Which AI Image Model Renders Text Best? Test Protocol

Disclosure: Vofy is an all-in-one AI creative studio. This comparison uses models available through Vofy, but the scoring method is designed to expose failures rather than favor a platform or vendor.

The usual AI text demo asks a model to draw one large word. That is not a poster test. Our broader AI image generator comparison covers general visual quality; this protocol isolates text-heavy production reliability. A real event poster has a headline, subtitle, date, time, venue, and URL, and every line competes for a limited amount of attention. It must remain readable after social platforms shrink it, and a late date change should not destroy the design around it.

This benchmark therefore asks a harder production question: which model can create a usable poster repeatedly and survive one precise copy edit? The locked protocol calls for the same NIGHT BLOOM brief, four independent runs per model, and one controlled date-change request. We will keep every output. Until the complete retained set is generated and scored, this article will not name a winner.

Benchmark status, August 5, 2026: the prompt, model set, aspect ratio, scoring rubric, and edit instruction are locked. Six preliminary Run 1 samples are shown below as visual previews only. They lack complete records under the formal execution schema, so they are excluded from scoring rather than mixed with the official test window. All 24 scored first-generation outputs, six controlled edits, and all scores are pending. No image or result from the earlier infographic comparison is reused here.

TL;DR

• This is an event-poster reliability test, not another infographic or illustration comparison.
• The completed benchmark will include four new independent 3:4 attempts from GPT Image 2, Seedream 5.0 Pro, Nano Banana 2, Grok Imagine Image Quality, Qwen Image 2.0 Pro, and Wan 2.7 Image. The six preliminary samples currently shown will not enter the score.
• The benchmark checks exact copy, invented text, hierarchy, text-to-purpose relationships, readability at mobile display size, and the ability to change only the date.
• A beautiful best-case image is not enough. The useful model is the one that produces the highest share of usable outputs with the lowest repair burden.
• Legal copy, QR codes, schedules, and critical URLs should still use deterministic typesetting when a single incorrect character is unacceptable.

The short answer is that the best AI image model for text is the one that remains accurate after the poster is reduced, revised, and repeated. Correct spelling is necessary, but it is only the first gate. This test is designed to find that model without filling in a result before the evidence exists.

1. Poster Text Is a System, Not a Spelling Test

Text inside an image can fail at several levels. The headline may be spelled correctly while the venue is too small to read. The date and time may both be accurate but visually grouped with the wrong line. A URL can contain the right letters at full resolution and collapse into ambiguous shapes when the poster appears in a mobile feed. These are different errors, and a single “text accuracy” score hides them.

For this benchmark, poster reliability has five layers. Transcription asks whether every required character is present exactly once. Hierarchy asks whether the eye reaches the headline, subtitle, and event details in the intended order. Binding asks whether date, time, venue, and ticket information read as one coherent event rather than disconnected decorations. Delivery-size legibility checks the actual size at which someone will encounter the poster. Edit stability measures whether the model can revise one line without quietly redesigning everything else.

This changes what “best” means. A model that returns one immaculate poster and three malformed variants may be useful for exploration, but it is not dependable for a weekly campaign. Likewise, a plain first output that needs two minutes of deterministic cleanup can be more valuable than a spectacular design that takes twenty minutes to reconstruct. The benchmark records both the success rate and the human repair time instead of treating the prettiest sample as representative.

2. The NIGHT BLOOM Benchmark

The test uses a fictional botanical light exhibition because the brief combines expressive artwork with practical event information. The flower imagery gives each model room to design, while six exact text lines make errors easy to identify. Unlike a chart, there is no quantitative geometry to dominate the result; unlike a one-word typography demo, the copy has a realistic information hierarchy.

The model set is GPT Image 2, Seedream 5.0 Pro, Nano Banana 2, Grok Imagine Image Quality, Qwen Image 2.0 Pro, and Wan 2.7 Image. These names reflect the routes selected for the test on Vofy as of August 5, 2026. Availability and behavior can change after vendor or platform updates, so a later rerun should record a new date instead of silently replacing this one.

For current vendor context, consult the official OpenAI image generation guide, ByteDance Seedream 5.0 Pro launch post, Google Gemini image generation documentation, xAI image generation documentation, Qwen-Image 2.0 launch post, and Alibaba Cloud's Wan2.7-Image announcement. These sources describe vendor capabilities and availability; they do not determine the scores in this independent poster test.

