Coder powers secure, scalable development across key industries — automotive, finance, government, and technology — enabling faster builds, tighter compliance, and seamless AI adoption in enterprise-grade cloud environments.
Administrators configure LLM providers and models from the Coder dashboard.
These are deployment-wide settings — developers do not manage API keys or
provider configuration. They select from the set of models that an administrator
has enabled.
Providers
Each LLM provider has a type, an API key, and an optional base URL override.
Coder supports the following provider types:
Provider
Description
Anthropic
Claude models via Anthropic API
OpenAI
GPT and o-series models via OpenAI API
Google
Gemini models via Google AI API
Azure OpenAI
OpenAI models hosted on Azure
AWS Bedrock
Models available through AWS Bedrock
OpenAI Compatible
Any endpoint implementing the OpenAI API
OpenRouter
Multi-model routing via OpenRouter
Vercel AI Gateway
Models via Vercel AI SDK
The OpenAI Compatible type is a catch-all for any service that exposes an
OpenAI-compatible chat completions endpoint. Use it to connect to self-hosted
models, internal gateways, or third-party proxies like LiteLLM.
Add a provider
Navigate to the Agents page in the Coder dashboard.
Click Admin in the top bar to open the configuration dialog.
Select the Providers tab.
Click the provider you want to configure.
Enter the API key for the provider.
Optionally set a Base URL to override the default endpoint. This is
useful for enterprise proxies, regional endpoints, or self-hosted models.
Click Save.
The providers list shows all supported providers and their configuration
status.
Adding a provider requires an API key. The base URL is optional.
Provider API keys and security
Provider API keys are stored encrypted in the Coder database. They are never
exposed to workspaces, developers, or the browser after initial entry. The
dashboard shows only whether a key is set, not the key itself.
Because the agent loop runs in the control plane, workspaces never need direct
access to LLM providers. See
Architecture for details
on this security model.
Models
Each model belongs to a provider and has its own configuration for context limits,
generation parameters, and provider-specific options.
Add a model
Open the Admin dialog and select the Models tab.
Click Add and select the provider for the new model.
Enter the Model Identifier — the exact model string your provider
expects (e.g., claude-opus-4-6, gpt-5.3-codex).
Set a Display Name so developers see a human-readable label in the model
selector.
Set the Context Limit — the maximum number of tokens in the model's
context window (e.g., 200000 for Claude Sonnet).
Configure any provider-specific options (see below).
Click Save.
The models list shows all configured models grouped by provider.
Adding a model requires a model identifier, display name, and context
limit. Provider-specific options appear dynamically based on the selected
provider.
Set a default model
Click the star icon next to a model in the models list to make it the
default. The default model is pre-selected when developers start a new chat.
Only one model can be the default at a time.
Model options
Every model has a set of general options and provider-specific options.
The admin UI generates these fields automatically from the provider's
configuration schema, so the available options always match the provider type.
General options
These options apply to all providers:
Option
Description
Model Identifier
The API model string sent to the provider (e.g., claude-opus-4-6).
Display Name
The label shown to developers in the model selector.
Context Limit
Maximum tokens in the context window. Used to determine when context compaction triggers.
Compression Threshold
Percentage (0–100) of context usage at which the agent compresses older messages into a summary.
Max Output Tokens
Maximum tokens generated per model response.
Temperature
Controls randomness. Lower values produce more deterministic output.
Top P
Nucleus sampling threshold.
Top K
Limits token selection to the top K candidates.
Presence Penalty
Penalizes tokens that have already appeared in the conversation.
Frequency Penalty
Penalizes tokens proportional to how often they have appeared.
Input Price
Optional USD price metadata for input tokens, recorded per 1M tokens.
Output Price
Optional USD price metadata for output tokens, recorded per 1M tokens.
Cache Read Price
Optional USD price metadata for cache read tokens, recorded per 1M tokens.
Cache Write Price
Optional USD price metadata for cache creation/write tokens, recorded per 1M tokens.
Provider-specific options
Each provider type exposes additional options relevant to its models. These
fields appear dynamically in the admin UI when you select a provider.
Anthropic
Option
Description
Thinking Budget Tokens
Maximum tokens allocated for extended thinking.
Effort
Thinking effort level (low, medium, high, max).
OpenAI
Option
Description
Reasoning Effort
How much effort the model spends reasoning (none, minimal, low, medium, high, xhigh).
Max Completion Tokens
Cap on completion tokens for reasoning models.
Parallel Tool Calls
Whether the model can call multiple tools at once.
Google
Option
Description
Thinking Budget
Maximum tokens for the model's internal reasoning.
Include Thoughts
Whether to include thinking traces in the response.
Azure OpenAI uses the same options as OpenAI. AWS Bedrock uses the same
options as Anthropic.
How developers select models
Developers see a model selector dropdown when starting or continuing a chat on
the Agents page. The selector shows only models from providers that have valid
API keys configured. Models are grouped by provider if multiple providers are
active.
The model selector uses the following precedence to pre-select a model:
Last used model — stored in the browser's local storage.
Admin-designated default — the model marked with the star icon.
First available model — if no default is set and no history exists.
Developers cannot add their own providers, models, or API keys. If no models
are configured, the chat interface displays a message directing developers to
contact an administrator.
Using an LLM proxy
Organizations that route LLM traffic through a centralized proxy — such as
Coder's AI Bridge or third parties like LiteLLM — can point any provider's Base URL at their proxy endpoint.
For example, to route all OpenAI traffic through Coder's AI Bridge:
Add or edit the OpenAI provider.
Set the Base URL to your AI Bridge endpoint
(e.g., https://example.coder.com/api/v2/aibridge/openai/v1).
Enter the API key your proxy expects.
Alternatively, use the OpenAI Compatible provider type if your proxy serves
multiple model families through a single OpenAI-compatible endpoint.
This lets you keep existing proxy-level features like per-user budgets, rate
limiting, and audit logging while using Coder Agents as the developer interface.