GPT-Image 2.5 vs GPT-Image 2

A GPT-Image 2.5 vs GPT-Image 2 decision comes down to three questions that marketing pages blur together: which model is actually behind the name, how output size and quality behave, and what a finished image costs. The short answer: on OmniaKey all three routes cost $0.068 per call, the extra 2.5 quality tiers do nothing on this route, and the real difference is editing behaviour.

OpenAI now splits its newer image family into two model IDs, gpt-image-2.5-flare and gpt-image-2.5-sunburst. OmniaKey publishes both official ids next to gpt-image-2; the single gpt-image-2.5 alias is gone. The three routes differ in ways that change real workflows: quality headroom, size handling, editing behaviour, and the price you see per call.

GPT-Image 2.5 vs GPT-Image 2: the short answer

The practical rule: with one price for all three routes, pick by what you are doing rather than by cost. GPT-Image 2 for volume, drafts and anything that needs OpenAI Batch; Flare or Sunburst when an edit has to leave everything else untouched.

Three visual cases we actually ran

The claims above are about documented interfaces. To see what the two OmniaKey routes actually produced, we ran three deliberately visual cases on September 12, 2026. Each comparison used the same prompt or the same input image, n=1, the same requested dimensions, and a fresh media task. These are n=1 observations, not a benchmark or a claim about every random seed.

Case 1: a complex scene at the same setting

Prompt summary: a glass greenhouse carved into a sea cliff, three red lanterns, a copper telescope, one white sailboat, wet basalt, and a stormy blue-hour ocean. Both requests used high and 1536x1024.

GPT-Image 2 / high / 1536x1024

GPT-Image 2.5 / high / 1536x1024

In this run, GPT-Image 2.5 followed the exact-count constraint: it hung three red lanterns from the terrace. GPT-Image 2 placed four lanterns on posts instead. Both included the telescope and one sailboat; 2.5 also made the stacked greenhouse terraces easier to read, while 2 produced a stronger cliff crop and more aggressive waves. Our conclusion for this case: 2.5 was the better first candidate for a complex, client-facing composition because it passed the explicit object-count check. This is one paired result, not proof of a universal quality win.

Case 2: product material and the 2.5 quality ceiling

Prompt summary: a luxury emerald glass perfume bottle, brushed brass cap, black obsidian, water, warm key light, cool rim light, physically plausible refraction, and condensation. GPT-Image 2 used high; GPT-Image 2.5 used max on purpose, because max is one of the new 2.5 quality levels.

GPT-Image 2 / high / 1024x1536

GPT-Image 2.5 / max / 1024x1536

GPT-Image 2.5 produced the more controlled bottle facets and highlight boundaries in this pair, while GPT-Image 2 gave the rock and water a more dramatic texture. Our conclusion for a product hero: pay for 2.5 max when the bottle geometry and reflective material are the deliverable; keep GPT-Image 2 high in the exploration loop when mood and texture matter more. Because the quality settings differ, this is a ceiling test, not a fair same-setting score.

Case 3: precision colorway edit

We fed the same small JPEG source to both routes and asked them to change only the emerald liquid and glass to ruby red while preserving the bottle silhouette, brass cap, droplets, camera, lighting, and obsidian slab. Both used high and 1024x1536.

Same source image

GPT-Image 2 / edit / high

GPT-Image 2.5 / edit / high

This was the least dramatic difference: both edits succeeded, preserved the layout, and produced a convincing red glass variant. Our conclusion for this one edit: OmniaKey's 2.5 route did not show a decisive advantage. Now that both official ids are published separately, re-run this case against gpt-image-2.5-flare and gpt-image-2.5-sunburst before deciding which one carries your edit pipeline.

The visual run does not measure latency or reliability. One separate gpt-image-2.5 high generation attempt timed out upstream, so those metrics are deliberately excluded rather than turned into a claim.