2.1 The exact generation prompt

Every model receives the following prompt without model-specific optimization:

Create a polished vertical event poster for a nighttime botanical exhibition.

Visual direction: deep black background, luminous cobalt-blue and coral flowers, translucent leaves, restrained Swiss editorial layout, high contrast, crisp professional print design.

Include the following text exactly as written, preserving the line breaks:

"NIGHT BLOOM"
"A Botanical Light Exhibition"
"FRIDAY, OCTOBER 16"
"7:30 PM"
"RIVER GLASSHOUSE"
"TICKETS AT NIGHTBLOOM.CO"

Render every quoted line exactly once, letter for letter. Do not add, remove, translate, repeat, or rewrite any text.

Typography hierarchy:

• "NIGHT BLOOM" is the largest headline
• The subtitle is medium-sized
• Date, time, venue, and ticket URL are smaller but clearly readable

Use generous spacing and a balanced poster composition. No logos, no watermark, no frame, no product mockup. Show the flat poster directly facing the viewer and filling the image.

All scored runs will use a 3:4 aspect ratio, a native option shared by all six tested models. It preserves a vertical poster format while providing more horizontal room for the subtitle, date, and URL than narrower 2:3 or 9:16 canvases. Each model gets four independent generations, and the first four terminal results are retained. Image payloads are saved as files; refusals, blank outputs, and safety blocks are retained in the execution log. There are no rerolls, cherry-picked seeds, reference layouts, hidden negative prompts, or prompt rewrites. Native pixel dimensions may differ, but the evaluation canvas remains proportional.

Each scored file must also have a complete execution record. The prompt and aspect ratio stay identical, while model-specific values are recorded rather than silently normalized.

Execution field	Locked rule
Model and provider route	Record the exact Vofy model ID and resolved route for every request
Quality and resolution	Use the Vofy defaults selected for the run and record the resolved values
Seed and request identity	Do not set a manual seed; retain request ID, output order, and timestamp
Test window	Pre-register a window ID and UTC start time; finish all 24 initial generations and six Run 1 edits within 24 hours of the first scored request
Version stability	Record model version, provider route, and resolved defaults before Run 1; any change during the window invalidates the complete window
Attempt completion	The first terminal model-handled result completes an attempt, whether it is an image, refusal, blank output, or safety block
Transport retry	Retry only a timeout, connection failure, 5xx, or malformed transport response that returns no terminal model result; use the identical payload and allow at most two logged retries
Missing metadata	Missing any required execution field invalidates the complete window rather than only the affected image

A returned refusal or safety block with no image is retained as a failed output and receives 0/75; a returned blank image is retained and scored under the same visible rubric as every other image. None of these terminal results can be rerolled. A transport retry continues the same planned attempt; it may never replace a terminal result that has already been returned. If the same attempt still has no terminal result after two transport retries, or if the 24-hour limit, route, version, defaults, or metadata rules are broken, the benchmark remains incomplete. The entire window is archived and excluded, then all 24 initial generations and six edit evaluations restart in a new documented window. Partial windows and model-specific replacement sets are never mixed.

To reproduce the brief, open Vofy Image Studio with GPT Image 2, paste the exact prompt above, and then use the model selector for the other routes. The preliminary samples below are visual examples only; the final score will use a fresh four-output set from every model.

2.2 The controlled edit prompt

Run 1 is always the edit source, even when its copy is imperfect. This prevents a subjective best-output selection from making one model's editing task easier than another's. If Run 1 has no editable image because it ended in a refusal or safety block, the controlled edit is recorded as not executable and receives 0/15; Run 2 is not substituted. The instruction for every editable Run 1 is deliberately narrow:

Edit this poster by changing only "FRIDAY, OCTOBER 16" to "FRIDAY, OCTOBER 23".

Preserve every other word, letter, number, visual element, color, type treatment, spacing decision, flower, and layout position exactly. Do not redesign the poster and do not add or remove any other text.

The edit passes only if the date changes correctly and every non-date element remains materially unchanged. A changed canvas size fails the composition check immediately. Otherwise, reviewers align the before-and-after canvases by their outer edges at equal dimensions, then compare a blink view and a difference overlay. The permitted date mask is the union of the old and new date-line bounding boxes, padded on every side by 2% of the shorter canvas dimension. Any visible change outside that mask to non-date copy, spacing, flowers, geometry, color, or type treatment fails the composition check; compression noise that does not change a visible edge or form does not. Existing Run 1 errors are part of the baseline and must not be repaired during this step. A fresh interpretation may look attractive, but it is still an editing failure.

