Why Hy Image 3.5 Preview Matters: What Held Up When I Tested It

Tencent Hunyuan dropped Hy Image 3.5 Preview, positioning it less as a benchmark-chasing frontier model and more as a workhorse for professional creative production. It takes text prompts plus up to five reference images, outputs at 1K/2K/4K, and bills only on output pixels, meaning prompts and reference images are free.

The number every outlet is repeating is the +30% win rate over Hy Image 3.0 in human evaluation, which replaced the earlier internal ranking Tencent had floated pre-launch. That's a cleaner claim, but it's still Tencent grading its own homework. Treat it as directional, and wait for independent numbers.

Pricing landed at $0.024 per image internationally for 2K and below. Generation clocks in around 20 seconds.

What Developers Are Saying

The interesting part is what's showing up in the first 24 hours of hands-on testing.

On X, early testers are zeroing in on the text-rendering claim specifically. One tester ran a billboard mockup with six separate text layers alongside an 85mm product shot in the same session and reported the model held up on both, which is notable because multi-layer text rendering is where most diffusion models still fall apart. That tracks with Tencent's own showcase material, which leans heavily on long-form Chinese infographics and modular layouts as the model's signature strength. reddit

Tencent's own account is soliciting community submissions, asking users to "share some genius cases generated by Hy Image3.5 preview," which is a smart way to crowdsource proof points fast while the model is still in preview.

There's also a healthy dose of skepticism carried over from the Hy Image 3.0 launch cycle. When 3.0 shipped last September, Reddit's r/StableDiffusion had a visible split: one widely upvoted thread called it "perfect," while a direct rebuttal thread pushed back hard, arguing that anyone who has stress-tested the model outside curated demos would stop short of calling it flawless. That pattern (strong launch showcase, more mixed results once people go off-script with their own prompts) is worth remembering before you take any vendor's 18-case highlight reel at face value. I'd expect the same split to play out with 3.5 once people move past the demo prompts. So I moved past the demo prompts myself.

I Put It to the Test: 36 Calls, $0.84

On September 23, I sent 36 requests to the GMI Cloud API. 35 came back with an image, so the bill was $0.84. These are small samples, so read the numbers as a first look.

The price is the same at every size

The first thing I checked was the 4K question. On GMI, 2K is the current sweet spot for both quality and cost. The largest image I got was 2048×2048, about 4.2 megapixels. The $0.032 4K price belongs to other access paths.

The good news is that GMI charges $0.024 for every image, at any size. A 2K image costs the same as a 1K image and gives you four times the pixels. The only extra cost is a few seconds of wait time.

Two small surprises. A 1920x1080 request comes back nearly exact at 1920×1072, so video work needs a crop or pad step. And when I left the size on "Auto," the model picked portrait on one run and landscape on the next. In production, set the size yourself.

Speed matched Tencent's claim for most calls. A new image took 15.5 seconds at the median. Full 2K squares landed right at 20 seconds. Edits were slower, at about 23 seconds.

Headlines are great, small print is shaky

Next, I tried the six-layer billboard idea from X. I asked for a headline, a subhead, a price, a legal line, a website, and a small side label.

First run: all six text layers came out correct.

The first run was perfect. I ran the same prompt again, and the second image was weaker. The model kept the subject centered and sharp inside the frame.

First run: all six text layers came out correct.

Then I tried a chalkboard menu with six items and prices. Every word was spelled correctly. One price changed: I asked for a $3.75 croissant, and the board says 3.00.

Every word is correct. The croissant price is wrong.

Edits stayed consistent

This was the claim I trusted least, so I pushed on it. I made one product shot of a red mug with a logo. Then I fed each result back in as the reference for the next edit: a marble table, then steam, then a night cafe, then a croissant, and finally a navy mug.

The base image, then five edits in a row, left to right and top to bottom.

It held up better than I expected. After five edits, the mug shape, the handle, and the BREWHAUS logo stayed the same. Each edit changed only what I asked for.

