GPT Image 2 vs 2.5 · Flare and Sunburst in 15 Tests

Quick Summary

• Credit: the video, fifteen tests and visual assessments belong to Brendan Jowett. JQ VISIONS provides the official-source research, methodological caveats and supplemental prompts.
• OpenAI documents Flare as the fastest model for everyday high-quality generation and Sunburst as the most capable model for generation and editing, especially when accuracy is a priority.
• In the poster test, Sunburst presented Brendan's preferred typographic composition. Flare and Sunburst distributed the title better than GPT Image 2, although this rating involves visual taste as well as literal fidelity.
• In the run shown, GPT Image 2 took about 120 seconds, Flare 40 and Sunburst 65. The three cases cost approximately 21 cents, according to the creator.
• The speed and cost comparison is not perfectly equivalent: GPT Image 2 ran at high, while the 2.5 models used max. Additionally, a single sample does not represent average latency.
• Sunburst got the most correct results in wiring diagram and switch editing. Flare left a strange space and GPT Image 2 removed the component instead of opening it.
• Flare won the casual iPhone photography example in the creator's assessment, showing that the most capable model doesn't always produce the most natural result for every aesthetic.
• Fruit, mechanical assembly, and other scenes revealed cleaner backgrounds in the 2.5 models. Brendan found GPT Image 2's texture pattern to be much less visible, but this is an observation of those outputs, not a guaranteed fix.
• In the architectural edits, Flare and Sunburst better preserved camera, lighting, and geometry while changing only the window. The test illustrates the official localized editing improvement.
• The best decision is not to pick an absolute winner. Use Flare for exploration and volume, Sunburst for difficult briefs and critical edits, and measure cost per approved asset rather than cost per generation.

Fifteen tests, three models and one methodological limit

Brendan Jowett generates fifteen images with GPT Image 2, GPT-Image-2.5 Flare and GPT-Image-2.5 Sunburst. Cases cover graphic design, photography, product, diagrams, multilingual typography, character continuity, architecture, visual physics, mechanical assembly, and surreal synthesis.

Variety is useful because a model can be strong on aesthetics and weak on technical relationships. It also prevents the decision from relying solely on a pretty portrait. Still, each example appears to correspond to one run per model, without multiple seeds or blind evaluation.

The video is an exploratory comparison, not a statistical benchmark. Composition preferences belong to the creator; symbol correctness can be externally validated; and observed time depends on quality, load, platform and size. This article retains these differences.

To use the results as a production decision, repeat the orders that represent your work, apply criteria before viewing the outputs, and calculate the approval rate. Fifteen different prompts are a good map, but they are no substitute for multiple runs of the same prompt.

Flare and Sunburst according to official documentation

OpenAI Docs describes GPT-Image-2.5 Flare as the fastest model for everyday high-quality generation. Accepts text and image, produces image and supports low, medium, high, xhigh, max and auto.

GPT-Image-2.5 Sunburst is touted as the most capable model for generation and editing. The official recommendation is to use it when editing accuracy is the priority. It supports the same quality levels and can be chosen from the Image API or the Responses API image tool.

Both have documented fees of $5 per million text tokens in, $8 per million image tokens in, and $30 per million image tokens out. Cached input has lower rates.

OpenAI states that these fees per token correspond to those of GPT Image 2, but warns that the previous model's calculator does not estimate consumption of 2.5. Equal fee does not necessarily mean equal final cost, because token quantity, quality, size and number of attempts may vary.

120, 40 and 65 seconds: what the test really measures

In the poster shown, GPT Image 2 takes about 120 seconds, Flare 40 and Sunburst 65. Brendan records approximately 21 cents for each generation. It's an impressive result, especially for Flare, but it should be read as a one-off measurement.

There is a configuration difference: GPT Image 2 uses high, the maximum available in this model, while Flare and Sunburst use max. Thus, the experiment compares the best selectable level of each model, not the same nominal level.

There are also no multiple runs to calculate median, spread, or gaps. Network, queue, platform and caching can all affect latency. For a team test, run at least five samples per case, alternate the order of the models, and record the time until the file is available.

The most useful economic indicator is cost per approved asset. Add generations, edits, upscale, retouching and revision. A model that is more expensive per attempt may be cheaper if it reduces rejections and manual labor.

Swiss poster: correct text is not enough

The first order creates a square exhibition poster, on warm white paper, with the title Made of Light and the subtitle A Study in Color. All three templates include content but distribute typography in different ways.

GPT Image 2 compresses too much text into one line. Flare and Sunburst create a more breathy hierarchy. Brendan prefers Sunburst for its composition and the way it divides the title, a decision that combines fidelity, readability and taste.

In a design test, separately evaluate spelling, hierarchy, alignment, margins, contrast and coherence with the requested style. A more visually beautiful poster may disobey a mandatory grid; a literal layout can be correct and still look weak.

Edit just the title without rebuilding the poster

Brendan asks to replace only Made of Light with Shaped by Light. The three models carry out the change without any obvious problems. This simple case doesn't create a big separation between versions.

Correct evaluation does not end with the new text. Compare position, apparent font, kerning, scale, paper texture, colors and all the elements that should remain. Use an overlay or pixel offset when the asset is important.

OpenAI documents partial or full editing via the Image API and multi-turn editing via the Responses API. Even when the change appears localized, the process remains probabilistic and must be validated.

Baker, fruit and perfume: physical detail versus artificial perfection

In the baker's photo, all three results look professional. GPT Image 2 represents the three-strip braid worse. Sunburst gets Brendan's preference because the action and composition make the braiding process more readable.

