ChatGPT Image 2.5 · Infographics, Consistency and PNG Tests

Quick Summary

• Credit: the embedded video, examples and comparisons belong to Futurepedia. JQ VISIONS adds a practical framework, official documentation and editorial prompts.
• In Futurepedia testing, ChatGPT Image 2.5 produced the most informative poster about the American Revolution and had fewer visible errors than compared generations of Gemini, Nano Banana 2, Nano Banana Pro, and GPT Image 2.
• The conclusion that it is the best model for many use cases is the creator's assessment, based on this set of experiences. It is not an independent benchmark, repeated with seeds, costs and blind evaluation.
• The new image area shown in the video includes prompt templates, a trends page, Sketch, image comments, format change, and background removal. Availability may vary during rollout.
• The most practical gains appear in dense text, preservation of elements during edits, character sheets, style transfer from references and PNGs with complex transparencies.
• Consistency is not resolved in all cases. In very dense images, correcting two words can change typography, spacing or other text that was already correct.
• The official documentation defines Flare as the fast model for everyday generation and Sunburst as the most capable option for generating and editing with greater precision.
• The traditional problems remain present: occasionally strange anatomy, residual text, incorrect times on clocks, poorly linked arrows and coordinates, noise in repetitive patterns, and details that resist multiple corrections.
• The safest workflow is to research and structure first, generate later, correct one element at a time, and validate text, facts, geometry and transparency outside the model before publishing.

Credit, verdict and test method

Futurepedia published this test on September 10, 2026. The video goes through generations in ChatGPT and the API, compares results with GPT Image 2, Nano Banana 2, and Nano Banana Pro, and ends with examples where the new model still fails.

The creator's verdict is strong but limited: for many real-world use cases, this has become their primary image model. The wording matters. The video does not demonstrate that ChatGPT Image 2.5 beats any model in any task, only that it was the best option in the set of challenges presented.

Equivalent seeds, multiple rounds per prompt, standard times, total cost, or blind evaluation are not provided. Comparisons are useful as a creator test and workflow discovery, not as a scientific ranking.

In this article, everything describing a specific generation comes from the video. The differences between Flare and Sunburst come from the official OpenAI pages. The prompts at the end were written by JQ VISIONS from the techniques demonstrated and are not transcriptions of Futurepedia's private prompts.

The challenge of complexity: a lot of information in one image

The first test asks for a detailed poster for a History room about the American Revolution. The result brings together chronology, battles, portraits, quotes and a map. Futurepedia claims to have reviewed the text without finding an obvious error, although it acknowledges that it did not visually verify the historical fidelity of all the people depicted.

In the comparison shown, Gemini returned duplicate or illegible text and a composition that blocked students. Nano Banana 2 and Nano Banana Pro improved the organization, but were lower in density and accumulated more errors. GPT Image 2 remained strong in text, but version 2.5 condensed more information.

The point is not to always ask for more content. A dense image is only good when it maintains a clear reading order. Title, context, chronology, secondary blocks, caption and sources need distinct visual levels. Otherwise, the generation becomes an impressive mosaic that no one can use.

For educational or commercial material, a visual review is not enough. Copy the generated text into a checklist, confirm names and dates in independent sources, and complete typography in an editor when content is critical.

Localized Templates, Sketch, and Comments

The video shows an updated Images area in the sidebar. The templates work like presets that fill in a prompt, some with instructions to ask clarifying questions. There is also a trends section. They are starting points, not visual references or quality guarantees.

Sketch allows you to draw a simple composition and add a style instruction. In the first test, a cartoonish scribble generated a more polished illustration. In the second, a skater between skyscrapers maintained colors and intention, although it did not exactly reproduce the drawn perspective.

Futurepedia considers Sketch to be more useful during editing than when creating from scratch. You can draw on the image, mark areas and send various comments. However, the model does not always understand which object each annotation belongs to. Describing the change in text produced a better correction in one of the examples.

The lesson is to use the sketch as a spatial map and not as an absolute specification. Indicate what is to the left, center and back, define proportions and write which relationships cannot change.

Background removal and PNGs with true transparency

The Remove background option cut out a skater well and preserved part of the brightness, something that according to the creator was less reliable in the previous model. The most demanding test came with a jellyfish, water and dragonfly wings, where transparency exists within the object itself.

The result showed semi-transparency in the jellyfish's bell and tentacles, water and wings. This is more useful than a binary clipping, because it allows you to compose the element on new backgrounds without an opaque halo.

The automatic button did not win in all cases. For a complex image, Futurepedia achieved better results by asking for the transparent background directly in the prompt. It is worth specifying that the file must maintain reflections, glow, glass, water, fine hair and anti-smoothed edges.

Before using the PNG, place it over black, white, and a saturated color. This way you find color contamination, holes, halos and regions that appear transparent but were simply painted with a checkerboard pattern.

Consistency between editions: the biggest gain and the biggest pitfall

The most life-altering benefit to everyday work is the ability to change one part without rebuilding the rest. The video shows changing clothes in a thumbnail, small movements and infographic edits with better preservation of the composition.

This helps those who generate separate layers and finish the layout in Photoshop or another tool. Futurepedia does not normally ask for a complete thumbnail for the model. Produce face, elements and variations separately, then compose manually. The model starts to function as a supplier of consistent parts.

The promise is broken when the image already contains too much information. By correcting small errors on a poster, a generation resolved the highlighted words, but changed other texts. A subsequent round corrected the content, although it also changed font and spacing.

