Best AI models for text rendering and typography

Ideogram 3.0 is the best AI model for text rendering in 2026: it's the only model on this list built around a dedicated typography system rather than treating legible text as a side effect, at $0.06/image. Nano Banana 2 is the practical runner-up, Infer's own copy calls it "Ideogram-grade text fidelity at half the latency," a claim worth attributing to Infer rather than an independent benchmark, but it's also the fastest model here and covers 30-plus languages. Seedream 4.0 is the budget pick at $0.03/image if your work is bilingual CJK-plus-Latin.

This ranking covers the four Infer-hosted models with a documented case for putting readable words inside a generated image: Ideogram 3.0, Nano Banana 2, Seedream 4.0, and GPT Image 1.5. Text rendering is still a real weak spot across the image-generation category, not a solved problem, so every entry below gets its garble rate and its scripts named specifically rather than assumed.

The ranked list

Ideogram 3.0's full Infer model page was returning an HTTP 500 error on every fetch during this compilation, so its resolution and benchmark rank are unconfirmed beyond the page's meta description; treat the #1 slot as earned on documented purpose-built typography positioning and price, not a verified spec sheet. Rank 1's leaderboard cell has no published figure for the same reason.

Ideogram 3.0: the typography specialist, with a data gap

Ideogram 3.0 exists to solve one specific failure mode: text that reads correctly inside a generated image, rather than a plausible-looking scramble of letterforms. Infer's own meta description for the model names posters, ads, and logo generation as its core use cases, at $0.06/image, the highest per-image price on this list, but the only model here whose entire positioning is built around in-image copy rather than treating it as a secondary skill.

The honest flaw isn't the model's typography, it's Infer's own page. Ideogram 3.0's full model page returned a server error on every fetch attempt during this compilation, so beyond the meta-tag description, resolution, exact benchmark rank, and current script support are unconfirmed for the Infer deployment specifically. That's a real gap for a page that should be selling typography fidelity with numbers, not a meta tag. Until Infer's page is healthy again, budget an extra proofing pass on any run of logos or storefront signage before it ships. Try Ideogram 3.0 in the Infer playground →

Nano Banana 2: near-Ideogram fidelity, per Infer, at real speed

Nano Banana 2 is the fastest model on this list, roughly 1.4 seconds median and 2.6 seconds at P95, and it renders 30-plus languages legibly, including non-Latin scripts, according to Infer's own model page. Infer's copy frames this as "Ideogram-grade text fidelity at half the latency," and that's Infer's comparative claim about its own catalog, not an independent third-party benchmark result, so treat the comparison as directional rather than settled. It also holds character consistency across up to 5 subjects in a scene, useful for localized multi-panel ad sets where the same mascot needs the same face across languages.

At $0.039/image it undercuts GPT Image 1.5 and sits just above Seedream 4.0. The gap: its Editing Elo (1065) trails its Arena Elo (1262) by a wide margin, so heavy in-place text corrections are a weaker use case for it than fresh generation. For a poster or product mockup with copy baked in from the first pass, that's not a problem; for iterative logo tweaks, expect more retries. Test this prompt with Nano Banana 2 on Infer →

Seedream 4.0: the bilingual budget pick

Seedream 4.0 costs $0.03/image, the cheapest model on this list, and it's the only one whose documented strength is specifically bilingual: Infer's own copy calls it the catalog's strongest performer at CJK-plus-Latin typography in a single frame, the case where a poster or product label needs two scripts to stay legible side by side. At roughly 2.8 seconds average latency it's competitive on speed too.

The catch is scope. Outside bilingual and East-Asian-aesthetic work, neither Infer's page nor DATA.md confirms strong text performance, and the model's page states no maximum resolution at all. Treat this as the pick for a specific job, dual-script signage and packaging, not a general-purpose typography default. Seedream 4.0 is live on Infer. Try it →

GPT Image 1.5: strong second, per Infer, for general marketing copy

GPT Image 1.5 lands fourth here on scope, not on overall quality; it's Infer's #2 image model on the general leaderboard at 1271 Elo. Infer's own copy names "text-on-image marketing" as a documented use case and positions the model as a "strong second to Ideogram," Infer's framing, worth repeating with attribution rather than as an independent verdict. It's the model to reach for when text is one requirement among several, product staging with a price tag overlay, an editorial composite with a headline, rather than the entire brief.

