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 is that GPT-Image 2.5 is a real generation upgrade for final assets and precise edits, while GPT-Image 2 remains the more economical default for drafts and volume.

OpenAI now splits its newer image family into two model IDs, gpt-image-2.5-flare and gpt-image-2.5-sunburst. OmniaKey lists a single gpt-image-2.5 route next to gpt-image-2, and the two differ in ways that change real workflows: quality headroom, exact requested dimensions, editing behavior, and the price you see per call.

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

The practical rule: start with GPT-Image 2 when the image is disposable or you are exploring a prompt. Move to GPT-Image 2.5 when the output has to be final, when the size must be exact, or when an edit has to land without redrawing everything else.

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. Both gpt-image-2 and gpt-image-2.5 are exposed through 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.

OmniaKey's documented parameter set is narrower: 1024x1024, 1536x1024, and 1024x1536. Within that set, the recorded acceptance results show the two models do not return the same pixels:

GPT-Image 2 normalized a 1024x1024 request into a 1254x1254 file during OmniaKey's smoke test, while GPT-Image 2.5 returned the requested size. OmniaKey's documentation therefore warns integrators to read the real dimensions from the downloaded file instead of assuming them.

That matters anywhere downstream code trusts metadata: a fixed thumbnail grid, a print template, a layout with a hard width, or a storage estimate. If an exact dimension is a requirement, GPT-Image 2.5 is the safer route, and you should still validate the returned file header.

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.

The practical takeaway is to treat xhigh and max as production-only settings, not defaults. Compare high, then xhigh, then max on your own prompts and keep the first setting that passes your review.

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 $0.05 and $0.10, one hundred accepted images cost about $5 or $10 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, and it is the case where paying double per call is easiest 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 when exact pixels are not required and the budget is tight. It is the cheaper route on every plan currently listed.
• Use GPT-Image 2.5 when a requested size must be honored. It returned the exact dimensions in the recorded test.
• 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",
    "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",
    "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?

On the documented dimensions, yes: it adds xhigh and max quality, returns the exact requested size, and its Sunburst variant is positioned for editing precision. That is not the same as "better images at every setting," because no independent quality benchmark is claimed here. The honest answer is that 2.5 raises the ceiling while GPT-Image 2 remains sufficient for many jobs.

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 a single gpt-image-2.5 alias, so confirm which upstream model your channel maps to.

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

OmniaKey's recorded smoke test observed exactly that normalization. The documented fix is to read the real width and height from the downloaded file and not from request metadata. GPT-Image 2.5 returned the requested size in the same test series.

Does GPT-Image 2.5 cost more?

Through OmniaKey, yes: the live catalog listed $0.10 per call for gpt-image-2.5 and $0.05 for 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. Switch the jobs where exact size, editing precision, or final quality matter, and keep the cheaper model for exploration and volume. Paying double for a draft you will delete is the simplest way to waste an image budget.

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. Size behavior, token observations, and file sizes come from OmniaKey's own recorded acceptance results. No independent quality benchmark, latency test, or cost-versus-quality study was performed. Model availability, aliases, and prices change; confirm the live catalog before you budget.