The Best AI Models for Text in Images: Typography, Compared by Job (August 2026)
Last updated August 7, 2026
Text rendering is four jobs, and no model wins more than two. Ideogram 4 wins layout control (JSON bounding boxes, 16 hex colors, 47.9% blind designer win rate) but fails diacritics and non-Latin scripts. GPT Image 2 wins multilingual scripts (CJK, Hindi, Bengali) via its reasoning pass. Recraft wins editability with native SVG. Qwen owns Chinese; Z-Image is the 6B bilingual budget pick. Body copy beyond ~25–30 words fails on every model. All six run inside the invideo agent.
Updated August 2026
"Best AI model for text in images" has no single answer, because text rendering is not one job — it is four: literal-string accuracy (does "SUMMER SALE — 40% OFF" come out spelled right), layout control, multilingual scripts, and editability (can you fix the kerning without regenerating). As of August 2026, the specialist Ideogram 4 wins layout, the generalist GPT Image 2 wins scripts, the vector model Recraft wins editability, and Qwen owns Chinese outright. No model wins more than two of the four — so this comparison is organized by job, not by leaderboard.
Which model wins each text-rendering job?
| Model | Exact-string accuracy | Layout control | Scripts beyond Latin | Editability of output |
|---|---|---|---|---|
| Ideogram 4 | 47.9% blind designer win rate | Best available: JSON bounding boxes + 16 hex colors | Weak — diacritics dropped, Arabic/Cyrillic/CJK unreliable | layerize splits graphics into editable text layers |
| GPT Image 2 | Strong; #1 on both major arenas overall | Planned by the reasoning pass, not user-pinned | Widest documented: Japanese, Korean, Chinese, Hindi, Bengali | Re-prompt only |
| Recraft V4.1 | Reliable at logo length (~1–5 words) | Design-system coherent, not coordinate-pinned | Not documented | Native SVG — text editable as vector shapes |
| Flux 2 flex | Best in the Flux family | Hex-code binding + JSON prompts | Not the pitch | Prompt-level only |
| Qwen 2.0 Pro / 2512 | Built around complex text rendering | ~1,000-token prompts, native 2K on Pro | Chinese: unmatched | Edit models revise in-image text directly |
| Z-Image Turbo | Legible at short lengths | Basic | Bilingual EN/CN at 6B | Prompt-level only |
The verdict: teams burned by misplaced headlines need Ideogram's boxes, teams shipping in five languages need GPT Image 2, and teams that touch the file after generation need Recraft's vectors — everything else exports flattened pixels.
Why do designers keep picking Ideogram 4?
Because it is the only model where typography is addressable. Ideogram 4 (June 3, 2026, a 9.3B open-weight diffusion transformer) was trained on structured JSON captions, so a prompt can pin every element to a bounding box on a 0–1000 grid and lock the palette to up to 16 hex colors — layout as specification, not suggestion. The evidence is direct: in a blind typography evaluation with professional designers (ContraLabs eval, per the Hugging Face model card), Ideogram 4 took first place 47.9% of the time, and independent testing has it out-rendering open models three times its size. It rewards typographic literacy, too: naming a real typeface — "Cooper Black," "Futura" — beats "a bold retro font."
The honest limits, from community testing and the model's own documentation: Polish, Turkish, and Vietnamese diacritics get dropped or duplicated; Arabic, Cyrillic, and CJK remain unreliable; quality degrades past roughly 25–30 words. Ideogram is a headline machine, not a paragraph machine — and its script coverage is exactly where the next model takes over.
What makes GPT Image 2 the multilingual outlier?
GPT Image 2 renders in-image text across Japanese, Korean, Chinese, Hindi, and Bengali — the widest documented script range in this comparison — via a different mechanism: an agentic reasoning pass that researches, plans the composition, renders, and checks its own work. That is why its text tends to be correct in context — labels on the right diagram parts, readable speech bubbles — not merely spelled correctly. The blind-test record is the strongest here too: #1 on Arena.ai at 1385 Elo (July 10, 2026) and on Artificial Analysis at 1339 (August 2026), though both measure overall quality, not typography in isolation.
The trade-offs are structural. You cannot pin a headline to coordinates — the reasoning pass decides layout, a feature for "make me an infographic" and a frustration for "put the logo exactly here." Reasoning also costs time and tokens: per-image cost is a derived $0.005–$0.21 range. But for localized campaign art — one design, five languages, correct scripts in each — nothing else here is documented to match it.
When does Recraft beat both?
When the text has to survive contact with a designer. Recraft V4.1 is the only major family that outputs true native SVG — real vector paths and structured layers, not a traced raster — so generated type opens in Figma or Illustrator as shapes you can refine: adjust a letterform, recolor, re-kern. Recraft's V4 positioning treats typography as structural rather than painted decoration, and V4.1 (May 14, 2026) specifically tightened vector and typography precision. At $0.035 per image after the June 2026 cuts, it is also the cheapest specialist pick. Scope it correctly: reliability holds at logo length, roughly one to five words. It is the wordmark and icon-set model, not the poster-body-copy model.