3. The Poster Reliability Score

A single spelling percentage cannot describe this job, so the benchmark uses a 100-point Poster Reliability Score (PRS). The score rewards exact copy most heavily, then adds the qualities that determine whether a poster can ship. Every criterion is visible before the outputs exist, preventing the rubric from being adjusted to favor a surprising result.

Dimension	Points	Scoring anchor
Exact required copy	25	4 points for each exact required line, plus 1 point when all six are exact
No invented or repeated text	10	10 for none, 5 for one extra or repeated line, 0 for two or more, a logo, or a watermark
Information hierarchy	15	5 each when headline, subtitle, and event details occupy the requested reading order
Delivery-size legibility	15	2.5 for each required line readable at a 360 x 480 px preview
Text-to-purpose binding	10	2.5 each when date, time, venue, and URL are unambiguously associated with the event
Controlled edit precision	15	5 for the exact date change, 5 for unchanged non-date copy, 5 for materially unchanged composition
Four-run consistency	10	2.5 for each publishable initial output in the four-run set

The first five dimensions produce an initial-output score from 0 to 75 for each run. The mean of the four initial-output scores is then combined with the single Run 1 edit score from 0 to 15 and the consistency score from 0 to 10. The resulting model PRS is therefore mean initial score + edit score + consistency score, with a maximum of 100.

An output first passes a hard copy gate. All six required lines must be exact, no invented or repeated text may appear, and all six lines must remain readable in the standardized preview before the output counts as publishable. It must also be a flat, front-facing poster without a frame, product mockup, logo, or watermark. A visually strong poster that fails this gate still receives its measured PRS components, but it does not count toward first-pass usability or consistency.

Two reviewers independently score hierarchy, legibility, binding, and edit stability from the original export, standardized preview, and aligned edit pair. The score sheet retains Reviewer A's value, Reviewer B's value, the consensus value, and a written rationale for every cell that changes during reconciliation. Exact copy and invented text are checked character by character against the locked prompt. The same designated designer performs every timed repair after scoring is complete.

The PRS will be accompanied by two uncompressed measurements. First-pass usability rate is the number of publishable initial outputs divided by four attempts. Median repair minutes covers failed initial outputs only. Before timing starts, the team records one desktop editor and version; allowed operations are deterministic text layers, selection and masks, transform, clone or heal, and sampled color adjustments. AI generation, generative fill, a second image model, stock replacement art, and prompt reruns are prohibited. The timer starts when the original export opens with the locked copy brief visible and stops only after proofreading, a passing 360 x 480 check, and final export. Each repair is capped at 30 minutes, and unfinished work is recorded as 30+. A terminal result with no image is an automatic 30+ rebuild for this measurement.

For the median, 30+ is treated as 30 only to calculate a lower bound; a + is appended whenever a capped value occupies either central position. For example, 10 and 30+ report as 20+, not an exact 20. If a model has no failed initial outputs, the repair median is reported as N/A (0 failures), not zero minutes. Repair time does not change the PRS. Keeping these values separate prevents a weighted score from hiding instability or labor.

4. Why Mobile Size Can Reverse the Ranking

Poster generators are usually judged in a large gallery view. Real campaign assets are often seen at a fraction of that size. For this test, each 3:4 export is also reduced to 360 x 480 pixels and viewed at 100% zoom. Reviewers may not enlarge the image while scoring legibility. That simulates a common mobile-feed presentation without claiming to reproduce every device or social network.

The reduction test exposes choices that spelling checks miss. Thin letterforms can close up, coral text can lose contrast against luminous flowers, and a URL that looks sharp in the source file can become too tight to distinguish I, L, and 1. Hierarchy also becomes easier to judge: if the subtitle competes with the headline or the ticket line disappears, the poster is not doing its communication job.

This is one of the benchmark's newer conclusions before any model wins: resolution alone is not legibility. A 2K or 4K export can contain beautifully formed microtype that fails when delivered. Models should be evaluated at the final use size, and prompts should reserve enough scale and contrast for the least glamorous but most actionable information.

5. The Date Edit May Matter More Than the First Render

Campaign design rarely ends after generation. A venue changes, an event moves by one week, or a ticket URL receives a tracking path. If every update forces a new composition, the model is functioning as an idea generator rather than an editable design tool. That can still be valuable, but it is a different workflow and should be priced accordingly.