The still life with fruit highlights another difference. In GPT Image 2, apples and pears look too perfect. Flare and Sunburst add points, irregularities and texture that the creator associates with greater realism. Flat bottoms also have less artificial pattern.

In perfume bottles, the overall difference is small. Sunburst represents the inner tube entering the liquid, Flare shows it incompletely and GPT Image 2 omits it. It's a tiny detail, but decisive when the image needs to explain the construction of a product.

These cases show why detail must be evaluated in context. Organic imperfection helps fruit; accuracy of components helps product; Too much microtexture can make a casual photograph less convincing.

Electrical diagram: plausible does not mean correct

The order specifies a closed series circuit with battery on the left, closed switch on top, resistor on the right, lamp on the bottom, standardized symbols and exact labels. At first glance, the three diagrams appear similar.

Validating the symbols changes the result. GPT Image 2 draws a figurative light bulb instead of the requested symbol. Flare improves the symbology, but the switch does not appear fully closed. Sunburst best represents the components and continuity of the circuit according to the research carried out by the creator.

In the next edition, the switch must open and the lamp go out, preserving positions, wires, labels, visual weights and background. Sunburst fulfills the request. Flare leaves a strange interrupt and GPT Image 2 removes the interrupt.

A diagram can look clean and still be technically wrong. Validate nomenclature, symbols, topology, and status with documentation or an expert. For engineering, medicine, or security, use generation as a draft, never as an authority.

Multilingual lettering and cartoon continuity

In multilingual signs, Brendan finds Sunburst to be more natural in the background and subject. GPT Image 2 presents a texture and tonal treatment that the creator recognizes as typical of the generated image. Flare comes close, but comes in second in that sample.

For lettering, aesthetics are not enough. Confirm each character with a reliable speaker or source, check writing direction and avoid publishing plausible but incorrect words. The visual test does not replace linguistic review.

In cartoon character continuity, all three models work well. Flare and Sunburst use more vibrant colors, while GPT Image 2 adds more detail. Brendan does not claim deep experience in this category and recognizes that more detail can counter the simplicity of the style.

The winner depends on the brief. A children's series may require stable and simple forms; an editorial illustration can benefit from texture. Evaluate identity, silhouette, palette and proportions before rewarding visual richness.

Architecture: preserve chamber while window changes

The three models generate a plausible architectural corridor. The really discriminatory test comes later: transforming the tall, arched window into a circular window, maintaining geometry of the corridor, doors, floor pattern, camera position and light.

GPT Image 2 changes the angle and lighting. Flare and Sunburst preserve the image much better and essentially swap the requested element. It is the clearest example of the editing improvement located in the comparison.

To measure preservation, mark vanishing lines, fixed points, and luminance before editing. Compare frames by overlay. A visually similar image may have enough drift to prevent continuity between scenes, campaign variations or presentation to a client.

Casual photography: Flare wins precisely because it looks less produced

In the iPhone natural photography application, the three outputs seem credible. Brendan chooses Flare because the text and background appear to be captured by the camera, while Sunburst appears to be the most generated of the three in that example.

This result is a useful antidote to the idea that greater ability always wins. A precision-oriented model can produce too much visual intent when the request seeks randomness, imperfection, and banality.

The creator himself notes that new executions could change the winner. For casual photography, evaluate uneven exposure, imperfect framing, plausible depth, movement, sensor noise, and secondary text. Do not confuse deliberate low quality with anatomical errors.

Motion, fluids and mechanical assembly

A fruit falling into the water tests splash, transparency, lighting and the sensation of movement. All three models produce strong images; Brendan prefers Sunburst lighting, but recommends Flare when the difference doesn't justify waiting any longer.

The exploded mechanical assembly also presents similar quality. The main distinction is again the background: GPT Image 2 shows a distorted texture that does not appear with the same strength in Flare and Sunburst.

Neither a physics image nor a technical montage proves real correctness. Check sequence, alignment, number of parts, interfaces, gravity and continuity. For assembly instructions, always check with CAD, documentation and human review.

Surreal scene: many relationships in a single request

The last prompt calls for a giant, lidless porcelain teapot floating over a desert, a miniature forest, a red cabin, a waterfall falling into a circular lake, and a ladder connecting the lake to the edge of the teapot.

GPT Image 2 spills water into the desert, breaking the relationship with the lake. Flare and Sunburst correctly link nozzle, cascade and destination. The test is less about beauty and more about obeying multiple simultaneous spatial relationships.

For complex briefs, turn the request into an inventory: entities, attributes, relationships, counts, and prohibited elements. Then evaluate each item. A composition can convey the general idea and still miss the exact relationship that makes the concept understandable.

Which model to choose: an operational rule

Choose Flare for quick exploration, social content, variations, and tasks where multiple solutions are acceptable. Its official function favors speed, and in the video casual photography won out even without being the maximum capacity model.

Choose Sunburst for localized editing, product, difficult text, diagrams, campaigns, and briefs with exact relationships. It was the most consistent model in Brendan's technical and preservation tests.

Maintain GPT Image 2 only when there is already a stable pipeline, specific compatibility, or a demonstrated economic case. The documented token fees of the 2.5 models correspond to those of GPT Image 2, so the decision must consider consumption, quality, latency and rework.

Implement a simple router: Flare generates three exploits, the human chooses a base, and Sunburst performs the critical edit. Store model, snapshot, quality, prompt, reference, time, tokens and decision. After twenty to fifty assets, you will have real data for your business.