When a team adds a new AI API route, the first milestone should be small.
Do not start with a production migration, a large benchmark, or a full agent rollout. Start with one successful request that proves the base URL, API key, model name, balance, request shape, and logs are all working.
Disclosure: I work with ModelRouter. ModelRouter is an independent third-party OpenAI-compatible AI API gateway. It is not an official service from OpenAI, Anthropic, Google, DeepSeek, or any model provider.
Before comparing models or routes, check these basics:
This sounds boring, but it prevents a lot of false conclusions. Many route evaluations fail because of setup issues, not because the model route is bad.
Use one tiny request before wiring the route into a workflow:
export BASE_URL="https://modelrouter.site/v1"
export API_KEY="your_test_key"
export MODEL="copy_a_supported_route_from_your_dashboard"
curl "$BASE_URL/chat/completions" \
-H "Authorization: Bearer $API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "'"$MODEL"'",
"messages": [
{"role": "user", "content": "Say hello in one short sentence."}
]
}'
Never paste a real API key into public screenshots, GitHub issues, forum posts, or support threads.
For the first test, write down:
After that, test 10 to 20 real tasks from your workflow. For example:
The useful metric is not token price alone. It is cost per successful task after setup failures, retries, latency, and unusable outputs are counted.
Continue only if:
If any of those fail, before scaling. Fix the setup, try a different route, or keep the current production path.
ModelRouter evaluation link:
Again, ModelRouter is an independent third-party OpenAI-compatible gateway, not an official model-provider service.