Two of the strongest open-source image models of the past year both come from China, both use the Apache-2.0 license, and both claim leading text rendering. Yet almost every comparison you'll find online is written from neither model's actual numbers. This one cites primary sources throughout — including where LongCat-Image (the model this site runs) loses.
Short version: they win at different things. Choose LongCat-Image for Chinese text rendering and lower deployment cost; choose Qwen-Image for general aesthetics, a bigger ecosystem, and cheaper API calls.
The Numbers Side by Side
| LongCat-Image | Qwen-Image | |
|---|---|---|
| Developer | Meituan | Alibaba Qwen team |
| Weights released | 2025-12-05 | 2025-08-04 |
| Architecture (DiT backbone) | 6B | 20B MMDiT |
| DiT weights (bf16) | 12.5 GB | 40.9 GB |
| License | Apache-2.0 | Apache-2.0 |
| ChineseWord benchmark | 90.7% | 56.6–58.3% |
| Local VRAM (official/community) | ~17 GB with CPU offload | ~24 GB full precision, ~16 GB FP8 |
| ComfyUI | Native since 2026-03 | Native since 2025-08 |
| API price | $0.13/MP (fal.ai) | ~$0.02–0.035/image (Replicate/fal) |
Where LongCat-Image Wins: Chinese Text Rendering
The headline gap is in-image Chinese text. On the ChineseWord benchmark — 8,105 characters, the full national standard set — LongCat's technical report measures 90.7% accuracy for LongCat-Image vs 56.6% for Qwen-Image (with Seedream 4.0 at 58.5%).
Is that self-serving? Here's the cross-check: Qwen's own technical report self-reports about 58.3% on the same benchmark — within 1.7 points of what LongCat's team measured for it. The two reports, written by competitors, agree. For rare characters, dense signage, menus, packaging, or long Chinese passages inside an image, the gap is real and large.
Honest caveat: LongCat's 90.7% is a self-reported number; no independent lab has replicated the head-to-head yet. But since both sides' published numbers corroborate each other on Qwen's score, the direction of the gap is well-supported.
The second win is deployment weight. A 6B backbone (12.5 GB) versus 20B (40.9 GB) means LongCat runs with ~17 GB using CPU offload per the official model card, and community GGUF files currently go down to 2.1 GB, with severe quality trade-offs at the smallest quant. Qwen-Image full precision wants a 24 GB card; FP8 needs ~16 GB; GGUF Q4 runs on 8–12 GB with visible quality cost.
Where Qwen-Image Wins: Ecosystem, Aesthetics, and Price
Fairness requires the reverse list, and it's substantial:
- General visual quality. LongCat's own report concedes Qwen-Image scores higher on Visual Aesthetics. On Artificial Analysis' arena, LongCat-Image sits around #99 — its strength is specialized, not universal.
- Ecosystem maturity. Qwen-Image shipped four months earlier and has native ComfyUI support since August 2025, plus a deep bench of LoRAs, Lightning distills (4–8 step generation), and quantization options. LongCat's native ComfyUI support arrived in March 2026 and its LoRA ecosystem is younger.
- API cost. Qwen-Image runs roughly $0.02–0.035 per image on Replicate and fal — around a quarter to a sixth of LongCat's $0.13/MP. At volume without Chinese-text needs, Qwen is clearly cheaper.
- Edit iteration speed. Qwen-Image-Edit has shipped three revisions (original → 2509 → 2511). LongCat countered with Edit-Turbo (2026-02, ~10× faster distill), but Qwen's edit line has had more real-world bake time.
The Decision Rule
- Text inside the image, especially Chinese — signage, posters, menus, packaging, UI mockups with CJK: LongCat-Image. The benchmark gap (90.7% vs ~57%) is the largest capability delta between the two in either direction.
- General art, portraits, stylization, high-volume cheap generation: Qwen-Image. Better aesthetics scores, richer tooling, lower per-image cost.
- Low-VRAM local deployment (under 16 GB): LongCat-Image via GGUF — the smaller backbone quantizes to sizes Qwen can't reach at comparable fidelity. See our local deployment guide.
- Bilingual e-commerce/marketing assets (accurate CN + EN in one image): LongCat-Image — this is precisely what it was trained for.
Anyone telling you one model "beats" the other across the board is selling something. These are different tools with one overlapping ambition.
Try the Text-Rendering Claim Yourself
The fastest way to settle it for your use case is a same-prompt test. Longcat Image runs LongCat-Image in the browser with free signup credits — prompt it with quoted Chinese text (a storefront sign that says "兰州牛肉面") and compare against your current model's output. Our online usage guide covers the prompting details.
Sources: LongCat-Image technical report (arXiv 2512.07584) · Qwen-Image technical report (arXiv 2508.02324) · LongCat-Image model card · Qwen-Image weights · fal.ai pricing · Artificial Analysis