What OpenAI actually changed in 2.5

The first thing to unlearn is that "GPT-Image 2.5" is one model. OpenAI's catalog lists two:

• gpt-image-2.5-flare is described as the fastest option for high-quality, everyday image generation.
• gpt-image-2.5-sunburst is described as the most capable model for image generation and editing, aimed at workflows where editing precision matters most.

Both use the default snapshot dated 2026-09-08 and both accept text and image inputs and return images. GPT-Image 2's default snapshot is older, gpt-image-2-2026-04-21.

The second change is quality range. GPT-Image 2 tops out at high. The 2.5 models add xhigh and max, and every setting defaults to auto. Higher quality means more image output tokens, which is where the cost difference between models and settings actually lives.

Both generations expose the same two image endpoints: v1/images/generations for text-to-image and v1/images/edits for editing and reference-image work. Neither is a chat model. OpenAI notes that GPT Image models may require API Organization Verification before use, so confirm access in the target project before promising a launch date.

There is one regression to know about: OpenAI's model pages mark Batch as supported for gpt-image-2 but not for either 2.5 model. If your pipeline batches thousands of images overnight at a discount, the older model currently has the feature and the newer one does not.

How OmniaKey exposes both models

OmniaKey does not proxy the OpenAI Images API shape directly. gpt-image-2, gpt-image-2.5-flare, and gpt-image-2.5-sunburst all use the same asynchronous media task lifecycle: create a job with POST /v1/media/tasks, poll GET /v1/media/tasks/{id}, then download the signed asset URL. That is true even though OpenAI's own image calls are synchronous.

Two consequences follow for anyone porting an OpenAI script:

• There is no inline base64 response. Poll until the task reports succeeded or failed, then fetch the asset.
• Multi-image n is not available. OmniaKey requires n to be 1, so ten images means ten tasks. OpenAI's Image API can return several images in one request. The task-based model also replaces streaming and partial images with status progress.

Idempotency is handled with the Idempotency-Key header. Replaying the same key with the same body returns the original task, and reusing a key with a different body is rejected rather than silently creating a second job.

Output size: 1254x1254 versus exact dimensions

The most visible behavioral difference is sizing, and it is easy to miss because both models accept the same size values.

OpenAI documents recommended sizes of 1024x1024, 1536x1024, and 1024x1536, and allows custom WIDTHxHEIGHT strings where both edges are multiples of 16, the aspect ratio stays between 1:3 and 3:1, no edge exceeds 3840 pixels, and total pixels land between 655,360 and 8,294,400. Resolutions above 2560x1440 are marked experimental.

On OmniaKey every GPT Image route shares one set of sizing rules — the 2.5 ids do not return "more exact" pixels than the older model:

Our 2026-09-17 measurements on the two 2.5 ids showed the 4K file carries only 2K-grade effective detail: its high-frequency spectrum, half-resolution roundtrip residual and pixel autocorrelation all match a deliberately upscaled control, not a native 1K/2K render. Treat 4K as a bigger canvas and always read the real dimensions from the downloaded file.

That still matters anywhere downstream code trusts metadata: a fixed thumbnail grid, a print template, a layout with a hard width, or a storage estimate.

Quality settings and what they cost

Quality is not a cosmetic toggle. It changes how many image output tokens the model spends, and output tokens are the priced part of image generation.

OmniaKey's recorded results for GPT-Image 2 show the spread clearly: the same prompt returned roughly 408, 1056, and 6240 output image tokens at low, medium, and high. That is more than a 15× swing between the cheapest and most expensive setting on one model.

OpenAI publishes per-image estimates for GPT-Image 2 that make the same point in dollars:

Those figures are OpenAI's own estimates, not an OmniaKey quote, and they exclude text input tokens. For 2.5, OpenAI explicitly warns that equal token rates do not mean equal cost per image, because token consumption can differ by model and quality setting. OpenAI's published token calculator covers GPT-Image 2 and states that it does not estimate GPT-Image 2.5 consumption.

