FLUX 3 vs GPT Image 2: choose your image editing workflow

FLUX 3 Image accepts up to ten reference images through reAPI; GPT Image 2 accepts up to sixteen. That gives a FLUX 3 vs GPT Image 2 comparison one concrete starting point: how many source images does your task need? FLUX 3 Image also documents a bounding-box vocabulary for describing where elements belong and how they should move during an edit^[1][2].

This FLUX 3 vs GPT Image 2 guide compares the default reAPI model IDs, flux-3-image and gpt-image-2. It covers request controls and an evaluation plan for your assets. We have not run a controlled quality or speed benchmark between them. Reference limits and “4K” labels cannot establish output quality.

TL;DR

• For explicit layout instructions, start with FLUX 3 Image. Its documented prompt format includes element names and normalized bounding boxes. Boxes guide placement; they are not strict clipping masks^[3].
• For eleven to sixteen references in one request, the default GPT Image 2 ID has the necessary capacity. FLUX 3 Image accepts at most ten. Capacity does not establish how well either combines a particular set^[1][2].
• Both defaults generate one image per request through the same task interface. Submit to /api/v1/images/generations, then poll the returned task ID. Neither default accepts a selectable output format or quality tier^[1][2].
• FLUX 3 Image exposes extra controls: five resolution tiers, a grounding toggle, and an API safety tolerance. Its website limits safety tolerance to 0–1; API requests accept 0–4^[1].
• Evaluate the delivered files before deciding. Both default models use resolution-based per-image billing. Compare their current quotes and the number of outputs you accept; this article contains no measured FLUX 3 vs GPT Image 2 cost winner^[1][2].

FLUX 3 vs GPT Image 2 starts with the exact request

A FLUX 3 vs GPT Image 2 comparison needs an exact API scope. OpenAI's native GPT Image 2 API exposes settings beyond the default reAPI gpt-image-2 request. Use the following table when building against reAPI; use the native OpenAI documentation when calling OpenAI directly^[2][4].

The two reAPI references document these request boundaries^[1][2]. Both accept common shapes such as square, 16:9, and 4:3, but their full ratio lists differ. FLUX 3 Image includes 7:5 and 5:7; GPT Image 2 includes 3:1 and 1:3. Check the enum before copying a less common ratio between requests.

For a FLUX 3 vs GPT Image 2 trial, begin with one shared ratio and n: 1. Keep optional fields model-specific to avoid invalid requests.

Reference roles and boxes solve different editing problems

Suppose you have a photograph of a room and a separate image of a chair. A clear shared instruction is: use the first image as the room, replace the chair beside the window with the chair in the second image, and preserve the window, floor, and camera position. Both default IDs accept a prompt with reference URLs for this kind of image-to-image request^[1][2].

Assign each reference a role: the room supplies composition; the second image supplies the replacement object. A FLUX 3 vs GPT Image 2 evaluation with unclear reference roles could end up measuring two interpretations of an ambiguous brief.

FLUX 3 Image provides an additional documented way to express the edit. An element row can identify a source reference, a source box, a target box, and a description. Coordinates use [top, left, bottom, right] on a 0–1000 grid. The JSON rows belong inside the prompt string; there is no separate bbox request field^[3].

This is a useful control to evaluate when a brief includes moving an object, anchoring a layout, or changing several named elements. Preparing those boxes is part of the FLUX 3 vs GPT Image 2 tradeoff. Our FLUX 3 Image editing guide explains the row formats and reference ordering.

BFL describes boxes as placement and scale guidance, and says an element can extend outside its box. A box is therefore not a promise that every surrounding pixel remains identical^[3]. Inspect unchanged objects after each edit. The FLUX 3 vs GPT Image 2 decision should reflect the output your task needs, including whatever moved unintentionally.

Match delivered dimensions before judging detail

Resolution names need care in a FLUX 3 vs GPT Image 2 comparison. FLUX 3 Image offers two tiers absent from the default GPT request: 768sq and 1.5k. Both offer 1k, 2k, and 4k, but a tier name is not a universal pixel specification^[1][2].

The reAPI GPT Image 2 reference, for example, documents a square 4K request as 2880×2880 and a 16:9 4K request as 3840×2160. It requires an explicit ratio at 4K rather than auto. The FLUX 3 Image reference describes resolution tiers without promising one fixed width and height for every ratio^[2][1].

Record downloaded dimensions. Inspect originals at 100% for artifacts, then compare copies at the same delivery size. Label those views separately.

Also keep native settings separate. OpenAI's GPT Image 2 documentation describes flexible pixel dimensions and low, medium, high, or auto quality. Its native generation reference supports selectable image formats. Those facts do not make quality or output_format valid fields on reAPI's default gpt-image-2 ID^[4][5][2].

For the default-ID FLUX 3 vs GPT Image 2 test here, leave both fields out. Download the completed image URL and perform any delivery-format conversion as a separate step in your application.

Build a FLUX 3 vs GPT Image 2 evaluation you can repeat

This proposed test plan separates a common brief from a second round using model-specific controls. It is not a report of results.

Use permitted source files at public HTTP(S) URLs. Keep files, order, instruction, explicit ratio, and resolution tier consistent. Neither model accepts base64 references through these reAPI endpoints^[1][2].

These illustrative room-and-chair requests need accessible replacement URLs. Send them to POST /api/v1/images/generations with your reAPI authorization header.

