GoModel routes OpenAI-compatible requests to many AI providers through one
gateway. For most providers, setting the API key is the whole setup. The pages
in this section exist for the ones whose setup is not purely “set an API
key” — auth flows from cloud credentials, deployment-based URLs, region or
project requirements, dual native + OpenAI-compatible API surfaces, or other
quirks.
Supported providers
| Provider | Credential | Guide |
|---|
| OpenAI | OPENAI_API_KEY | — |
| Anthropic | ANTHROPIC_API_KEY | Anthropic |
| Google Gemini | GEMINI_API_KEY | Google Gemini |
| Google Vertex AI | VERTEX_PROJECT + VERTEX_LOCATION + GCP credentials | Google Vertex AI |
| DeepSeek | DEEPSEEK_API_KEY | DeepSeek |
| Groq | GROQ_API_KEY | — |
| OpenRouter | OPENROUTER_API_KEY | — |
| Z.ai | ZAI_API_KEY (ZAI_BASE_URL optional) | — |
| xAI (Grok) | XAI_API_KEY | — |
| MiniMax | MINIMAX_API_KEY (MINIMAX_BASE_URL optional) | — |
| Azure OpenAI | AZURE_API_KEY + AZURE_BASE_URL (AZURE_API_VERSION optional) | Azure OpenAI |
| Amazon Bedrock | BEDROCK_BASE_URL (region or endpoint) + AWS credentials | Amazon Bedrock |
| Oracle GenAI | ORACLE_API_KEY + ORACLE_BASE_URL | Oracle GenAI |
| Ollama | OLLAMA_BASE_URL | Ollama |
| vLLM | VLLM_BASE_URL (VLLM_API_KEY optional) | vLLM |
Why some providers have dedicated pages
These are the providers most users hit friction on:
- Google Vertex AI — needs a GCP project, region, and either Application
Default Credentials or a service-account JSON key.
- Amazon Bedrock — no API key of its own; uses the AWS credential chain
and requires explicit model access in the Bedrock console.
- Azure OpenAI — deployment-scoped base URLs, the
api-version query
parameter, and the api-key header instead of Authorization: Bearer.
- Oracle GenAI — requires an OCI IAM policy for
generativeaiapikey and a
region-specific OpenAI-compatible endpoint URL.
- Google Gemini (AI Studio) — two routing modes (native
generateContent
vs OpenAI-compatible) with different image-input behavior.
- Anthropic — reasoning effort maps to Claude’s adaptive thinking and
effort control, which differ across model generations.
- DeepSeek — reasoning effort mapping quirks for DeepSeek V4.
- Ollama / vLLM — local-model hosting with optional multi-instance setup
through suffixed env vars and provider-qualified model IDs.
Every provider page shows three launch forms in one CodeGroup: Docker with
--env-file .env (recommended), Docker with inline -e flags, and a
prebuilt binary from make build. Once you have more than a couple of
variables, copy .env.template, fill in the values you need, and reuse the
same file for both Docker and the native binary.