On OmniaKey those two tiers do not exist: our 2026-09-17 measurements show xhigh and max render as medium, with exactly the same output image tokens, so send only low, medium or high. The catalog charges one flat price per call, so high still spends about fifteen times the tokens of low upstream — but trying it costs no more.

Price: flat per call versus token billing

This is where the two models separate most sharply for a buyer.

OpenAI's standard token rates are identical for gpt-image-2, gpt-image-2.5-flare, and gpt-image-2.5-sunburst: $5 per million text input tokens, $8 per million image input tokens, $30 per million image output tokens, with cached input at $1.25 and $2. Only GPT-Image 2 appears in the Batch table, at a discount.

OmniaKey does not pass those token rates through. The live catalog lists a flat per-call price:

At a flat $0.068, one hundred accepted images cost about $6.80 on OmniaKey, before any retries. On OpenAI's estimates, one hundred high-quality 1024x1024 images cost roughly $21.10 at $0.211 each, and one hundred low-quality images cost about $0.60. A flat gateway price is therefore most attractive at high quality and least attractive at low quality, which is the opposite of how per-token pricing scales.

That is also why the live catalog, not this article, owns the number. Media pricing depends on which channels an operator has connected, which quality tiers they expose, and when they last repriced. Check the model page before you budget, and remember that a failed task should be refunded rather than billed — confirm that behavior for your account.

Verify the GPT-Image 2 model page, the GPT-Image 2.5 model page, and the live price table before you budget.

Editing: reference images and the 1 MiB limit

Both models support generate and edit. On OmniaKey, an edit is an edit operation with an input image, and reference-image work uses input.image or input.images.

Two limits shape what you can actually build:

• Inline base64 only. The input image must be a data URL or an object with data and mime_type. The whole request, including that payload, is capped at 1 MiB, so the route suits small sources, masks, or thumbnails rather than full-resolution camera files.
• Edits consume input tokens. OmniaKey's recorded edit task reported 194 input image tokens, confirming that the source image enters the model's reasoning instead of being ignored. OpenAI also notes that gpt-image-2 always processes image inputs at high fidelity and does not allow changing the input_fidelity parameter, which can raise input token cost on reference-heavy edits.

For editing precision, the official split matters. OpenAI positions gpt-image-2.5-sunburst specifically for workflows where editing precision matters most. If your job is "change only this region and keep the camera angle, lighting, and product geometry intact," that is the model to test first; with one price across all three routes there is no premium left to justify.

Which model should you use?

• Use GPT-Image 2 for prompt exploration, storyboards, social drafts, thumbnails, and any high-volume loop where a rejected image is cheap to regenerate.
• Use GPT-Image 2.5 for final or client-facing assets, for text and layout accuracy, and for edits that must preserve everything outside the change.
• Use GPT-Image 2 for volume. All three routes cost $0.068 per call, so the older model is no longer the budget option. Its advantages are OpenAI Batch and a longer track record.
• Use GPT-Image 2.5 Flare or Sunburst for edits. Both ids accept a reference image and are positioned for editing precision; size handling is identical to the older model, so do not switch expecting a different canvas.
• If you need OpenAI Batch discounts, plan around GPT-Image 2, because the 2.5 models do not list Batch support.
• If you need many images from one request, plan around neither. OmniaKey requires n to be 1, so parallelism happens at the task level.

A useful default is to generate at medium on GPT-Image 2, shortlist, then regenerate only the winners at high or higher on GPT-Image 2.5. You spend the expensive model's tokens on images you already decided to ship.

