Is Z-Image Turbo free?

Yes,. The weights are published under Apache 2.0, which permits commercial use, and you run them on your own hardware, so there is no per-image cost and nothing you generate touches anyone else's servers. Your costs are the GPU and the electricity. That is a meaningfully different deal from "free tier" models that meter you.

What GPU do you need to run Z-Image Turbo?

16GB of VRAM runs the full-precision build comfortably, which puts it in reach of a 4060 Ti 16GB, a 4080 or a 3090. That is the design goal: Tongyi built a 6B model specifically so it would fit consumer cards without quantisation. On data-centre hardware the eight-step generation is sub-second.

Is Z-Image Turbo better than Flux 2?

On the Artificial Analysis Image Arena it ranks above Flux 2 dev among open-weights models, and it is roughly a quarter of the size. In our own testing the honest answer is narrower: it is better at text and at following an unusual instruction, and it is not better at hands. Benchmarks aggregate across prompt types, so treat the ranking as a reason to try it rather than a verdict on your particular work.

Can Z-Image Turbo edit an existing image?

Not out of the box. It ships as text-to-image only, with no inpainting or instruction-based editing, which is the same gap Krea 2 launched with. If you need to change one element of an image you already have, you will be pairing it with a separate editing model or doing the pass in ComfyUI yourself.

Why does Z-Image Turbo only need 8 steps?

Because Turbo is a distilled model, not the base one. Tongyi compressed the base Z-Image using Decoupled-DMD, which trains the small model to reproduce in eight function evaluations what the base model does across many more. You are trading a little diversity for a large speed gain, which is the same bargain Krea 2's Turbo variant makes.

Z-Image Turbo: the 6B open model that renders text

Last updated: 2026-08-05

Written by Clement

Our verdict

The best open-weight model available for images that contain text, and the one most likely to hand your subject a broken finger.

Reach for it when

Posters, packaging, signage and anything where words have to be spelled correctly
People on a 16GB consumer card who do not want to quantise
Bilingual work: it handles Chinese and English typography in the same frame
Commercial projects that need a permissive licence (Apache 2.0)

Skip it if

Your work is close-up hands, jewellery or anything where finger anatomy is the subject
You need a single character to stay identical across a set without training a LoRA
You want built-in editing or inpainting: this is text-to-image only

At a glance

Made by: Alibaba Tongyi Lab, released 27 Nov 2025
Size: 6B parameters, S3-DiT architecture
Speed: 8 sampling steps, sub-second on data-centre GPUs
Runs on: 16GB VRAM consumer cards, ComfyUI
Licence: Apache 2.0, commercial use permitted
Ranking: #1 open-weights on the Artificial Analysis Image Arena

Z-Image Turbo output in anime style: three women in pastel satin robes at the edge of a lit rooftop pool at night, with a white cat and two dark dogs nearby and a neon skyline behind them. — Z-Image Turbo, prompt 3 of our showdown. Three distinct character designs in one frame is the check most models fail.

Alibaba's Tongyi Lab put Z-Image Turbo out on 27 November 2025 under Apache 2.0, and the number that got attention was 6 billion parameters. That is small. Flux 2 dev is several times the size. Small usually means worse, and here it did not: Artificial Analysis put Z-Image Turbo at the top of its open-weights image ranking, above Flux 2 dev, HunyuanImage 3.0 and Qwen-Image.

It gets there through distillation rather than scale. The base model is compressed into Turbo using Decoupled-DMD, which collapses generation down to eight sampling steps, then aligned to human aesthetic preference with DPO and GRPO. Eight steps on a 16GB card is the practical headline. You do not need a quantised build and you do not need to wait.

We ran it through the same three prompts we run every image model through. It did one thing no other model in the set managed, and one thing badly enough that it changes what you should use it for.

The text rendering is the reason to install it

Most open image models treat text as texture. Ask for a word and you get letter-shaped marks that dissolve under inspection. Z-Image Turbo spells.

Our first showdown prompt asks for a small tattoo in fine linework on a subject's forehead, reading "GenLovers". Krea 2, which beats Z-Image on almost every anatomy check, simply declined to render it. Z-Image Turbo produced it legibly, in the right place, following the curve of the brow. That is a hard ask: small text, on skin, on a curved surface, at an angle.

The published capability matches what we saw. Tongyi built it to handle Chinese typography in signage, posters and packaging alongside English in the same image, which is a harder problem than either alone and something most Western models still fumble. If your output has words in it, this is the open model to reach for.

Full specification

Architecture	Scalable Single-Stream DiT (S3-DiT): text, visual semantic tokens and image VAE tokens concatenated into one input stream
Distillation	Decoupled-DMD, collapsing inference to eight function evaluations
Preference alignment	DPO and GRPO against human aesthetic preference
Parameters	6B
Minimum VRAM	16GB for the full-precision build
Sampling steps	8
Licence	Apache 2.0
Editing / inpainting	Not included. Text-to-image only out of the box
Text rendering	Chinese and English, including mixed-script layouts

How it compares to Krea 2

These two models came out of our showdown as a clean pair of opposites, which is more useful than a ranking.

Krea 2 is the better photographer. Hands, skin, fabric and light are stronger, and it produced the single best frame in our whole set. It also ignored two explicit instructions to get there: no tattoo, and a composition adjacent to the one we asked for.

Z-Image Turbo is the better listener. It rendered the text, kept two mirror reflections tracking the subject, and produced three distinct character designs where Krea 2 cloned faces. Then it broke a hand.

So the choice is not which model is better. It is whether your image lives or dies on anatomy or on instruction-following. Portrait and product work: Krea 2. Posters, signage, multi-character scenes and anything with words: Z-Image Turbo.

The model showdown

Every image model on this site runs the same six prompts across three tiers (SFW, erotic and explicit) on the same rig, with no cherry-picking and no retouching. Below is what Z-Image Turbo returned, graded against a fixed rubric so the results mean the same thing from one model page to the next.

Run on: Z-Image Turbo, FP8, ComfyUI on a single RTX 4090, 8 steps (Erotic Prompt 2 above); Civitai native sdcpp/zImage/turbo orchestration (all other results below, 2026-08-07)
Date: 2026-08-07

Why these six prompts? ▾

They are chosen to break things, across three tiers so a model that refuses NSFW outright still gets a fair, comparable test. The SFW tier stacks two-person contact, readable logo text on folded fabric and genuine expressions with teeth. The erotic tier stacks the four failure modes that mark an image as machine-made at a glance: hands, fine text on skin, a mirror that has to obey geometry, and three distinct character designs in one frame. The explicit tier exists to test the same anatomical limits at full intensity, but its output is never published on this site.

No model passes every check, and not every model can attempt every tier. That is the point. A rubric everything passes tells you nothing about which model to reach for on a Tuesday afternoon.

SFW detail test

Two people, bright kitchen

Two subjects interacting in hard morning light, with a brand logo on fabric that has to stay readable while the fabric folds. Tests skin-on-skin contact, genuine expressions with teeth, and text rendering on a non-flat surface. Fully clothed throughout.