{
  "model": "flux-3-image",
  "prompt": "Use the first image as the room. Replace the chair beside the window with the chair from the second image. Keep the window, floor, lighting, and camera position unchanged.",
  "image_urls": ["https://example.com/room.jpg", "https://example.com/chair.jpg"],
  "aspect_ratio": "4:3",
  "resolution": "1k",
  "grounding": false,
  "safety_tolerance": 1,
  "n": 1
}

{
  "model": "gpt-image-2",
  "prompt": "Use the first image as the room. Replace the chair beside the window with the chair from the second image. Keep the window, floor, lighting, and camera position unchanged.",
  "image_urls": ["https://example.com/room.jpg", "https://example.com/chair.jpg"],
  "size": "4:3",
  "resolution": "1k",
  "n": 1
}

Grounding is disabled to focus on supplied references. This does not make internal behavior identical. Record the FLUX safety setting; GPT exposes no matching numeric control.

Before starting a FLUX 3 vs GPT Image 2 run, define what passes. For this example, a reviewer could check whether the correct chair was replaced, whether the replacement follows the supplied design, and whether the original window and camera framing remain acceptable. Set those requirements before seeing which model produced which file.

Use a simple record for each request:

Then run a separate layout round for FLUX 3 Image with a box table describing the target chair and important anchors. Preserve the original common-brief result. This second FLUX 3 vs GPT Image 2 comparison answers a narrower question: does the extra layout work help your acceptance criteria enough to justify preparing it?

Choose the number of repeated requests and a spending limit beforehand. Review outputs without model labels if practical, preserve failures, and report how many attempts were made. One selected FLUX 3 vs GPT Image 2 pair cannot establish a dependable success rate.

Record billing and safety alongside the result

Both default reAPI models bill per image according to resolution. FLUX 3 Image does not add separate charges for reference count, aspect ratio, or grounding. Their current quotes belong on the FLUX 3 Image model page and GPT Image 2 model page, where they can reflect current account pricing^[1][2].

For a practical FLUX 3 vs GPT Image 2 comparison, record total billed credits across the evaluation and divide by the number of outputs you accepted. This is a proposed measure, not a result. If no output passes, report that directly rather than presenting a cost per accepted image.

Safety settings are part of the recorded conditions. FLUX 3 Image's API accepts integer safety_tolerance values from 0 to 4 and defaults to 2. The reAPI playground permits only 0–1 and defaults to 1; values 2–4 require an API key. The default GPT Image 2 request exposes no adjustable moderation parameter^[1][2]. Do not assume that two different safety interfaces provide equivalent filtering.

Keep polling each existing task until you have its status. A new submission creates a new task; it is not a status check. This distinction matters when recording a FLUX 3 vs GPT Image 2 trial because accidentally submitting twice produces a different attempt count^[1][2].

Questions about FLUX 3 vs GPT Image 2

How should I choose FLUX 3 vs GPT Image 2 for layouts?

Evaluate FLUX 3 Image when normalized boxes are a required input. Its format supports placement and source-to-target editing; verify the result because boxes are guidance rather than clipping boundaries^[3].

Which accepts more reference images?

The default reAPI gpt-image-2 request accepts up to sixteen; flux-3-image accepts up to ten. If a task needs eleven separate reference files in one request, only the GPT default has the documented capacity. That does not establish a quality advantage for smaller sets^[1][2].

Can both use exactly the same request body?

The prompt, reference URL list, resolution, and n: 1 can be shared. Set the model ID separately, and use each model's supported ratio field. FLUX grounding and safety tolerance must be omitted from the default GPT request^[1][2].

Does FLUX 3 vs GPT Image 2 at 4K compare equal pixels?

Not automatically. Choose the ratio explicitly, download both results, and read their dimensions. reAPI's GPT reference documents different pixel sizes for different 4K ratios. A resolution label alone is insufficient for a pixel-matched comparison^[2].

Can I send a mask or request WebP with either default ID?

Neither default request exposes mask_url or output_format. OpenAI's native API documentation describes a broader feature set, but those native settings must not be copied into these default reAPI requests^[1][2][5].

Which is faster or better at preserving a character?

This article has no controlled measurements supporting a winner. Evaluate your own character references, instructions, delivered dimensions, and acceptance criteria across repeated attempts. Keep timing and visual judgments separate.

Is GPT Image 2 the same model as GPT Image 2.5?

No. OpenAI's current generation guide includes both, with a separate earlier-model section for GPT Image 2. The newer model examples and their extra quality settings should not be treated as GPT Image 2 parameters. This article uses the exact default ID gpt-image-2^[4].

Choose the control your next task requires

For a FLUX 3 vs GPT Image 2 decision, identify the requirement that could rule a model out before generating. It might be eleven references, a box-based layout, a particular ratio, or a resolution tier. Then test the remaining choices against a small, written acceptance checklist. Save the request, output, dimensions, timing, and charge together. That gives your next decision usable evidence instead of another attractive image with an unknown setup.

References

1. reAPI. FLUX 3 Image generation and editing reference. Implementation and documentation checked October 2026. reapi.ai/docs/flux-3-image
2. reAPI. GPT Image 2 default model reference. Default request fields checked against the model schema October 2026. reapi.ai/docs/gpt-image-2
3. Black Forest Labs. Bounding boxes with FLUX 3 Image. Retrieved October 2026 from docs.bfl.ai/flux_3/flux3_image_bounding_boxes
4. OpenAI. Image generation: Earlier GPT Image models. Retrieved October 2026 from developers.openai.com/api/docs/guides/image-generation
5. OpenAI. Create image: API reference. Retrieved October 2026 from developers.openai.com/api/reference/resources/images/methods/generate