How to call both through OmniaKey

The request body is the same shape for either model. Only the model value changes.

curl --fail-with-body https://api.omniakey.com/v1/media/tasks \
  -H "Authorization: Bearer your-omniakey-api-key" \
  -H "Content-Type: application/json" \
  -H "Idempotency-Key: hero-image-001" \
  -d '{
    "model": "gpt-image-2.5-flare",
    "type": "image",
    "operation": "generate",
    "input": {"prompt": "a paper boat on a quiet lake at sunset"},
    "parameters": {"size": "1536x1024", "quality": "high"}
  }'

The response is 202 Accepted. Poll the returned task id until the status is succeeded, then download the signed URL before it expires. The image itself is retained for 24 hours and the signed link for roughly an hour, so a poll refreshes the link when needed.

For an edit, keep operation and swap the input to an inline image:

BASE64=$(base64 -i source.png | tr -d '\n')

curl --fail-with-body https://api.omniakey.com/v1/media/tasks \
  -H "Authorization: Bearer your-omniakey-api-key" \
  -H "Content-Type: application/json" \
  -H "Idempotency-Key: hero-image-edit-001" \
  -d '{
    "model": "gpt-image-2.5-flare",
    "type": "image",
    "operation": "edit",
    "input": {
      "prompt": "replace the background with a clean studio wall",
      "image": {"data": "'"$BASE64"'", "mime_type": "image/png"}
    },
    "parameters": {"size": "1024x1024"}
  }'

The full parameter reference, output-format options, and error table live in the image generation docs. Read the real dimensions and MIME type from the finished task instead of assuming PNG.

Frequently asked questions

Is GPT-Image 2.5 better than GPT-Image 2?

In OpenAI's documentation it adds xhigh and max, and Sunburst is positioned for editing precision. On OmniaKey those tiers render as medium and size handling is identical to the older model, so 2.5's real difference from GPT-Image 2 is its editing orientation, not a working quality tier. No independent quality benchmark is claimed here.

What are GPT-Image 2.5 Flare and Sunburst?

They are the two official OpenAI model IDs behind the 2.5 generation. Flare is described as the fastest option for everyday generation; Sunburst is described as the most capable for generation and editing. OmniaKey publishes both separately with the same parameters and price, but outputs are nondeterministic and are not expected to be byte-identical.

Why does GPT-Image 2 return 1254x1254 for a 1024x1024 request?

It is the normalisation every GPT Image route shares: 1024x1024, or an omitted size, returns 1254x1254, and that is the only size rewritten. The fix is to read the real width and height from the downloaded file and not from request metadata; switching to 2.5 does not change this.

Does GPT-Image 2.5 cost more?

Through OmniaKey, not any more: the live catalog lists $0.068 per call for gpt-image-2.5-flare, gpt-image-2.5-sunburst, and gpt-image-2. On OpenAI's direct token rates the two generations are priced the same per token, but token consumption changes with model and quality, so the per-image result can still differ.

Can I generate multiple images in one request?

Not through OmniaKey. The media task API requires n to be 1, so each image is a separate task. OpenAI's Image API accepts an n parameter, and GPT-Image 2 supports Batch, but neither capability is currently part of the OmniaKey media route.

Should I switch everything to GPT-Image 2.5?

No. All three routes cost the same, so there is no reason to migrate everything: leave edits and final assets to 2.5, and volume and drafts to GPT-Image 2. The only real constraint is that OpenAI Batch exists only on the older model.

Primary sources

• OpenAI: GPT-Image 2 model page
• OpenAI: GPT-Image 2.5 Flare model page
• OpenAI: GPT-Image 2.5 Sunburst model page
• OpenAI: image generation guide
• OpenAI: API pricing
• OmniaKey: image generation docs
• OmniaKey: live image catalog

Evidence disclosure: official model IDs, quality options, endpoints, token rates, and per-image estimates were read from OpenAI's documentation on September 12, 2026; the size tiers, quality behaviour and per-call price of the two 2.5 ids were re-measured on September 17, 2026. Size behavior, token observations, and file sizes come from OmniaKey's recorded acceptance results and the three paid visual cases shown here. The visual cases are single samples, not an independent benchmark, latency test, or cost-versus-quality study. Model availability, aliases, and prices change; confirm the live catalog before you budget.