the /models command to select a model like Kimi K2 Instruct.
```
/models
```

Ollama

You can configure opencode to use local models through Ollama.

{
  "$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.

Ollama Cloud

To use Ollama Cloud with OpenCode:

Head over to https://ollama.com/ and sign in or create an account.
Navigate to Settings > Keys and click Add API Key to generate a new API key.
Copy the API key for use in OpenCode.
Run the /connect command and search for Ollama Cloud.
```
/connect
```
Enter your Ollama Cloud API key.
```
┌ API key
│
│
└ enter
```
Important: Before using cloud models in OpenCode, you must pull the model information locally:
Terminal window
```
ollama pull gpt-oss:20b-cloud
```
Run the /models command to select your Ollama Cloud model.
```
/models
```

OpenAI

Head over to the OpenAI Platform console, click Create new secret key, and copy the key.
Run the /connect command and search for OpenAI.
```
/connect
```
Enter the API key for the provider.
```
┌ API key
│
│
└ enter
```
Run the /models command to select the one you want.
```
/models
```

OpenCode Zen

OpenCode Zen is a list of tested and verified models provided by the OpenCode team. Learn more.

Sign in to OpenCode Zen and click Create API Key.
Run the /connect command and search for OpenCode Zen.
```
/connect
```
Enter your OpenCode API key.
```
┌ API key
│
│
└ enter
```
Run the /models command to select a model like Qwen 3 Coder 480B.
```
/models
```

OpenRouter

Head over to the OpenRouter dashboard, click Create API Key, and copy the key.
Run the /connect command and search for OpenRouter.
```
/connect
```
Enter the API key for the provider.
```
┌ API key
│
│
└ enter
```

Many OpenRouter models are preloaded by default, run the /models command to select the one you want.

/models

You can also add additional models through your opencode config.

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "openrouter": {
      "models": {
        "somecoolnewmodel": {}
      }
    }
  }
}

You can also customize them through your opencode config. Here’s an example of specifying a provider

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "openrouter": {
      "models": {
        "moonshotai/kimi-k2": {
          "options": {
            "provider": {
              "order": ["baseten"],
              "allow_fallbacks": false
            }
          }
        }
      }
    }
  }
}

SAP AI Core

SAP AI Core provides access to 40+ models from OpenAI, Anthropic, Google, Amazon, Meta, Mistral, and AI21 through a unified platform.

Go to your SAP BTP Cockpit, navigate to your SAP AI Core service instance, and create a service key.

The service key is a JSON object containing clientid, clientsecret, url, and serviceurls.AI_API_URL. You can find your AI Core instance under Services > Instances and Subscriptions in the BTP Cockpit.
Run the /connect command and search for SAP AI Core.
```
/connect
```

Enter your service key JSON.

┌ Service key
│
│
└ enter

Or set the AICORE_SERVICE_KEY environment variable:

AICORE_SERVICE_KEY='{"clientid":"...","clientsecret":"...","url":"...","serviceurls":{"AI_API_URL":"..."}}' opencode

Or add it to your bash profile:

export AICORE_SERVICE_KEY='{"clientid":"...","clientsecret":"...","url":"...","serviceurls":{"AI_API_URL":"..."}}'

Optionally set deployment ID and resource group:
Terminal window
```
AICORE_DEPLOYMENT_ID=your-deployment-id AICORE_RESOURCE_GROUP=your-resource-group opencode
```
These settings are optional and should be configured according to your SAP AI Core setup.
Run the /models command to select from 40+ available models.
```
/models
```

OVHcloud AI Endpoints

Head over to the OVHcloud panel. Navigate to the Public Cloud section, AI & Machine Learning > AI Endpoints and in API Keys tab, click Create a new API key.
Run the /connect command and search for OVHcloud AI Endpoints.
```
/connect
```
Enter your OVHcloud AI Endpoints API key.
```
┌ API key
│
│
└ enter
```
Run the /models command to select a model like gpt-oss-120b.
```
/models
```

Together AI

Head over to the Together AI console, create an account, and click Add Key.
Run the /connect command and search for Together AI.
```
/connect
```
Enter your Together AI API key.
```
┌ API key
│
│
└ enter
```
Run the /models command to select a model like Kimi K2 Instruct.
```
/models
```

Venice AI

Head over to the Venice AI console, create an account, and generate an API key.
Run the /connect command and search for Venice AI.
```
/connect
```
Enter your Venice AI API key.
```
┌ API key
│
│
└ enter
```
Run the /models command to select a model like Llama 3.3 70B.
```
/models
```

xAI

Head over to the xAI console, create an account, and generate an API key.
Run the /connect command and search for xAI.
```
/connect
```
Enter your xAI API key.
```
┌ API key
│
│
└ enter
```
Run the /models command to select a model like Grok Beta.
```
/models
```

Z.AI

Head over to the Z.AI API console, create an account, and click Create a new API key.
Run the /connect command and search for Z.AI.
```
/connect
```
If you are subscribed to the GLM Coding Plan, select Z.AI Coding Plan.
Enter your Z.AI API key.
```
┌ API key
│
│
└ enter
```
Run the /models command to select a model like GLM-4.5.
```
/models
```

ZenMux

Head over to the ZenMux dashboard, click Create API Key, and copy the key.
Run the /connect command and search for ZenMux.
```
/connect
```
Enter the API key for the provider.
```
┌ API key
│
│
└ enter
```

Many ZenMux models are preloaded by default, run the /models command to select the one you want.

/models

You can also add additional models through your opencode config.

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "zenmux": {
      "models": {
        "somecoolnewmodel": {}
      }
    }
  }
}

Custom provider

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

Run the /connect command and scroll down to Other.

$ /connect

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

Enter a unique ID for the provider.
Terminal window
```
$ /connect

┌  Add credential
│
◇  Enter provider id
│  myprovider
└
```
Choose a memorable ID, you’ll use this in your config file.

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-...
└

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

{
  "$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.

More on the advanced options in the example below.

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

Example

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

{
  "$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, learn more.
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 the 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, 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.