Don't reach for the 99%-character-accuracy figure that's floating around text-rendering coverage this year and pin it to this model; that number describes GPT Image 2's claimed accuracy across Latin, CJK, Arabic, and Hebrew scripts, a different, newer OpenAI model that isn't in Infer's live catalog as of this writing. At $0.04/image flat and roughly 5 seconds average latency, GPT Image 1.5 is the slowest model on this list, a real cost if you're batching localized ad variants. Run GPT Image 1.5 on Infer →

Why AI models garble text in the first place

Most image models treat text as a shape to paint, not a string of characters to spell. Google Research's 2023 study on the problem puts it directly: "Current image generation models struggle to reliably produce well-formed visual text," and the paper traces the failure to a specific architectural gap, "popular text-to-image models lack character-level input features, making it much harder to predict a word's visual makeup as a series of glyphs" . Most text-to-image systems tokenize a prompt the way a language model does, in whole-word or sub-word chunks, then hand that representation to a diffusion process that paints pixels. The model never sees "cat" as C-A-T; it sees a single opaque token, so when it has to render the letters on a storefront sign, it's reconstructing glyph shapes from a compressed representation that discarded the spelling.

That gap is also why longer strings fall apart faster than short ones. A logo with four letters gives the diffusion process fewer glyphs to keep aligned across denoising steps; a full sentence on a poster multiplies the chances that one character drifts, duplicates, or gets swapped for a visually similar one. Microsoft Research's Glyph-ByT5 paper frames the fix the same way Google's study does, "the core problem lying in text encoder deficiencies," and reports that swapping in a character-aware encoder took spelling accuracy on its design-image benchmark "from less than 20% to nearly 90%," with the resulting model gaining what the paper calls a "newfound ability for text paragraph rendering" at "tens to hundreds of characters" . Read the other direction, that's an admission that the un-fixed baseline models could barely hold a paragraph together at all.

Stability AI made a similar bet with DeepFloyd IF, one of the first widely used models to render legible text reliably: it paired a large T5 text encoder with diffusion that runs directly "in pixel space" rather than in a compressed latent, specifically to keep "coherent and clear text alongside objects of different properties" . That's the pattern behind every model on this list that actually renders text: Ideogram 3.0 is built around typography as its core positioning rather than a side effect, and Nano Banana 2 and Seedream 4.0 both lean on wider multilingual training passes rather than the general-purpose encoders that ship with most image models. Researchers attribute the remaining gap on rare words and non-Latin scripts to the same root cause, a text encoder that was never trained to think in individual characters, so a model without that specialized conditioning is guessing at glyphs even when everything else about the image is right.

How we ranked

The order blends three inputs: whether a model's documented positioning is built around typography specifically (Ideogram 3.0, first, on purpose rather than benchmark rank), confirmed image-leaderboard Elo where one is published (Nano Banana 2, Seedream 4.0, GPT Image 1.5), and price per image. Infer's revenue doesn't depend on which of these four a reader ends up choosing, so nothing above is tilted toward one; where a page had gone down (Ideogram 3.0's), we ranked on documented purpose and price rather than guessing at a benchmark number that doesn't exist for that page.

Which model for which job

• Posters, logos, or packaging where the type is the design → Ideogram 3.0, and proof the output manually until its Infer page is fully back.
• Localized ad sets across multiple languages, including non-Latin scripts → Nano Banana 2.
• Bilingual CJK-plus-Latin signage or packaging → Seedream 4.0.
• UI mockups and product screens with a few lines of interface copy → GPT Image 1.5, since general compositional strength matters as much as the text itself.
• A logo you plan to trademark → generate with any of the four, then run a clearance search before you commit; AI-only output has a real copyright gap regardless of which model made it.

What didn't make the list

GPT Image 2 carries the widely cited 99% character-accuracy claim across Latin, CJK, Arabic, and Hebrew scripts, per coverage of its release, and it opened to developers in early May 2026 with a faster architecture than its predecessor. It's excluded here because it isn't in Infer's live catalog as of this compilation, not because the number isn't real; that gap is worth watching, not ignoring.

FLUX 1.1 [pro] and FLUX.1 Kontext [pro] are strong general and editing models, but neither's documented use cases name text rendering as a strength, and Infer's own copy for FLUX 1.1 [pro] carries an unreconciled $0.02/$0.04 pricing discrepancy on top of that. They're covered instead in the best text-to-image models ranking.

Imagen 4 Standard is Google's photorealism specialist on Infer, product shots and stock-photo replacement, not typography, so it's out of scope for this list on documented use case alone.

All four of these models run on one Infer API key. Try Ideogram 3.0 in the Infer playground →

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See also: Best text-to-image models in 2026, Best AI models for product photography, GPT Image 1.5 vs Nano Banana 2, Best AI video models for ads and marketing creative, and the best/ hub for every ranked list.