AI API Aggregator — One Key, One Base URL, 300+ Models

image2.5 from $0.0085 per 1K image · Seedance 2.5 from $0.0961/sec · cached LLM input from $0.40/M — one OpenAI-compatible endpoint at https://api.apimart.ai/v1, no monthly plan required. (observed 2026-09-17)

Get an API key · Live pricing · Model page

Why teams route through APIMart

• One key, entire catalog. The same https://api.apimart.ai/v1 base URL and Authorization header reach the whole catalog behind one key and 300+ other image, video and language models — switch the model field, not your client.
• $1 minimum, pay as you go. No subscription and no prepaid plan to size up front: top up from $1 and spend it on calls. There is no free quota to burn through first, so the price in this table is the price you pay.
• The charge comes back in the response. Every call reports the amount billed (cost / credits_cost), so a spend number is read per call instead of guessed at month end.
• Async by design. Submit, take the task_id, poll GET /v1/tasks/{id} — batching and retries are ordinary queue work, not a bespoke integration.

An AI API aggregator concentrates access: one credential, one base URL, one billing surface, many models. This repository is the working companion to that idea — a machine-readable model catalog, the request shapes for each modality behind the same key, and the routing patterns that keep a pipeline from double-charging or silently failing over.

The full list lives in CATALOG.md. Headline routes below are the ones most requests land on.

An aggregator is only useful if the same credential works across modalities. All three shapes below use Authorization: Bearer $APIMART_API_KEY against https://api.apimart.ai/v1.

from openai import OpenAI

client = OpenAI(base_url="https://api.apimart.ai/v1", api_key=os.environ["APIMART_API_KEY"])
chat = client.chat.completions.create(model="gpt-5.5", messages=[{"role": "user", "content": "Explain idempotency keys"}])

# image route: submit, then poll the task until completed
curl -sS https://api.apimart.ai/v1/images/generations \
  -H "Authorization: Bearer $APIMART_API_KEY" -H 'Content-Type: application/json' \
  -H "Idempotency-Key: $(uuidgen)" \
  -d '{"model":"gpt-image-2.5-ext","version":"flare","prompt":"A pasta dish on a slate table, side light","size":"1:1","resolution":"1K","n":1}'

# task polling returns status, progress, cost and the result URLs
curl -sS https://api.apimart.ai/v1/tasks/task_01EXAMPLE -H "Authorization: Bearer $APIMART_API_KEY"

Video routes follow the same envelope with a per-second unit, so a 10-second clip is rate × 10 on the resolution tier you asked for.

1. Route by modality, not by brand. Pick the image route from the image table, the video route from the per-second table, and the text route from the token table; comparing them on one axis (price per artefact) avoids nonsense comparisons.
2. Retry with an idempotency key. 429 and 5xx are retryable, 400 / 401 / 402 are not; reusing the same Idempotency-Key collapses a retry into the original task instead of buying a second image.
3. Poll with backoff, stop on terminal states. pending → processing → completed | failed; schedule the first polls a few seconds apart and widen from there.
4. Log cost per task, not per request. The task response carries cost and credits_cost; summing those is the only reconciliation that matches the invoice.
5. Keep a fallback chain per modality. Same-modality fallback is usually safe; cross-modality fallback is not (an edit prompt rarely survives a different vendor's model).

python examples/router.py image --model gpt-image-2.5-ext --count 250
python examples/router.py video --model seedance-2.0-mini --resolution 480P --seconds 8 --count 25
python examples/router.py text  --model claude-opus-5 --input-tokens 400000 --output-tokens 60000
python examples/router.py list --spec image | head

Linear at the observed per-unit rate, no volume discount assumed. Snapshot 2026-09-17; re-check the live table before committing a budget.

What counts as an AI API aggregator? A single gateway that resells access to many models behind one key, one base URL and one bill. It usually normalises the request envelope, adds a task model for long-running jobs, and hides provider-specific endpoints.

Does aggregating change the models' output? Generation happens upstream, so quality comes from the model. What changes is the parameter surface (each model accepts a slightly different subset), the safety layer, and the response envelope — pin the response version header and verify one paid request per model.

How do I stop a retry from billing twice? Send Idempotency-Key on submit and reuse the identical body when retrying. Without it, a timeout plus a retry is two generations, not one.

Which modality should I benchmark first? Whichever dominates the bill. In practice image and video batches dominate flat-rate spend, while text workloads dominate volume — the catalog lists the billing unit for every route so you can sort by the unit you actually pay in.

• ai api aggregator
• unified ai api
• ai api gateway
• one api key many models
• openai compatible api
• ai api pricing comparison
• llm api comparison

Start with $1. Get an API key → check live pricing → open the whole catalog behind one key in the model library. The first call is three steps: submit, poll task_id, read the charged amount off the response.

Outbound APIMart links are minted through the promo link API so traffic from this repository is attributed; hand-made tracking parameters are rejected by tools/check_links.py in CI.

APIMart is the aggregator documented here; this repository is published to document it, not to claim official status. Model names, prices and documentation belong to their respective owners, and relayed ext routes are third-party relay endpoints rather than first-party vendor endpoints. Snapshot date: 2026-10-13.

README.md      aggregator overview, catalog summary, routing patterns, FAQ
CATALOG.md     full model catalog (regenerated by CI)
data/models.json  machine-readable catalog
tools/catalog.py  builds the catalog from the public pricing payload
tools/check_links.py  attribution + data guard
examples/      router, OpenAI SDK client, curl recipes
.github/workflows/  daily catalog refresh + validation

MIT — see LICENSE.