The most robust method is to edit one variable at a time. List concrete invariants: framing, position, scale, palette, lighting, face, clothing, approved text and margins. Then compare the new version overlaid with the previous one, not just side by side.

Consistent characters and style transfer

In the portraits and character sheets presented, the model preserved the face, details, palette, texture and clothing through various angles, expressions and light conditions. The creator considers creating character sheets pretty much solved for the examples shown.

This does not equate to mathematically locked identity. A front sheet, profiles, back, close-up, and expressions adds context, but each new scene must be compared with approved references. Scars, accessories, distance between eyes and fabric patterns are good control points.

Style transfer also improved in testing. Images with unusual visual languages, originally created in Midjourney, served as references for new characters and scenarios without losing the general treatment.

Only use your own or authorized images for reference. When you don't have rights to a work, describe general features such as paper cutout, risograph print, soft side light, or limited palette, rather than asking for an imitation of a living artist, brand, or specific work.

Photorealism: better faces, still imperfect physics

The video tests concerts, crowded markets, children playing football and an octopus bartender surrounded by creatures. Faces appear more coherent and varied than in previous generations, preventing the entire crowd from looking like the same person.

The composition can still fail semantically. At a concert, people were facing the wrong direction. In another example, a leg was deformed. The image may appear photographic at first glance and still be physically wrong.

Futurepedia compared the octopus bartender between GPT Image 2, Flare, Sunburst, Nano Banana 2 and Nano Banana Pro. It preferred Sunburst for its fidelity and realism, although even that result had ambiguous liquid detail on a tentacle.

A convincing photograph needs three separate reviews: anatomy and contact, coherence of light and lens, and logic of action. Zoom in, look for hands, feet, shadows, reflections, eye lines, and objects that cross over other objects.

Flare versus Sunburst: the official difference

OpenAI documents two models. GPT-Image-2.5 Flare is the fast, high-quality option for everyday generation. GPT-Image-2.5 Sunburst is touted as the company's most capable model for generation and editing, recommended when editing accuracy is a priority.

Both accept text and image as input and return an image. The official pages indicate low, medium, high, xhigh, max and auto qualities. In the API, you can explicitly choose the model.

In ChatGPT shown in the video, the interface does not identify which of the two performed each generation. Futurepedia infers that there is routing as per the request, but says it has not found documentation that confirms this behavior. It should be treated as an observation by the creator, not as a product fact.

Rule of thumb: use Flare to explore composition, color, and multiple directions. Swipe Sunburst to your chosen frame for fine editing or when details are more valuable than speed. Compare in each task, because the most advanced model does not guarantee the preferred image in all seeds.

Research first: research before generating

In several requests on ChatGPT, the experience shown researched before creating the image. An infographic about video models took more than four minutes, a comparison of Toyota Sienna versions consulted four websites and a news panel worked for more than eight minutes.

According to Futurepedia, these chat generations had more current and verified information than versions made directly by the API, where Flare and Sunburst created strong layouts but did not carry out the same independent research.

The gain comes from the sequence, not just the image model: collecting sources, synthesizing, structuring and converting the synthesis into a visual briefing. To repeat the pattern with control, first ask for a table of facts and references. Approve it before starting the generation.

Even with research, the final image is not a reliable database. Text can be rewritten during rendering. Save the source table and validate each label after generating.

Where ChatGPT Image 2.5 Still Fails

The most revealing challenge was a map of the United States with national parks, locations, arrows and retro posters. The result looked sophisticated, but several positions and connections were wrong. Image models can draw the appearance of a diagram without fully understanding the topology it represents.

Futurepedia found illegible text on a conveyor belt, noise in repetitive textures, a clock that didn't reach 8:22 despite several corrections, and elements that didn't change even after a drawn note.

The template also tends to add unsolicited text. The words may look nice and even readable, but they introduce statements, quotes or decoration that the brief never approved. Include the rule without additional text and confirm the four corners of the image.

Celebrities and intellectual property may be denied or treated inconsistently. In addition to the service's policies, there are image, brand and author rights. Choose authorized people, original characters and references that you can legally transform.

How Futurepedia constructed the test presentation

The tool used to show and compare images was not a ready-made product. Futurepedia says it created it with Codex while testing the generator, sending requests, experimenting with the results and adding features in short cycles.

The small site allowed you to navigate between images, go back to previous versions and rearrange examples. At the end of the research, the presenter already had a useful interface to record the video.

This detail contains a good editorial lesson. When a study produces dozens of files, it is worth building a viewer with side-by-side pairs, filters by model, prompt, date, seed, cost and approval status. The presentation system becomes part of the research method.

For a serious comparison, also preserve the original files, metadata and generation conditions. A beautiful gallery with no history makes it impossible to understand whether we are seeing the first attempt or the best among twenty.

Recommended workflow for production

Start by defining the output: channel, size, audience, message, mandatory text, authorized references and prohibited elements. If there are facts, investigate and approve a table before drawing.

Make three or four quick directions with Flare or the generation available on ChatGPT. Choose a composition and turn it into a reference. Only then request a high-precision version or a Sunburst edition when working via the API.

Lock invariants in writing. For characters, use a reference sheet. For products, use photographs from various angles. In infographics, provide the final text and grid. In PNG, describe the semi-transparent zones.

Correct one variable per round. Overlay versions to detect drift. Complete text, logos, coordinates and alignments in a deterministic tool, then export and validate at the actual publication size.

Record model, quality, prompt, inputs, date, time, cost and decision. This way you can know if the new model has improved the process or just produced an impressive image in a happy attempt.