Qwen-Image-2.1 vs FLUX.2 dev vs GPT Image 2.5 vs Nano Banana
Side by side
| Qwen-Image-2.1 | FLUX.2 [dev] | GPT Image 2.5 | Nano Banana Pro | |
|---|---|---|---|---|
| Maker | Alibaba Qwen | Black Forest Labs | OpenAI | Google DeepMind |
| Released | September 20, 2026 | November 25, 2025 | September 8, 2026 | Late 2025 (Gemini 3 Pro Image) |
| Weights | Open (research licence) | Open (non-commercial licence) | Closed | Closed |
| Size | 7B DiT + Qwen3-VL 8B encoder | 32B | Undisclosed | Undisclosed |
| Native resolution | 2048x2048 (up to 2752x1536 wide) | Up to 4MP editing | Arbitrary up to 3840x2160 | Up to 4096x4096 |
| Transparent RGBA output | Yes, native | No | Yes, native | No |
| Reference images | Up to 10 | Up to 10 | Up to 16 | Up to 14 |
| Local edit control | Circles, painted marks, masks | Prompt + references | Multi-turn prompt edits | Conversational edits |
| Runs on consumer GPU | Yes (~24GB, RTX 3090 class) | fp8 build on RTX 40/50 | No (API) | No (API) |
| API price | Free to self-host; Alibaba Cloud pricing TBA | fal / Replicate per-image; BFL API | Per token, ~$0.21 per 1024² at max quality | ~$0.039 (1K) – $0.24 (4K) per image |
| Commercial use | Separate licence from Alibaba | BFL commercial licence | Yes | Yes |
| Independent benchmarks | None yet | Yes | Yes | Yes |
Verified September 21, 2026 against the Qwen Hugging Face model card and GitHub repo, BFL’s FLUX.2 announcement, and OpenAI’s and Google’s image pricing pages.
What Qwen-Image-2.1 changes
Alibaba framed the release around “compact and efficient”: a 7B single-stream DiT with mixed-granularity attention and prefix KV-cache reuse, so multi-reference edits do not re-encode every reference each step. Four things are new relative to the open-image field:
- Native transparency. One model generates regular or RGBA images from text, edits existing transparent layers (change the text on a sticker without touching the alpha), and extracts a subject from an ordinary photo as a transparent layer. The recommended prompt literally states “This is an RGBA image with transparency… the background is transparent.”
- Ten references. Group photos assembled from individual portraits, virtual try-on, and room design from furniture references, with identity preservation for people and products.
- Annotation-driven local edits. Circle a region or paint a mark on the input and describe the change; no separate inpainting model.
- Typography and portrait lighting improvements — Alibaba’s showcase leans on legible text rendering, historically the gap between open and closed models.
Setup is pip install transformers>=5.17 plus diffusers from git; QwenImage21Pipeline handles text-to-image, editing and RGBA in one class, and enable_model_cpu_offload() gets it onto smaller cards. Supported aspect presets run from 1:1 (2048²) to 16:9 (2752x1536).
The catch is the licence. Qwen-Image 1.0 (August 2025) was Apache-2.0; Qwen-Image-2.1 ships under the Qwen Research License Agreement — non-commercial only, separate grant for commercial use, “Built with Qwen” attribution. Alibaba’s closed Qwen-Image-3.0 (July 21, 2026; 4.5K-token prompts, 10px text, 12 languages) remains API/app-only with no weights, so 2.1 is the open line, and the open line just got a more restrictive licence.
How the other three compare
FLUX.2 [dev] is still the reference open-weight model for quality: 32B, text-to-image plus multi-reference editing in one checkpoint, up to 4MP, strong typography and world knowledge. Its weakness is footprint — 32B in bf16 is a data-centre model, and consumer use depends on the NVIDIA/ComfyUI fp8 build. BFL’s open-core model means [dev] is non-commercial by default with paid commercial terms, while [pro] and [flex] are API-only and the small [klein] variants (January 2026) cover the fast/cheap tier. See GPT Image 2 vs MAI-Image-2 vs FLUX.2.
GPT Image 2.5 (September 8, 2026) is the instruction-following leader: arbitrary resolutions to 3840x2160, native transparent backgrounds, up to 16 references, and two API variants — Flare for speed, Sunburst for precision (which to pick). It is per-token billed, roughly $0.21 for a 1024² image at max quality and about $0.40 at 4K max.
Nano Banana Pro (Gemini 3 Pro Image) is the cheapest route to true 4K and the best conversational editor, with web-grounded factual text for infographics; Nano Banana 2 is the high-volume sibling. Full breakdown in ChatGPT Images 2.5 vs Nano Banana Pro vs Midjourney V8.2.
Where each one wins
- Transparent assets (stickers, product cut-outs, UI icons): GPT Image 2.5 if you want an API; Qwen-Image-2.1 if you want it local and free to run for research or internal tooling.
- Self-hosted, best absolute quality: FLUX.2 [dev], until independent evals of Qwen-Image-2.1 land.
- Self-hosted on one consumer GPU: Qwen-Image-2.1 (7B) or Z-Image-Turbo (6B); FLUX.2 [dev] only in fp8.
- Multi-person composites from references: Qwen-Image-2.1 and GPT Image 2.5 (10 and 16 references respectively) — FLUX.2 also handles 10 but without annotation-guided local edits.
- Cheapest 4K at volume: Nano Banana Pro.
- Commercial product, no legal friction: GPT Image 2.5, Nano Banana Pro, or FLUX.2 with a BFL commercial licence. Not Qwen-Image-2.1 without Alibaba’s grant.
The honest caveat
Alibaba’s claim that a 7B model beats “most closed models” rests on Qwen’s own benchmark, and the-decoder and others noted independent results were pending on release day. Multi-reference identity preservation and RGBA edge quality are exactly the properties vendor benchmarks flatter. If you are choosing for production, run your own 50-prompt bake-off; if you are choosing for experimentation, the 24GB footprint and the RGBA pipeline make Qwen-Image-2.1 the most interesting open image release since FLUX.2.
For the wider open field — Z-Image, HunyuanImage 3.0, FLUX.2 [klein] — see best open-weight image models 2026.