From 3DS Max to 60 Seconds: The State of AI 3D Generation

If you’ve ever used 3DS Max, Blender, or Maya, you know the pain. Model a character? Days. Texture it? More days. Rig it? Don’t even ask. A single production-quality 3D asset can take a skilled artist weeks to complete.

AI 3D generation promises to compress that into seconds. And after testing every available option — direct APIs, serverless platforms, open-source models — we can say this: the promise is partly real. Some of these tools produce genuinely usable assets. Others produce what can only be described as a bad dream rendered in polygons.

Here’s what we found.

Tripo: The One That Actually Works

Tripo was the standout. By a wide margin.

We integrated their direct API — not through fal.ai, not through HuggingFace, direct. And the difference shows. Text-to-3D in about 60 seconds. Image-to-3D in about the same. The output is a GLB file with PBR textures — physically-based rendering materials that work in any modern 3D engine, from Three.js to Unreal.

Tripo direct API results:

• Text-to-3D: ~60 seconds, consistent quality
• Image-to-3D: ~60 seconds, respects reference image well
• Output: GLB with PBR textures (metallic-roughness workflow)
• Cost: ~$0.20 per model
• Cold starts: None — always-on infrastructure
• Reliability: Every request completed in testing

We built a full /3d page on Zubnet around Tripo’s capabilities, using the <model-viewer> web component from Google to render the results in-browser. Users type a prompt, wait a minute, and get a rotatable, zoomable 3D model they can download and drop into their project.

The quality isn’t going to replace a senior character artist. But for prototyping, game jams, e-commerce product mockups, or architectural concept models? It’s genuinely useful. A $0.20 model that takes 60 seconds beats a $500 freelancer model that takes five days — especially when you’re iterating on concepts.

The key insight: Tripo runs their own infrastructure. No serverless cold starts. No middleman queues. You hit their API, they process your request on GPUs they manage, and you get your result. This is the direct API pattern in action — fewer layers, more reliability.

Trellis 2 via fal.ai: Promising But Unreliable

Trellis 2 is Microsoft’s open-source 3D generation model, and the results can be impressive. The geometry is often cleaner than Tripo’s, with better topology for downstream editing. On paper, it’s excellent.

In practice, we couldn’t rely on it.

Trellis 2 isn’t available as a direct API. The main way to access it is through fal.ai’s serverless GPU platform. And this is where the wrapper economy problem hits hard. fal.ai runs Trellis 2 on serverless GPUs, which means:

Trellis 2 via fal.ai — the problems:

• Cold starts — First request of the day? Wait 3–8 minutes for the GPU to spin up and load model weights
• Image download failures — Trellis 2 takes an image URL as input, but fal.ai’s serverless environment couldn’t download images from many common hosting services
• Inconsistent availability — Some days it worked perfectly, other days the queue timed out
• No SLA — Serverless means nobody guarantees your model will be warm when you need it

The image download issue was particularly frustrating. We’d upload a reference image to our CDN, pass the URL to fal.ai, and Trellis 2 would fail because the serverless container couldn’t reach the URL. Same image, same URL, worked fine when fetched from anywhere else. The serverless environment’s networking was the bottleneck.

If Microsoft or another provider offers Trellis 2 as a direct API with dedicated infrastructure, it could be a serious competitor to Tripo. But running it through fal.ai’s serverless platform? Not production-ready.

Cosmos Predict 2.5: Not 3D, and Not Great

NVIDIA’s Cosmos Predict 2.5 sounds promising — world-model prediction from a reference image. We tested it through fal.ai expecting some form of spatial understanding or 3D-aware generation.

What we got was video. Specifically, short video clips that looked like someone fed a photograph through a kaleidoscope while on ayahuasca. The “prediction” had no spatial coherence. Objects warped, colors shifted unpredictably, and the output bore only a passing resemblance to the input.

We ran Cosmos Predict 2.5 three times with different inputs. Each time, the result was more psychedelic than the last. This is not 3D generation. It’s not even useful video generation. It’s a tech demo that got shipped too early.

To be fair, Cosmos Predict is designed as a world model for robotics and autonomous systems, not for consumer 3D generation. But fal.ai lists it alongside 3D models, which sets wrong expectations. If you’re looking for 3D assets, this isn’t it.

The Scorecard

Tripo (direct API) Quality: ████████░░ 8/10 Speed: █████████░ 9/10 (~60s) Cost: █████████░ 9/10 ($0.20) Reliability: █████████░ 9/10 Verdict: Production-ready Trellis 2 (via fal.ai) Quality: █████████░ 9/10 Speed: ████░░░░░░ 4/10 (cold starts) Cost: ████████░░ 8/10 Reliability: ███░░░░░░░ 3/10 Verdict: Not production-ready Cosmos Predict 2.5 (via fal.ai) Quality: ██░░░░░░░░ 2/10 Speed: ██████░░░░ 6/10 Cost: ████████░░ 8/10 Reliability: ████░░░░░░ 4/10 Verdict: Not recommended

What This Means for Different Industries

Game Development

AI 3D generation is ready for prototyping and indie development. If you’re building a game jam entry or prototyping level layouts, $0.20 models in 60 seconds is transformative. You can generate dozens of asset variations, pick the best ones, and refine them in Blender. For AAA studios, these are concept tools, not production tools — yet.

Architecture and Interior Design

This is where AI 3D generation could have the fastest practical impact. Architects don’t need film-quality characters — they need furniture, fixtures, structural elements, and concept models. Tripo’s PBR output drops directly into architectural visualization tools. Generate a dozen chair variations for a client meeting? That used to be a week of work. Now it’s twenty minutes and $2.40.

E-Commerce

Product visualization is a massive market, and most small e-commerce businesses can’t afford 3D product shots. Image-to-3D changes that equation. Upload a product photo, get a rotatable 3D model for your product page. The quality needs to improve for luxury goods, but for general merchandise, it’s already viable.

The Infrastructure Lesson

The biggest takeaway from our testing isn’t about model quality. It’s about infrastructure.

Tripo works because they own their inference stack. No cold starts. No shared queues. No serverless gambling on whether a GPU will be warm. They invested in always-on infrastructure, and it shows in every request.

Trellis 2 might produce better geometry in ideal conditions, but “ideal conditions” on a serverless platform means “someone else warmed it up for you.” That’s not a foundation to build a product on.

The rule of thumb for 2026: If a 3D model is only available through serverless inference (fal.ai, Replicate), treat it as experimental. If it’s available through a direct API with dedicated infrastructure, it’s worth integrating. The model quality matters, but the infrastructure reliability matters more.

3D generation is where image generation was two years ago — clearly going somewhere, not quite there yet for all use cases, but already useful for the right ones. The frontier is moving fast. Tripo ships updates monthly. Trellis 3 is presumably in development. New players are entering constantly.

If you’re building anything that touches 3D — games, architecture, e-commerce, education, manufacturing — start experimenting now. The tools are cheap enough to test. And the ones that work today will only get better.

All testing was done in March 2026 using production API endpoints. Models were evaluated on real prompts, not cherry-picked demos. We have no financial relationship with Tripo, Microsoft, NVIDIA, or fal.ai.

Try AI 3D generation yourself at zubnet.com — Tripo integration is live on the /3d page.