Best Open-Source Image Models Right Now: Hunyuan Image 3 vs Qwen-Image vs Z-Image-Turbo

“Open source” image generation has entered a new era. Not long ago, the best-looking images mostly lived behind closed APIs. Today, some of the most capable image models are open-weight (and in some cases fully open-sourced), meaning creators can: run locally or self-host fine-tune / build workflows around them ship products without getting locked into one provider control cost (especially at scale) In this post, I’m comparing three trending open models you’ll see everywhere right now: Hunyuan Image 3 Qwen-Image Z-Image-Turbo I’ll share: where each model shines what it struggles with example prompts to test and a practical cost comparison (because cost matters if you generate a lot) Note: I’ll include real example images in each section (I generated these and added them below). Quick takeaways (if you just want the verdict) Hunyuan Image 3 : best “all-around premium” look, but costs the most . Qwen-Image : strong quality + great text rendering for an open model, with low cost . Z-Image-Turbo : the speed + cost king — amazing for high-volume generation and iteration. What “open source” means here (real talk) In the image world, “open source” often means open weights + a license that allows broad usage. The licenses differ: Qwen-Image is released under Apache-2.0 (very permissive). Z-Image-Turbo is also distributed under Apache-2.0 (including commercial use). Hunyuan Image 3 is open-sourced under Tencent’s community license (still broadly usable, but it’s not Apache/MIT — always read the license). That matters if you’re building a product, doing client work, or training derivatives. Model #1: Hunyuan Image 3 (premium look, premium cost) Hunyuan Image 3 is one of the biggest open releases we’ve seen recently, built as a large MoE image generation model. Where it shines Aesthetics: premium, polished outputs that feel “art-directed” strong composition and mood (cinematic stills, editorial visuals) great “final image” model when you want it to look expensive Where it can be weaker cost adds up fast if you’re iterating a lot if your goal is speed/volume, it might not be the best default 📸 Example gallery Press enter or click to view image in full size Press enter or click to view image in full size Press enter or click to view image in full size hunyuan-image-3 Model #2: Qwen-Image (best balance: quality + text + price) Qwen-Image has become a favorite because it feels like a “serious” foundation model: strong generation quality and surprisingly good text handling for an open model. Where it shines Text rendering: cleaner spelling and more stable typography great for posters, thumbnails, UI concepts, product labels strong balance of quality + cost for everyday generation Where it can be weaker on some super high-end photoreal scenes, you may need more retries than the top premium models some niche styles may require tighter prompting 📸 Example gallery Press enter or click to view image in full size qwen-image Model #3: Z-Image-Turbo (the speed & cost monster) Z-Image-Turbo is built for fast generation . It’s the model you use when you want: many variations rapid prompt iteration high volume at low cost Where it shines Photorealism: realistic lighting, materials, and natural-looking scenes extremely cost-effective for high-volume generation + iteration great for “concept → refine” workflows (generate many candidates quickly) Where it can be weaker if you’re chasing the most premium final-frame detail, you may want to “finish” in another model turbo models sometimes trade a bit of fine detail for speed 📸 Example gallery Press enter or click to view image in full size z-image-turbo Now you try these prompts to test the models on BP Image Studio . Prompt A: Premium portrait realism A cinematic portrait photo of a person at night in a neon city, shallow depth of field, realistic skin texture, natural bokeh, no plastic look, ultra sharp. Prompt B: Product shot with label text A studio product photo of a matte-black water bottle on a white seamless backdrop. Add a clean label that reads “BUDGETPIXEL” in uppercase sans-serif. Minimal reflections. Prompt C: Typography poster (hard mode) Design a clean event poster. Title: “WINTER NIGHT MARKET”. Subtitle: “Dec 20 • 7–11 PM”. Footer: “Atlanta • Free Entry”. Perfect spelling, aligned, no extra words. Prompt D: Illustration / design style Editorial illustration style: flat shapes, subtle gradients, crisp edges, minimal noise. A person holding an umbrella walking under neon lights. Prompt E: Composition stress test A cozy cafe interior with warm lighting. Foreground: a cappuccino with latte art. Background: a person reading. Midground: a hanging plant. Realistic perspective and depth. Pricing comparison (why Hunyuan feels expensive) Pricing varies by provider and image size, but here’s the general reality: Hunyuan Image 3 tends to be priced around ~$0.06 per image on some API providers. Qwen-Image is commonly ~$0.015 per image on some providers (often ~4–5× cheaper than Hunyuan). Z-Image-Turbo is frequently ~$0.015 per megapixel making it the cheapest at scale. Simple