Know what
your model
can really do.
Add a connection. Run meaningful benchmarks. Measure performance with useful scores and task metadata—not retained execution traces.
{
"endpoint": "my-model",
"benchmark": "mmlu-test",
"limit": 25,
"expected_collection_mode": "prepaid"
}The hard part is building the model.
Evaluating it shouldn’t be.
A focused workflow for teams that want to understand their models—not maintain evaluation infrastructure.
Connect it once.
Add your API URL, credentials, and any custom headers. Give your connection a name you’ll actually remember.
endpoint: "production-v2"Ask better questions.
Submit a benchmark and sample size through the API. Your run keeps a record of the protocol and configuration it used.
benchmark: "mmlu-test"See the whole picture.
Follow progress, compare task scores, and inspect safe failure classifications—without stored prompts or model outputs.
GET /v1/evaluations/:idOne home for
every evaluation.
From your first test to your next release, keep connections, evaluation history, and API access in one considered workspace.
STARTED THROUGH THE API
Run history
production-v2Completed25 / 25classifier-v1Completed25 / 25production-v2Completed25 / 25Three fields.
That’s the starting line.
Start evaluations through the API. Use the dashboard to follow their status, inspect results, and manage your connections.
curl "$ALLEVALS_URL/v1/evaluations" \
-H "Authorization: Bearer $ALLEVALS_API_KEY" \
-H "Content-Type: application/json" \
-H "Idempotency-Key: release-v2-mmlu-001" \
-d '{
"endpoint": "my-model",
"benchmark": "mmlu-test",
"limit": 25,
"expected_collection_mode": "prepaid"
}'