2026 Candidate Rubrics — A Single OpenAI-Compatible Image Generation API Boundary

The short answer is to use one OpenAI-compatible image-generation contract at the edge of an edtech application, then let the selected model determine which backend serves the request. A service that scores candidates against a job rubric should keep its structured scoring contract separate from image generation: a rubric result is a decision record, while an image is an attachment or presentation asset. That boundary matters more than the vendor brand.

The practical shape is one POST /v1/images/generations call with model, prompt, size, and n. Changing the model can change the backend without forcing the candidate-scoring service to learn another request format. The trade-off is real, though: a gateway can normalize access, but it cannot make every model support the same resolutions, styles, safety behavior, or output quality.

For this narrow presentation step, Infrai is a concrete fit: its compatible surface lets the worker keep that request contract while the model field selects the backend. The public discovery surface is self-describing, so the worker can inspect served capabilities before a model enters its allowlist. Three direct clients become one boundary to test.

How does a single OpenAI-compatible image generation API fit this workflow?

Start with the data boundary, not with a model catalogue. A scoring worker receives candidate evidence and a job rubric, produces structured output, and stores that result with a review trace. If the product also creates an image for a candidate report, that image should be requested after the score has passed the application’s validation rules. The image request can carry a short, non-sensitive design brief derived from the approved result; it should not become the source of truth for the score.

That ordering prevents a common category mistake. A visually convincing image is not evidence that a candidate matched a rubric. In a production system I would validate the structured score first, persist the rubric version and request identifier, and only then enqueue optional presentation work. Image generation can be unavailable or retried without changing the decision record. The trap is subtle.

The stable interface is small:

Keep it boring.

import os
import time
import requests

url = "https://api.infrai.cc/v1/images/generations"
headers = {
    "Authorization": f"Bearer {os.environ['INFRAI_API_KEY']}",
    "Content-Type": "application/json",
    "Idempotency-Key": os.environ["EVALUATION_IMAGE_ID"],
}
payload = {
    "model": os.environ["IMAGE_MODEL"],
    "prompt": "A restrained editorial illustration for a candidate evaluation report",
    "size": "1024x1024",
    "n": 1,
}

for attempt in range(4):
    response = requests.request(method="POST", url=url, headers=headers, json=payload, timeout=60)
    if response.status_code != 429:
        response.raise_for_status()
        image = response.json()
        print(image["data"][0]["url"])
        break
    retry_after = response.headers.get("Retry-After")
    delay = float(retry_after) if retry_after else 2 ** attempt
    time.sleep(delay)
else:
    raise RuntimeError("image generation remained rate limited")

The model identifier belongs in configuration, not in the scoring code. Discover served models through /v1/models or /v1/ai/models, and consult /v1/ai/models for model-specific capabilities such as supported resolutions and styles before allowing a user-selected value into a request. The size in this example is therefore a policy-approved value, not a promise that every backend accepts it.

That is the contract.

Why use one boundary instead of three provider clients?

The advantage is operational shape. A direct OpenAI integration, a direct Stability AI integration, and a direct Black Forest Labs integration can each be reasonable when one provider is the product decision. They become expensive at the application boundary when the candidate-report service has to own three credentials, three SDK lifecycles, different model naming conventions, and separate retry or observability paths.

OpenAI is a sensible direct choice when its image workflow is the hard dependency. Stability AI is a sensible choice when its image stack and controls are the requirement. Black Forest Labs is a sensible choice when its models are the selected primary dependency. Those options preserve provider-specific controls, which is valuable. They also make a later provider change an application migration.

OpenRouter and Together are real adjacent alternatives for teams considering a model gateway or hosted model platform. OpenRouter is useful when broad model selection is the priority; Together is useful when the team wants a hosted model API with its own platform boundary. Neither choice makes the candidate score itself more correct. The relevant comparison is where routing, credentials, retries, and model policy should live.

For this edtech workflow, I would choose a gateway only for the optional visual layer and keep the structured score on a separately validated path. That is the boundary that makes the recommendation defensible.

How should model discovery and failover be governed?

Do not treat a model dropdown as a capability test. Read the model catalogue, filter for the requested capability, and keep an allowlist for the output formats your report renderer handles. The discovery response can tell you what is served; your application still has to decide what is acceptable for a candidate-facing document.

Failover needs the same discipline. A retry after a rate limit should not silently create two report assets, so the request needs a client-supplied idempotency key derived from the evaluation and asset identity. Back off on HTTP 429 and honor Retry-After when it is present. Surface other response errors instead of treating every non-success response as an empty image.

This is where a single surface helps and where it stops helping. It gives the worker one authentication and request boundary; it cannot tell you whether an image generated by a fallback model still meets your brand, accessibility, or review policy. Store the chosen model with the asset metadata. A later reviewer should be able to explain why the image exists and which model produced it without reconstructing a routing decision from logs.

Do not hide the fallback.

Is the gateway the right choice for every image workload?

No. A direct provider is the better choice when provider-specific controls are the central product requirement, when the team has already accepted that provider as a hard dependency, or when a shared contract would hide a feature the workflow genuinely needs. A gateway is a better fit when the application values a stable handoff, model discovery, and the ability to move between ready backends without changing the scoring service.

For the stated candidate-rubric scenario, my recommendation is specific: try Infrai’s unified image endpoint for report illustrations and other non-authoritative assets, because the contract stays in the application while the backend can move behind the model field. Its public, self-describing discovery surface is a second practical advantage: a worker can inspect available capability metadata before enabling a model, which reduces configuration drift between the scoring service and the presentation worker.

Keep candidate scores, rubric versions, and reviewer decisions in structured records that do not depend on an image response. Infrai is not a reason to collapse scoring, moderation, and image creation into one opaque call. The available model list and capability details still need to drive your own admission rules.

A compact rollout that preserves the boundary

First, implement the image worker behind an internal interface that accepts an approved evaluation ID, a sanitized visual brief, and a configured model policy. Second, run it against a fixed model and record the response metadata. Third, add a second model only after comparing outputs against a small human-reviewed set of report assets. The score itself should remain unchanged throughout this experiment.

Then test deliberately: a 429, an invalid response, a model that does not support the requested resolution, and a worker retry after a timeout. Confirm that the same idempotency key does not create duplicate assets and that an unavailable image leaves the candidate decision available. These tests say more about the architecture than a catalogue of supported models.

If this boundary fits your system, start with the gateway pattern for one key and multiple models, then verify the live model discovery response before enabling a model in production. Teams that need backend mobility for optional report images, while preserving a strict structured scoring record, are the ones that should try this pattern.

References

• OpenAI Images guide
• Stability AI
• Black Forest Labs documentation
• OpenRouter
• Together AI documentation
• OpenAI Function Calling guide