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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsImage and video generation APIs can fail for very different reasons—and those reasons call for different responses. In one 30-day dataset of 14,069 generations, content rejections accounted for 90% of image failures, while infrastructure failures dominated video errors; only 4% of video failures were content rejections. The practical lesson is to classify failures before retrying: fix rejected inputs, retry transient capacity problems carefully, and investigate errors you cannot classify.
What the 30-day dataset found
Super Lewis published the results on DEV Community on September 21, 2026, describing measurements taken the previous day over the preceding 30 days on apimodels.app. The workload comprised a pipeline that generated a still image and then supplied it as the first frame to a video model. The author works on that API platform and explicitly presents the figures as one operator’s dataset, not an industry benchmark. Multiple upstream providers served the models, so the reported infrastructure failures also reflect that platform’s routing. The findings have not been independently audited or replicated.
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In that dataset, the mix of failure causes differed sharply by media type. Content rejections made up 90% of image failures but 4% of video failures. Those are shares of failures, not shares of all requests. A rejected prompt generally needs a change by the caller; an infrastructure failure may clear if retried later. Combining them into one error rate obscures what action is likely to help.
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Reported outcomes by tier
The table reproduces Super Lewis’s platform-specific figures. Success rate, infrastructure-failure rate, and latency describe different dimensions: a high success rate does not imply every request succeeds, and a latency percentile does not describe failure causes.
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| Model tier | Calls | Success | Infrastructure failures | p50 latency | p90 latency |
|---|---|---|---|---|---|
| Fast image | 10,149 | 91.0% | 0.65% | 40.1 seconds | 71.2 seconds |
| Detail image | 3,630 | 89.7% | 3.55% | 52.0 seconds | 135.9 seconds |
| Video model | 290 | 82.4% | 14.83% | 36.5 seconds | 104.1 seconds |
All values are reported by Super Lewis for apimodels.app’s 30-day workload in September 2026; they are not portable benchmarks. The video sample was much smaller than either image tier. The author’s point about the fast image tier is that 91% success can sound poor when read alone, even though the reported infrastructure-failure share was only 0.65%: its failures were mostly of a different, user-correctable kind.
How to decide whether an API error should be retried
Classify errors by likely cause and action, rather than retrying every failed request. Provider status codes and message wording vary, so map the specific API’s documented responses and observed payloads into operational categories.
| Failure bucket | Typical interpretation | Response |
|---|---|---|
| User-correctable | Content-policy rejection or invalid input | Do not automatically resubmit the same request. Review the prompt or parameters and make a meaningful correction first. |
| Transient | Capacity, rate limiting, or a temporary upstream problem | Retry with bounded backoff, respecting the provider’s limits and any retry guidance in its response. |
| Unknown | An error that cannot be reliably mapped to either category | Record enough context to investigate and alert on the unknown bucket; avoid treating it as safely retryable by default. |
Super Lewis gives three attempts at 2, 4, and 8 seconds as an example of bounded backoff, not a universal setting. Tune attempts and delays to the provider’s rate limits, job duration, user expectations, and the cost of duplicate work. Do not keep retrying a content rejection: repeating unchanged input is unlikely to solve it and can waste time or create unnecessary load.
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A bare HTTP 500 does not tell a caller whether the prompt was rejected, the provider was at capacity, or another fault occurred. Where status alone is ambiguous, inspect documented error bodies and messages, preserve the response for diagnosis, and refine your mapping as you learn the provider’s behavior. Avoid silently assigning every unfamiliar error to the retryable bucket. Track known categories separately and alert when unknown errors appear or grow; as the author puts it, “Alert on the size of UNKNOWN, not on your overall error rate.”
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Designing an asynchronous generation workflow
Image and especially video generation can take long enough that a create-and-poll pattern is more practical than holding a request open. The example in Lewis’s article uses Node.js 24.19 and built-in fetch, without an SDK. It submits an image task, polls for completion, uses the resulting still as the video’s first frame, and polls the video task to a terminal state. The example names gpt-image-2.5-flare and grok-imagine-video-1.5 on the described platform.
- Create the image task. Send the image-generation request and save the returned task identifier.
- Poll the image task. Continue until it reaches a terminal state; handle failures using the error classification rather than treating every non-success as a reason to resubmit.
- Persist the still image. Download the completed asset into storage you control before passing it to the next stage.
- Create the video task. Supply the still image as the first frame and retain the new task identifier.
- Poll and persist the video. Poll to a terminal result, then download the output to owned storage.
Check the provider’s actual JSON fields at both creation and polling stages. The article’s sample received taskId in a create response but used task_id when polling; copying those names without checking the live payload can break an otherwise sound workflow. The author reports that hosted result files expired after seven days on apimodels.app, so do not treat a temporary result URL as permanent storage. That retention period is platform-specific and may change.
Compare useful outputs, not just request success
A success percentage is only one part of a production decision. Compare image and video workloads separately, and consider failure mix, latency distribution, cost, output dimensions, retention, and how many outputs are genuinely usable. The values below are examples reported for the author’s platform, not general API prices or expected performance.
- In one sample, a still image cost $0.008 and took 37 seconds; despite a 16:9 request, the delivered dimensions were 1672×941.
- The author reports that one second of 720p video cost more than six times that entire still frame on the described platform. This is a platform-specific comparison, not a universal price ratio.
- The article says billing on that platform was recorded on success, so upstream work that consumed GPU time and then failed could appear free to the customer. Request counts alone may therefore not reconcile with provider-side spend; check the applicable billing semantics.
Dimensions may snap to a model’s tiling grid rather than match the requested aspect ratio exactly. If downstream software requires exact pixel dimensions, resize the result after generation and account for that processing in the workflow.
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Keeping outputs stable and reducing operational surprises
Store the asset you intend to keep
If a generated image or clip needs to remain stable, generate it once and save the actual file in storage you control. A seed can help reproduce a generation under some conditions, but the author warns that it does not guarantee the same result after a model-version change. Retaining the delivered asset is safer than depending on future regeneration.
Keep exact typography out of the generation step
When words must be spelled exactly, generate the visual background or illustration and add the text with a separate design or rendering step. The article cautions against relying on image generation for exact text in an image.
Decide whether an error classifier is worth maintaining
A classifier adds implementation and maintenance work, especially when providers change error codes or wording. Lewis’s rule of thumb is that it may not be worthwhile at low failure volume. Make that decision using your own request volume and the cost of missed or mishandled failures; the article does not establish a universal threshold.
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When evaluating a provider or production implementation, record these details for each media workflow:
Quick Recap
- Error semantics: Can you distinguish content rejection, invalid input, capacity or rate limiting, and temporary upstream failure from codes or messages?
- Retry policy: Which errors are safe to retry, what limits apply, and how long can bounded backoff run before the request should fail or move to manual handling?
- Latency: What are p50 and p90 completion times for your own workload, and how long do asynchronous jobs remain pending?
- Useful-output cost: What does each successful, usable deliverable cost after accounting for failures, dimensions, resizing, and downstream work?
- Retention and reproducibility: How long do hosted URLs last, where will you store results, and what happens when a model changes?
- Observability: Are known error buckets visible, and will you notice unknown errors or changed provider wording?
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