rule of thumb If you generate a lot , Z-Image-Turbo is the budget champ. If you want best value , Qwen-Image is hard to beat. If you want premium polish , Hunyuan Image 3 is worth it — just be deliberate. My recommended workflow (what I’d do as a creator) If you want both quality and cost efficiency: Start with Z-Image-Turbo to explore ideas fast (generate lots of variations). Switch to Qwen-Image when you need better typography or a more “designed” look. Finish with Hunyuan Image 3 when you’re ready for the premium final version. That workflow saves money and saves time. Want me to review your examples? If you publish this post with your comparison images, you can also post them on BudgetPixel Feeds so other creators can comment, remix prompts, and compare outputs. Model rankings & leaderboards (useful for staying current) If you want a quick pulse on which image models are trending right now (based on public votes and benchmarking), these two sites are worth bookmarking: LMArena (Text-to-Image Arena + Image Edit Arena) — crowd-voted comparisons across image generation and image editing. LMArena+1 Artificial Analysis (Image model rankings + price/speed/quality comparisons) — side-by-side model + provider comparisons and leaderboards. Final thoughts on Open Source Chinese open image models vs American “open” image models (why this shift matters) One thing that surprised a lot of creators this year: some of the most competitive “open” image releases are coming out of China . The three models in this post are all Chinese: Hunyuan Image 3 — released by Tencent (Hunyuan). GitHub+1 Qwen-Image — released by Alibaba’s Qwen team, under Apache-2.0 . GitHub+2Qwen+2 Z-Image-Turbo — released by Tongyi-MAI / Alibaba’s ecosystem (Turbo variant), and explicitly positioned as strong on photorealism and fast inference. Hugging Face+1 And here’s the pattern many creators are noticing in practice: Hunyuan Image 3 feels like it’s optimized for aesthetics / “art direction” polish (great hero images). Qwen-Image is especially strong at text-in-image and design-like outputs (posters, labels). Hugging Face+2Qwen+2 Z-Image-Turbo is surprisingly strong at photorealism , while staying fast and cheap , which makes it a great default for high-volume generation. Hugging Face+1 The “call out”: most top American image models are still closed Here’s the blunt reality: in the U.S., the best-known frontier image models are mostly API products , not open weights. OpenAI’s DALL·E 3, GPT Image 1, GPT Image 1.5 are offered through ChatGPT and the API (not an open-weight release). Google’s Imagen and Nano Banana models are available through the Gemini API / Vertex AI, again as an API model (not open weights). So when people say “American image models are leading,” they often mean closed models you rent , not models you can actually self-host, fine-tune, audit, or build on openly . “But what about Stable Diffusion?” To be fair: the U.S. open image story has been largely carried by Stability AI / Stable Diffusion , including SDXL and FLux dev models from Black Forest Lab . But even here, the licensing trend is telling: Stability’s and FLux’s newer releases are under a Community License with conditions (including a revenue threshold for broad free commercial use). That’s still very usable for many indie creators and startups — but it’s not the same simplicity as Apache/MIT-style “do what you want” licensing. Why the U.S. tends to stay closed (and why China is pushing open) This is my take, but there’s a real incentive gradient here: In the U.S., legal and reputational risk is a constant shadow over model releases. Stability has been in major disputes like Getty Images’ lawsuit over training data. Reuters+1 And “training data” lawsuits keep expanding across the industry (even Adobe got hit with a proposed class action recently, though that case was about LLMs rather than image generation). Reuters When your business is exposed to high litigation risk, “ship open weights” becomes harder to justify internally — because open weights are harder to control once they’re out. Meanwhile, Chinese labs and big tech groups have been aggressively releasing open models across categories, and it’s become part of the competitive playbook (example: Reuters coverage of DeepSeek releasing open models and claiming benchmark wins). Reuters What creators should do with this reality Instead of treating this as “country vs country,” treat it as two product philosophies : If you want maximum control and predictable unit economics , open-weight models (a lot of them currently Chinese) are increasingly attractive: you can self-host, swap providers, and avoid lock-in. GitHub+2GitHub+2 If you want the easiest UX and best “out of the box” polish , closed API models (many of them American) will keep being strong — but you’ll pay ongoing costs and accept platform rules. OpenAI+1 My personal “call out” to the U.S. ecosystem: Creators need more than “rentable magic.” We need open releases with clear licensing, reproducibility, and the ability to build long-term workflows without fear of sudden access or pricing changes. Now Let me know what is your take on this?