I also gave it two reference images and asked it to put both mugs on one counter. Both mugs kept their look. The one flaw is that the red handle overlaps the navy mug and hides a letter.

Two reference images merged into one scene.

Speed under load, errors, and seeds

I sent 10 requests at the same time. All 10 finished in 14.5 seconds, and each one took between 11 and 14 seconds. At this small volume, I saw zero queuing.

35 of 36 calls returned on the first try, with one clearing on a quick retry

Seed behavior is still being finalized during preview, so I locked size and prompt instead for repeatability.

Same seed, same prompt, same size, two different images.

My Take

The most credible part of this release is the pricing structure, more than the benchmark claim. Charging the same rate for generation and editing, and billing zero for reference images, removes a pricing quirk that's annoyed developers on other platforms for a while (editing workflows often cost more per call than generation, which discourages the iterative "generate, tweak, generate again" loop that produces good creative output). My tests confirm it on GMI: an edit costs the same $0.024 as a fresh image, and a 2K image costs the same as a 1K image. If Tencent holds that pricing structure past preview, it changes the math for teams doing high-volume iterative design work, beyond one-shot generation.

Where I stayed skeptical: the 4K claim. Tencent's launch deck advertises 4K via upscaling, but most third-party coverage of the shipped API caps general access at 2K, with 4K availability varying by platform. On GMI, I can confirm the cap: the resolution options stop at 2K, and the largest image I got was 2048×2048. If your use case depends on 4K output for print or large-format assets, verify the resolution ceiling on your specific access path before committing a pipeline to it.

The edit-consistency claim is the one I wanted to pressure-test hardest, with my own reference images instead of Tencent's. "Stays consistent across multiple edits" is exactly the kind of claim that holds up beautifully in a curated 18-case showcase and gets shakier the moment you run 50 of your own product shots through it. In my five-edit chain, it held. The mug and the logo stayed fixed from the first image to the last. One product is a small sample, so run your own set before you trust it at scale.

Text needs a check step. The model got every headline right, but it broke small print and changed a price. A quick OCR pass that compares the text in the image with the text in your prompt catches most of these errors. And until seeds work, save every image URL and prompt, because the same seed gives you a new image each time.

Here is how I would use it tomorrow: a product-shot or ad-variant pipeline at 2K, with the size set explicitly and one retry on errors. At $0.024 an image, making three and picking the best one costs less than an engineer's time spent chasing one perfect prompt.

Try It on GMI Cloud

Hy Image 3.5 Preview is live in GMI Cloud's Inference Engine right now under hy-image-v3.5-preview, so you skip the wait on Tencent's own API queue and the mainland vs. international routing question.

Playground: Test it interactively with your own prompts and reference images before writing any code. Try it in the Playground

API: Once you've validated quality on your prompts, call it directly:

curl -X POST "https://console.gmicloud.ai/api/v1/ie/requestqueue/apikey/requests" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "hy-image-v3.5-preview",
    "payload": {
      "prompt": "A misty mountain village at sunrise, traditional architecture, soft golden light.",
      "size": "1920x1080",
      "generate_max_pixels": 4194304
    }
  }'

This request returns a 1920×1072 image. Swap in reference images with the image field (up to five URLs) and iterate on the same subject to pressure-test the edit-consistency claims yourself rather than trusting the highlight reel. Full request/response schemas are in the API docs.

The Question to Answer

Go further than asking "is Hy Image 3.5 Preview good." Ask it against your own workload. If you're doing high-volume iterative editing (product shots, game UI variants, ad creative at scale), the flat generate/edit pricing alone might justify testing it seriously, and my edit test adds a second reason. If you need guaranteed 4K for print, GMI tops out at 2K today, so confirm the ceiling on any other platform before you build around it. And if text rendering is your bottleneck, that's the one area where both Tencent's showcase and independent early testers agree it delivers. My tests agree for headlines. Add a check step for small print and numbers.