- 🏢 Alibaba's third-generation Qwen-Image text-to-image model, catalogued August 2026.
- 🎯 Family focus: rendering text inside images plus compositional control.
- 📚 Lineage began with Qwen-Image, a 20B-parameter MMDiT foundation model.
- 🌐 The original release emphasized complex English and Chinese text rendering.
- 🔧 Same family also covers precise image editing and consistency work.
- 👁️ Qwen-Image generation is documented in Alibaba Cloud Model Studio APIs.
- 💬 A separate Pro tier is catalogued alongside this standard variant.
- 🔒 Parameter count, license and quantization are not listed for this entry.
Alibaba Group is a Chinese multinational technology company founded in 1999 and headquartered in Hangzhou, Zhejiang. Originally built around e-commerce and cloud computing, Alibaba has become one of the most prolific contributors to open-weight AI research, developing the Qwen…
Explore 51 more models by Alibaba Group →Qwen Image 3 is the third generation of Alibaba's Qwen-Image text-to-image line. The catalog summarizes it as a model oriented toward text rendering inside images and compositional control, which matches the stated priorities of the family since its first release. On Venice it sits alongside a higher-tier counterpart, Qwen Image 3 Pro.
The lineage starts with Qwen Image, which the Qwen team introduced as a 20-billion-parameter MMDiT image foundation model built around native text rendering — including multi-line and paragraph-level layouts in English and Chinese — as well as precise, consistency-preserving image editing. For the second generation, the catalog lists Qwen Image 2 and a Pro tier from early 2026, along with editing entries such as Qwen Image 2 Edit for inpainting. Qwen-Image generation endpoints are documented publicly through Alibaba Cloud Model Studio. Qwen is also Alibaba Cloud's broader model family covering language, vision-language and generative media.
For this entry, no parameter count, license, quantization or provider-published benchmark figure is listed, and no results from an independent top evaluator are available here, so the architectural changes relative to earlier generations cannot be described precisely. Rather than repeat unverified figures, this page stays with what is documented: same family, same emphasis on typography-heavy generation and layout fidelity, with a Pro tier offered for more demanding work and earlier generations still listed for editing pipelines.
This About section is AI-generated from public sources via VeniceStats + Venice inference, with no human editing. It may contain inaccuracies.
Daily usage · Qwen Image 3 · Organic sellers
| Seller | Health | Rep↓ | Sales | Images | Routing | $ / img | Save |
|---|---|---|---|---|---|---|---|
| ▲ Apex Ant 0x73b4…e736 | — | 81.71 | $0.55 | 25 | #1 | $0.02 | −50% |
| attacompute.ai 0xd5e7…7be0 | healthy | 51.22 | $0.23 | 4 | gated | $0.0581 | — |
| Venice.ai Proxy 0x1f22…18c9 | failingupstream error · 3.4 h | 86.84 | $0.50 | 25 | #2 | $0.022 | −45% |
"Best price" and the seller table are live Antseed catalog data (advertised $/1M tokens — or $ per generated image for unit-billed image models — not settled amounts). Reputation = buyer trust score (0-100, the Antseed SDK's own formula). "Routing" = the SDK's default buyer routing (what the VPR desktop app ships with): a trust ≥ 60 gate on the effective reputation, then cheapest-first among routable sellers; live failover state (per-peer cooldowns) is buyer-side runtime and not included. Model knowledge (TLDR, provider, About) via the VeniceStats enrichment layer. Advertised catalog, not the model used in any specific purchase. "Usage on Antseed" counts only settlements whose buyers share the per-model split on-chain (metadata v2/v3, opt-in), so every usage figure is a lower bound.