If you are searching for “GPT-Image-1 vs GPT-Image-2,” you may be trying to answer a practical question: which model should replace the image generation API used by an existing application?
The first thing to check is availability.
As of October 29, 2026, OpenAI’s official model page states that GPT-Image-1 was shut down on October 23, 2026. It recommends GPT-Image-2.5 Sunburst for current image generation and editing. An old comparison that treats GPT-Image-1 as an active deployment option is therefore no longer enough to guide a new integration. See the official GPT-Image-1 status page.
This guide focuses on migration: identifying the request your application sends, choosing a currently available replacement, checking an API gateway route and measuring whether the new workflow produces usable images.
Why changing the model name is only one part of migration
An image generation workflow contains more than a model parameter.
Your application may depend on a particular endpoint, request format, image size, output encoding, timeout and billing assumption. A change in any of these can affect whether the integration works.
Consider a product-image tool that generates a square background and then places a product photograph over it. The application may assume that every response contains a base64 image, that generation finishes within a fixed time and that the output always has the same dimensions.
Even if a replacement model generates better images, the application can still fail if its response parser expects a different structure.
A useful migration checklist therefore covers five areas:
- Model availability and identity.
- Endpoint and request compatibility.
- Output parsing and file handling.
- Visual acceptance criteria.
- Cost and failure behavior.
An API gateway can provide a consistent access point, but you still need to verify the capabilities of the selected route.
Step 1: Inventory the existing image request
Find the code that creates the current request.
Record the model ID, endpoint path, prompt, dimensions, quality setting, reference-image handling and output format. Also record where the application stores the generated image.
Next, identify the business task.
“Generate images” is too broad. Useful descriptions include:
- Create a product background with empty space for a title.
- Produce a blog illustration without text.
- Edit an approved image while preserving the product.
- Generate three concept variations for human selection.
- Create a square promotional asset with exact required wording.
A replacement should be evaluated against the actual task. A model that produces attractive illustrations may still perform poorly on a workflow that depends on preserving product details or rendering exact text.
Save several historical outputs that were accepted by your team. These become comparison examples after the old model is no longer callable.
Step 2: Separate historical comparison from deployment choice
GPT-Image-1 outputs can remain useful as a historical baseline. They should not be treated as proof that the retired API is still available.
For an existing GPT-Image-2 integration, check whether your current provider continues to offer the required route and whether it meets your quality requirements.
For a new integration, review currently recommended and available models. Our GPT-Image-2.5 API overview provides a starting point for exploring that model family, while current provider documentation should govern the actual request.
Keep three questions separate:
- What did the old workflow produce?
- What can the replacement produce?
- What can your account actually call today?
This avoids choosing a model based on a comparison table that no longer reflects the deployment options.
Step 3: Verify the image route in your API gateway
Before changing application code, inspect the TreeRouter model catalog and the documentation associated with the intended image model.
Confirm the exact model ID and whether the route supports generation, editing or both.
Also check whether the route uses an Images-style API, a Responses-style workflow or a provider-specific format. Do not assume that a working text-chat endpoint proves image-generation compatibility.
If your account displays an older model label, verify what it resolves to. A gateway alias is not sufficient evidence that the original upstream model remains available.
For a first test, use one image, a short prompt and a documented size. Add reference images and optional parameters only after the basic request succeeds.
Step 4: Build the smallest valid request
If your selected gateway route documents compatibility with the Images generations endpoint, a request body might start like this:
{
"model": "COPY_THE_SUPPORTED_IMAGE_MODEL_ID",
"prompt": "Create a clean square background for a reusable water bottle advertisement. Use soft natural lighting, a neutral surface and empty space in the upper third. Do not add text, logos or an extra product.",
"size": "1024x1024"
}Replace the placeholder with the model ID from your account. Submit it only to the endpoint documented for that route.
The small request has a purpose: it isolates connectivity and basic image generation from the complications of editing, masks and additional controls.
Do not copy every parameter from an old implementation. For example, OpenAI’s current guidance says to omit input_fidelity for GPT-Image-2 because its image inputs are already processed at high fidelity and the parameter cannot be changed. Check the official image-generation guide for model-specific behavior.
After the first request works, add one feature at a time and repeat the test.
Step 5: Update response handling
A successful HTTP response does not automatically mean your application has a usable image.
Check that the response contains the expected image data. If the route returns base64 data, validate decoding and save the result using an appropriate file format. If it returns a URL, handle that documented URL flow and any relevant expiry behavior.
Avoid writing a parser that silently accepts an empty result.
Also inspect the actual file:
- Can the image be opened?
- Does its format match the file extension?
- Are the dimensions acceptable?
- Is the file within your upload limits?
- Does the application store it in the intended location?
These checks are particularly important when moving between providers or gateway routes, where response wrappers can differ even when the underlying task is similar.
Step 6: Create a visual acceptance set
Use a small collection of representative requests.
For a product-marketing application, include a plain background, a layout with reserved text space, a request with exact text, a portrait composition and an edit using an approved reference image.
For each test, define what counts as a pass before generating the result.
For example, a product-image edit might require the object’s label, shape and color to remain unchanged. A background-generation request might require no additional products and no accidental lettering.
Evaluate the new outputs against those requirements and against saved historical examples.
Do not describe this as a live GPT-Image-1 comparison if you are only using archived images. Label the baseline clearly so readers and colleagues understand what was actually tested.
Step 7: Measure cost per accepted image
A lower advertised unit price does not necessarily produce a cheaper workflow.
Suppose one route generates ten images, but only three meet the acceptance criteria. Another route may cost more per request while producing eight usable results.
The practical metric is:
Cost per accepted image = total generation and editing spend / number of accepted images
Include unsuccessful attempts and additional edits where they are billed. If reference images are used, check whether image-input costs apply.
Keep human review time as a separate metric. Combining it with API charges can be useful for internal planning, but show the assumptions rather than presenting the result as a provider price.
For an initial comparison, use the same task set and review criteria across candidates. Record actual charges from the relevant account instead of copying historical prices from an old article.
Step 8: Roll out the replacement gradually
Do not migrate every image job immediately after one successful request.
Start with a limited workflow, such as draft blog illustrations. Keep the new configuration separate from your current application settings so that you can disable the route without rewriting the interface.
A fallback must also be available. A retired model is not a valid rollback target.
Depending on the application, a sensible fallback could be another verified model, a queued job for later processing or a library of approved static assets.
Track failed requests, completion time, accepted-image rate and cost. Increase usage only when the results meet your requirements.
Common image API migration problems
The gateway returns an unsupported-parameter error.
Remove optional parameters and begin with the documented minimum request. Reintroduce settings individually.
Text generation works, but image generation does not.
Verify image-model access and the image endpoint. Text API access does not establish support for every image operation.
The image looks good, but the application rejects it.
Inspect dimensions, encoding, file type and storage behavior. The failure may be in file handling rather than generation.
The new route appears more expensive.
Compare the same task, size, quality and number of accepted outputs. Include reference-image inputs and billed retries where applicable.
The old model name still appears in a third-party interface.
Ask what the alias routes to and verify the actual service provided. A familiar label alone does not establish model identity.
Choose a replacement with evidence
The useful answer to “GPT-Image-1 vs GPT-Image-2” now includes model availability, compatibility and migration quality.
Start with your real workload, verify the current route and measure the images your application can actually use.
Create a TreeRouter account, check the image models available to your account, and run a small documented test before moving a production workflow.