Is Flux worth using for text?
For one case: brand-locked short text inside a larger image pipeline. Flex is the Flux 2 family's designated best text renderer (Elo 1180 on Artificial Analysis, August 2026), and it pairs two controls the specialists lack in combination: exact hex-code color binding ("headline in #E4572E") and JSON-structured prompts for repeatable batches. Quote the literal string — Black Forest Labs' own guidance — and flex delivers competent typography at $0.05–0.06/MP without leaving the Flux stack. If typography is the point of the image rather than a component, Ideogram and GPT Image 2 remain the stronger picks.
Who wins Chinese and bilingual text?
Qwen, and it is not close — complex text rendering, especially Chinese, is the family's founding differentiator, unmatched among mainstream models. For text-dense finals (posters, infographics, packaging), Qwen-Image-2.0 Pro takes prompts up to ~1,000 tokens and outputs native 2K; the open-weight 2512 checkpoint carries the same text-first DNA under Apache 2.0. The edit line adds something rare: revising in-image text on an existing image while preserving the rest.
The budget alternative is Z-Image Turbo, a 6B Apache 2.0 model from Alibaba's Tongyi ecosystem whose model card calls out bilingual English–Chinese in-image text as a signature strength — sub-second on datacenter hardware, self-hostable on a 16GB card. Keep strings short; a few words render far more reliably than sentences, in either language.
Which models should you avoid for text work?
Two, for different reasons. Krea 2's third-party API documentation warns it "may have difficulty rendering legible text within scenes, intricate UI layouts, or pixel-perfect branding" — a reseller-doc caveat, but consistent with its aesthetics-first positioning. And Imagen 4, whose typography was genuinely first-rate, shuts down on the Gemini API on August 17, 2026 — a text workflow built on it gets rebuilt in weeks. One rule regardless of model: nothing here renders body copy reliably. Headlines yes, paragraphs no; the ceiling sits near Ideogram's ~25–30 words.
Which model for which text job?
- Logos and wordmarks → Recraft (editable vectors) or Ideogram 4 (typeface naming) — the pairing behind a logo generator workflow.
- Posters → Ideogram 4 for pinned headline/date/venue placement; Qwen 2.0 Pro for dense copy or Chinese — both natural engines for an AI poster maker.
- Multilingual marketing → GPT Image 2, the only model here documented across CJK plus Hindi and Bengali.
- Chinese-market creative → Qwen, with Z-Image as the fast, self-hostable understudy.
- High-volume text drafts → Z-Image Turbo: sub-second, effectively free self-hosted.
- Brand-color-exact banners → Flux 2 flex with quoted strings and hex binding.
Text-rendering questions, answered
What is the most accurate AI model for rendering exact words?
For Latin-script headlines, Ideogram 4 — backed by its 47.9% first-place rate in a blind designer evaluation. For multilingual strings, GPT Image 2. Either way, put the literal text in quotes.
Can AI models render Hindi or Arabic text in images?
GPT Image 2 documents Hindi and Bengali rendering. Arabic is thinner: Ideogram 4 is documented as unreliable on it, while Luma's Uni-1 documents multilingual in-image text including Arabic — worth testing for that script.
Which AI model handles Chinese text best?
Qwen — Chinese text rendering is the family's founding strength, unmatched among mainstream models. Z-Image offers credible bilingual English–Chinese rendering in a 6B open-weight package.
How many words can AI reliably put in an image?
Plan for headline length: Ideogram 4 degrades past ~25–30 words, Recraft's logo-context reliability spans ~1–5 words, and Z-Image's card favors short strings. Paragraphs are beyond the current field.
Can I edit the text after generating the image?
Three paths: Recraft's native SVG makes text editable as vector shapes; Ideogram's layerize decomposes a flat graphic into editable text layers; and edit models — Qwen's edit line, MAI-Image-2.5, Reve — revise in-image text by instruction, Reve at $0.01 per edit.
Why does quoting the text in the prompt matter?
Every family documents the same convention: quoted text is rendered as a literal string; unquoted text is description the model may rephrase. It is the highest-leverage habit in text-in-image prompting.
Where can you test all of them on the same prompt?
Every model in this comparison — Ideogram 4, GPT Image 2, Recraft V4.1, Flux 2 flex, the Qwen line, and Z-Image Turbo — is in the image picker of the invideo agent — so the practical way to settle rankings is to run the same quoted headline across three candidates and keep the one that spells. The family pages linked throughout, plus the AI models index, carry the per-model depth.
Version history: first published August 2026, comparing Ideogram 4, GPT Image 2, Recraft V4.1, Flux 2 flex, Qwen 2512/2.0 Pro, and Z-Image Turbo.