GPT Image 2 API for Image Generation and Editing

What Is GPT Image 2

An OpenAI model for image generation and editing

We position GPT Image 2 as a generation and editing model for image creation. It can create new images from text descriptions or accept reference images and adjust the scene as requested.

In practice, we focus on whether it can turn a brief into an image that can be refined further, such as a poster with event copy, a product scene, or a visual sketch for an app's home screen.

Understanding visual requirements through text and high-fidelity reference images

A key feature of GPT Image 2 is its ability to process reference images together with written instructions. OpenAI's official guidance says that input images are automatically processed at high fidelity and that the native API should omit the input_fidelity parameter. This helps the model use information about people, products, and styles from the original image.

We recommend explaining what each reference image is for. For example, the first image supplies the product's appearance, the second supplies the background style, and the text specifies what can change. Compared with simply asking to "make it look better," this makes the result easier to check and refine.

Flexible dimensions and multiple quality levels

GPT Image 2 supports multiple output dimensions for square product images, portrait covers, and landscape posters. The official API supports landscape output up to 3840×2160 and the corresponding portrait dimensions, with low, medium, high, and auto quality levels.

Choose dimensions for the final display location, and choose quality based on detail requirements and cost. For drafts whose composition is still changing, we recommend using a lower resolution to confirm the direction, then generating a high-resolution final version once the subject, text, and layout are right.

RequestBeam's GPT Image 2 online tool and API

Through RequestBeam, we want to help you find out sooner how GPT Image 2 performs on your own tasks. After signing in and adding a small amount of credit, you can generate or edit images with the online tool, without first writing API integration code or buying a fixed subscription for a short evaluation. Once the results meet your needs, you can create an API Key and integrate it into your application, while reducing the hassle of managing accounts across multiple model platforms.

GPT Image 2 Core Features and Strengths

Chinese and multilingual text: generating poster copy and visuals together

We value GPT Image 2's ability to place text directly inside images. In real use cases shared by our customers, GPT Image 2's Chinese character rendering has improved considerably over the previous gpt-image version. They also noted remaining errors in the character "笼" in "小笼包" (xiaolongbao) and in some smaller text.

For event posters, product selling-point images, or multilingual covers, we recommend supplying the final copy and specifying each passage's position, relative size, and language. Short headlines can be tried as part of the generated image. For prices, dates, long passages, and text that must be completely accurate, we recommend checking every character and, when necessary, manually typesetting or correcting it afterward.

Complex composition: handling subjects, layout, and detailed constraints together

We think GPT Image 2 handles tasks with several explicit requirements in a single image well. One customer shared a desktop scene containing multiple windows, text, and ASCII graphics, then continued modifying the browser window's contents and the dog graphic within it.

For product promotional images, we recommend specifying the subject, background, copy placement, and empty space together, such as "place the product on the right, leave room for a headline on the left, and arrange three selling points along the bottom." In our experience with GPT Image 2, you can describe your requirements in as much detail as possible, and it can carry out the task well.

Reference-image editing: continuing to refine existing assets

We recommend GPT Image 2 for modifying existing assets, such as replacing backgrounds, trying color schemes, or exploring another visual style. In public discussions, one user created visual mockups from reference images and felt the realism was sufficient for professional work. Another asked it to change the color and shape of specific scales on a snake, with disappointing results. Reference-image feedback · Failed fine-grained local edit

Based on this feedback, we would first recommend tasks with clear goals and changes that are easy to inspect. When replacing a product background, for example, specify that the bottle's proportions, label text, and placement must remain unchanged. After generation, compare the original and result side by side, especially logos, edges, and reflections. If a region must be preserved exactly, you can composite only the required changes into the original afterward.

UI sketches and visual proposals: bringing product ideas into discussion sooner

Many of our customers use Images 2.0 to make UI sketches for product ideas and have responded positively to the results.

If you are planning an admin interface, an app home screen, or a game menu, we recommend asking GPT Image 2 for several layouts first, then using the images to discuss information hierarchy, colors, and visual emphasis. For independent developers, this can make an idea easier to communicate. Once the direction is settled, implement it with real components, interactions, and responsive layouts; the generated image itself does not include that development work.

Characters and storyboards: creating a sequence around the same subject

We think GPT Image 2 is worth trying for character design and small storyboard explorations. One of our customers first generated an anthropomorphic portrait, then generated side views and action images, and was satisfied with how well the character's appearance was maintained.