The controlled date edit measures two independent abilities. First, the model must locate and replace the requested string accurately. Second, it must preserve everything outside that region: the other five lines, flower shapes, color palette, spacing, and type treatment. We will inspect the before-and-after pair at equal dimensions and record unintended changes, not rely on a general impression that the images look similar.

Edit stability can overturn a close first-generation result. Suppose Model A creates slightly stronger typography but redraws the entire poster when the date changes, while Model B starts with a plainer layout and performs a clean local replacement. A team expecting frequent revisions may rationally choose Model B. The correct recommendation depends on the lifecycle of the asset, not just its debut.

For readers testing a model-specific editing workflow, the GPT Image 2 editing guide provides additional context. This benchmark, however, uses the same minimal edit instruction for every route so operator-specific prompting skill does not become the hidden variable.

6. Preliminary Samples, Full Results Pending

The earlier specialist comparison cannot answer this benchmark. It used one output per model, tested different visual relationships, included no delivery-size check, and did not measure local editing. Reusing its images or rankings here would create the appearance of new evidence without a new experiment.

The scorecard will remain unpublished until 24 new first-generation posters and six controlled edits have been exported and reviewed. The six Run 1 samples below are visual previews, not scored evidence or a ranking. Their exact provider routes, resolved defaults, request IDs, output order, and timestamps were not all captured under the locked schema, which is the specific reason they cannot enter the formal set. When the scorecard is published, each result row will link back to the retained file set, and every failed attempt will remain visible. No model will be called the winner because of a single attractive image.

Once results are available, the article will report at least three recommendations rather than force a universal rank: strongest first-generation reliability, strongest controlled editing, and lowest repair burden. If different models lead those categories, the split verdict is the useful result. For a broader test of products, portraits, hands, object counts, and spatial placement, use the separate core AI image generator comparison.

7. How to Read the Finished Scorecard

The best model for a one-night promotional concept is not necessarily the best model for a recurring event series. Start by identifying the most expensive failure in your workflow. A solo creator may tolerate rerolls but hate manual retouching. An agency may accept a higher generation cost if the chosen poster can survive client copy changes without a redesign. A multilingual campaign may need deterministic layout regardless of which model produces the visual foundation.

Use first-pass usability rate when volume matters, edit precision when approvals are likely to change, and repair minutes when staff time dominates cost. Treat the PRS as a summary, not a substitute for these underlying measurements. Once the retained set is complete, inspect all four runs together: consistency is easier to understand from the full set than from one decimal number.

The GPT Image 2 model guide can help readers evaluate one candidate beyond this narrow poster assignment, while the Nano Banana 2 vs Seedream 5 comparison covers a different head-to-head decision. Neither article should be used to fill the pending scorecard; every task needs evidence matched to its own failure modes.

8. When AI Should Not Typeset the Final Poster

Even a strong result does not make generative text deterministic. Use a traditional layout tool for legal disclosures, safety instructions, schedules, prices, sponsor lists, scannable QR codes, and URLs where one character can send visitors to the wrong place. In those cases, generate a text-free or lightly typeset visual foundation, then place critical copy with real fonts and editable layers.

An AI-first poster is more defensible when lettering is part of the image itself: flowers wrap around the title, projected light crosses the characters, or the type needs to inherit the material of the scene. Even then, proofread the exported asset rather than the prompt. The prompt records intent; only the pixels reveal what a viewer will read.

The hybrid approach is not a concession. It assigns probabilistic generation to visual exploration and deterministic software to information that must remain exact. The winning model in this test, once one earns that label, will be the best starting point for this particular six-line event brief, not a replacement for typesetting discipline.

9. Conclusion

This poster test changes the central question from “can the model spell?” to “can the poster survive production?” The new evidence must cover four independent first attempts, a mobile-size reading check, a precise date edit, and the human time required to repair failure. That is a stricter standard than selecting one polished gallery image, but it aligns the ranking with the work creators actually have to finish.

No winner is published yet because the complete NIGHT BLOOM evidence set does not exist in the article record. The six preliminary Run 1 posters are excluded from scoring and do not establish consistency, edit stability, or repair burden. When the fresh 24-output result set and six edits are complete, the recommendation will follow the locked rubric even if the most attractive image comes from a less reliable model. Until then, the practical default is simple: use AI for integrated visual typography when variation is acceptable, and preserve deterministic control over any line that cannot be wrong.

FAQ

Related Reading

• Best AI Image Generators: The Core Model Test
• GPT Image 2: What It Is and When to Use It
• How to Edit Images with GPT Image 2