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An image-editing example tells you to set input_fidelity to high, so you copy it into a request using GPT Image 2. The intention is reasonable: preserve more detail from the reference. But GPT Image 2 input fidelity is already fixed at high. OpenAI’s current image-generation guide says to omit that parameter for this model.
The practical fix starts with the request being sent. Check the exact model and endpoint, remove the unsupported field where appropriate, and keep output quality separate from input fidelity. Changing a word in the prompt will not repair an incompatible request parameter.
GPT Image 2 input fidelity is already fixed
For GPT Image 2, the direct OpenAI API processes image inputs at high fidelity without allowing that setting to be changed. An explicit input_fidelity: “high” is therefore not a required instruction to enable the behaviour.
Older examples can make this confusing. A snippet may be correct for the model it originally demonstrated, while becoming unsuitable when someone replaces only the model name. The request still carries assumptions from the earlier integration.
Start with the documentation section for the exact identifier, gpt-image-2. A page covering several image models may contain different examples and settings for each. The fact that a field appears somewhere on the page does not establish that it belongs in your request.
Treat a third-party route as a separate contract. A gateway may expose a different set of controls or use an SDK with its own parameter names. Check that route’s documentation before applying a direct OpenAI example to it. The aim is to send the documented request for the service you are actually calling.
Find which layer is adding the field
First, read the complete error rather than guessing from the visible symptom. An unsupported-parameter error needs a different response from a missing image, an invalid file or a model-access problem. Record the relevant field name and the request location it refers to.
If your application builds the request directly, inspect the object immediately before submission. For the direct gpt-image-2 route, remove the input_fidelity key. Sending null, an empty string or a differently capitalised value still sends a field; those substitutions are not the same as omitting it.
If the field returns after you remove it, inspect the shared defaults. A request builder may combine a base configuration with model-specific settings. A wrapper can also add a value that never appears in the screen the user sees. The final payload is the useful place to verify the correction.
For example, imagine a reusable configuration that includes input_fidelity for every image-editing model. Changing the selection to GPT Image 2 would leave the shared field in place unless the builder adjusts the supported options. Fix that mapping, rather than making users repeatedly delete the same value from individual requests.
Keep the investigation focused. Do not log API keys or dump private source images into a support ticket. A sanitised record of the provider, endpoint, model identifier and submitted option names is often enough to show where an unwanted field entered the request.
Then repeat one small request with the correction applied. Use an image you are authorised to process and an uncomplicated edit. If a different error appears, investigate it on its own terms. Removing one incompatible parameter does not prove that the rest of the request is valid.
Separate input fidelity from output quality
The word “high” can refer to different controls. GPT Image 2 has output-quality settings as well as its fixed handling of image inputs. Choosing a lower supported output-quality setting does not turn input_fidelity into a supported adjustable field.
This partial configuration illustrates the distinction:
{
“model”: “gpt-image-2”,
“quality”: “high”
}
It is not a complete edit request: the documented image input, prompt and other required request details still need to be supplied. It simply shows an output-quality choice without adding an input-fidelity parameter.
High input fidelity should not be read as a guarantee that every original pixel will survive an edit. It describes how the reference is processed. You still need to inspect the generated result against the source and decide whether the requested changes are acceptable.
For a useful check, identify a few details before submitting the edit: the exact lettering on a sign, the outline of an object, or a small feature that the instruction asks to retain. Compare those details in the downloaded result. This tells you more than deciding that the image looks generally similar.
If you compare the edit with Nano Banana Pro, use that model’s documented controls. Do not carry input_fidelity across merely because both tools accept reference images. A shared task does not imply a shared request schema.
Keep the correction in the request builder
Once the small request works, check the paths that create real jobs. A batch import, a saved preset or a retry worker may use a different builder from the screen you just fixed. Confirm that each path sends options supported by its selected model.
Keep a small regression check for the configuration itself. Given the direct GPT Image 2 route, the assembled payload should omit input_fidelity. For another model, use its own documented rule. This is more useful than deleting the field everywhere and accidentally changing a working integration.
Save the accepted request shape with the documentation date and a note explaining the omission. Otherwise someone reviewing the code later may mistake the missing field for an oversight and restore it from an older example.
For a maintained integration, the GPT Image 2 input fidelity rule belongs next to the model’s request configuration. Record that the field is intentionally omitted and check the final payload when changing models or updating a wrapper. That gives the next developer a reason to leave the fix in place.
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Excerpt: Fix GPT Image 2 input fidelity by omitting the unsupported field. Inspect shared defaults, separate output quality and verify the final edit against its source.
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Tags: GPT Image 2 input fidelity, input_fidelity, quality parameter, image editing API, request validation