We recommend establishing one character reference image, then trying a front view, a side view, and one key action, checking the face, clothing, colors, and background texture in each image. Expand the full set only after those key shots are acceptable. For stories requiring multiple images, we also recommend keeping the original character reference rather than continually editing from results that have already drifted.

GPT Image 2 Benchmarks and Performance

We retain leaderboard versions and quality levels, and distinguish formal blind evaluations, small personal tests, and usage experiences.

Text-to-image blind evaluation: AA High reaches 1172 Elo

On Artificial Analysis's AA-Image-T2I v2.0 leaderboard, GPT Image 2 at high scores 1172±8 Elo, currently placing third. On the same leaderboard, GPT Image 1.5 at high scores 1107±8, and Nano Banana 2 scores 1126±7. This indicates that GPT Image 2 remains competitive under this evaluation's prompts and human-preference criteria. AA text-to-image leaderboard

For product posters, content covers, and images requiring a clear composition, we recommend including it among your first models to try. Teams already using Image 1.5 can start by comparing briefs that previously required the most rework, checking whether text, layout, and subject details improve. This result is from high; we do not apply it directly to lower quality levels.

Single-image editing: Arena Medium reaches 1462 points

Arena's single-image editing leaderboard on 2026-10-06 records GPT Image 2 at medium with 1462±3, in third place. Meanwhile, AA-Image-Editing v2.0 gives its high level 1125±8 Elo, in sixth place, with a rank interval of fifth to seventh. The two evaluations use different tasks, quality levels, and scoring scales. Their different rankings remind us to validate specific editing needs separately. Arena single-image editing leaderboard · AA image editing leaderboard

We recommend checking two things together: whether the requested changes were made correctly, and whether content that was not supposed to change was also altered. For a product background replacement, check not only whether the new background looks natural but also the packaging text, product shape, and colors. For work requiring strict preservation of original details, start with a small test before scheduling batch processing.

Generation speed: planning wait times around quality and aspect ratio

Some of our customers have waited around two minutes for a complex image generated through the API at high. OpenAI's GPT Image 2 guidance also notes that square images usually generate fastest; actual time depends on dimensions and request conditions. API High usage experience · Official model settings

We recommend fixing the dimensions and quality first, then recording the time from submission to receiving the complete image for your usual tasks. For features where users repeatedly edit images, evaluate medium first. For complex final deliverables, compare whether high is worth the wait. If waiting is noticeably hurting the experience, we also recommend trying GPT Image 2.5 Flare, discussed below.

Generation cost: quality levels within the same model have very different prices

OpenAI lists the following per-image output cost examples for GPT Image 2. They exclude text and reference-image input costs and are not RequestBeam's actual prices. OpenAI official cost examples

We pay particular attention to the move from medium to high at 1024×1024: the example output cost is nearly four times higher, but that does not mean visual quality will improve by the same proportion. We recommend establishing a baseline at medium, then comparing high on specific problems such as small text or complex materials. Include edits and retries when calculating the total cost of each final usable image.

Comparing GPT Image 2 with GPT Image 1.5, Nano Banana 2, GPT Image 2.5 Flare, and GPT Image 2.5 Sunburst

Compared with GPT Image 1.5: upgrade the tasks that need the most rework first

In the same AA leaderboard's high comparison, GPT Image 2 scores higher than Image 1.5 for text-to-image generation, but their cost difference depends on dimensions. In OpenAI's official examples, output costs at 1024×1024 and high are USD 0.211 and USD 0.133 respectively; at 1536×1024 and high, they are USD 0.165 and USD 0.200. AA text-to-image comparison · Official same-dimension cost table

If you frequently regenerate images because of packaging text, poster layouts, or complex composition, we recommend trying Image 2 first. For simple tasks that already reliably produce acceptable images, you can keep your existing workflow for now.

Compared with Nano Banana 2: compare quality levels alongside batch budgets

Nano Banana 2 corresponds to Google's gemini-3.1-flash-image, officially positioned around speed, efficiency, and high throughput. Under standard billing, output for a 1K image costs about USD 0.067. Compared with the Image 2 official examples above, this is less than high at USD 0.211 but more than medium at USD 0.053, so any cost comparison must state the quality level. Google official model and pricing

For large volumes of social images, thumbnail candidates, and ideas that need quick experimentation, we recommend including Nano Banana 2 in your comparison. For event key visuals, complex layouts, and text-heavy images, we recommend testing GPT Image 2 first.

Compared with GPT Image 2.5 Flare: worth reassessing for products with repeated image editing

When OpenAI released Image 2.5 on 2026-09-08, it said Flare delivered higher image quality than GPT Image 2 while reducing API generation latency by 50%. This is a vendor-published comparison, and the announcement does not list all test conditions. For your own tasks, measure again with your dimensions and quality settings. OpenAI Image 2.5 release announcement

If you are building an online image tool where users repeatedly replace backgrounds, change colors, and try alternatives, we recommend running the same set of tasks through GPT Image 2.5 Flare and GPT Image 2. Focus on wait times and whether the subject changes after each edit. For teams already integrated with Image 2, we think Image 2.5 Flare deserves an early small-scale comparison test.

Compared with GPT Image 2.5 Sunburst: fine editing puts more weight on detail preservation

OpenAI positions Sunburst for tasks needing finer editing control, such as finished marketing assets and retouched product images, while noting that it takes longer to generate than Flare. On AA's current editing leaderboard, Sunburst at max scores 1183±8 Elo, while Image 2 at high scores 1125±8 Elo. OpenAI model positioning · AA editing evaluation

Based on customer feedback, GPT Image 2.5 Sunburst use cases tend to be more complex than Image 2 use cases. Image 2.5 Sunburst performs better for brand assets that require multiple rounds of fine editing. If your task involves more complex details, we recommend upgrading to GPT Image 2.5 Sunburst for testing.

GPT Image 2 API Frequently Asked Questions

How do I call the GPT Image 2 API through RequestBeam?

We recommend first signing in to RequestBeam and creating an API Key, then reading our documentation or asking AI to help you configure the connection.

We recommend completing a minimal generation test through the online tool first, then integrating according to RequestBeam's current GPT Image 2 API documentation. This lets you verify whether the model meets your business needs before integration and helps avoid mistakenly carrying over endpoint paths and parameters from other providers into RequestBeam.

Why do image count, mask, or size settings cause a 400 error?

We recommend first checking whether old code is passing parameters from another API. RequestBeam's GPT Image 2 currently supports only n: 1, has no separate /images/edits route, and does not accept multipart/form-data. Passing mask, mask_url, or maskUrl returns 400. To edit an image, use image_urls and written instructions as described above. RequestBeam parameter boundaries

For dimensions, you can combine size: "16:9" with resolution: "2k". If you specify pixel dimensions directly, they take precedence over the resolution level. Choose auto, low, medium, or high for quality, not Image 2.5's xhigh or max. We recommend starting with a documented combination and adjusting one parameter at a time, rather than changing several at once and making problems harder to isolate. RequestBeam size and quality settings

Why is b64_json missing from the result, and how should I read images from a streaming request?

With stream: false, this RequestBeam endpoint waits for generation to finish and returns an image URL; read data[0].url. We recommend checking that the URL exists before downloading and saving the image. The native OpenAI Images API returns a different Base64 image structure, so its parsing code cannot be copied directly. RequestBeam response structure · OpenAI native response structure

With stream: true, keep processing SSE, wait for the image_generation.completed event, and read its image URL, continuing until [DONE]. This endpoint does not first return a jobId, so do not poll /v1/jobs/{id} for image results. We recommend treating "connected" and "image generated" as separate states. RequestBeam result handling

Why shouldn't I retry immediately after a timeout, and how should I troubleshoot it?

A timeout or stopping the wait only means the client did not continue waiting for the result; it does not prove that the server canceled generation. We recommend checking available history and account usage before deciding whether to submit again, to avoid repeatedly issuing paid requests when the outcome is unclear. After success, download the image to your own storage rather than treating the returned URL as a permanent storage location. RequestBeam timeouts and saving images

For explicit errors, start with the status code: check the API Key for 401, the balance for 402, whether the model exists or is enabled for 404, and request frequency for 429; for 5xx, use the specific error to assess service or generation problems. We recommend keeping the request time, model, parameters, and error details. Do not just increase the timeout or apply the same retry behavior to every error. RequestBeam error codes