Wan 2.3 vs. Chinese AI Video Models: A Practical Workflow Guide

The gap between leading AI video models has narrowed to the point where most viewers cannot guess which engine produced a finished shot. That changes the question worth asking. Instead of hunting for one universally best model, the more productive approach is understanding what each family of models does well, then building a process that lets you switch engines without rebuilding everything around them.

Wan 2.3, Alibaba's newer entry in the Wan line of video models, is a useful case study. It arrived in a field already dense with strong systems, each optimising a different balance of motion realism, prompt adherence, clip length, camera control, and raw iteration speed. This guide covers what genuinely differs between the main options, how to evaluate a new release without burning a week of render time, and how to keep your creative decisions portable when the next model lands.

If you prefer to start from the output side, Orelon's AI video generator is a quick way to check what good looks like before you benchmark anything.

Why model comparisons age badly

A comparison written this month can be misleading next month. Model names stay stable while the weights behind them may be retrained two or three times, and a hosted service can change its defaults, resolution ceilings, or safety filters without a new version number. If you treat a published ranking as permanent truth, you will make decisions based on a snapshot of a moving target.

There is also a selection bias problem. Demo reels and community showcases represent the best handful of results from hundreds of attempts. Your first render on any new engine will not look like the highlight reel, and judging it against one is unfair to the model and unhelpful to you.

The durable asset is not a ranking. It is your own test kit: a fixed set of reference images and prompts you run through every candidate model, scored consistently and stored with dates. That log becomes more valuable than any chart, because it reflects your material, your genre, and your tolerance for a given failure mode.

What Wan 2.3 actually brings to the table

Wan 2.3 is best understood as a deliberate iteration rather than a reinvention. The Wan family earned attention by combining coherent motion with relatively open access.

Motion and camera behaviour

Slow camera moves, parallax, and gentle subject motion tend to hold together better than fast, chaotic action. This makes the model a natural fit for establishing shots, product reveals, atmospheric b-roll, and any moment where the camera movement is doing the storytelling. Push it toward a crowded fight scene and the usual artefacts return.

Prompt literacy

The model generally rewards specificity. Describing lens behaviour, lighting direction, wardrobe, and blocking in a single flowing sentence tends to produce better results than a string of keywords. That is useful for diagnosis: if a longer, more precise prompt produces meaningfully better output, the engine rewards craft and is worth writing for.

Image-to-video conditioning

Starting from a strong reference frame remains the most reliable route to a usable shot. The task shifts from inventing a scene to animating a frame convincingly, which is a far easier problem. Character design, composition, and colour palette are already decided, so the model only has to handle motion.

Where it still strains

Complex human interaction, rapid dialogue-driven action, text rendered inside the frame, and large crowds remain the weak spots. Almost every competitor shares them, which is exactly why the comparison is less about a winner and more about matching an engine to a shot type.

The rest of the field in plain terms

Each major option has a recognisable personality, and knowing that personality is more useful than knowing its position on a leaderboard.

Kling built its reputation on motion realism for human subjects. Walking, turning, gesturing, and handling objects tend to read as physical rather than floaty, and early investment in start-and-end frame control made it a favourite for continuity work. A deeper breakdown lives in this Kling AI alternative comparison.

Hailuo, from MiniMax, often excels at stylised, high-energy motion and expressive character animation. Output frequently looks designed straight out of the box, which is an advantage for social-first content and a liability when you need neutral realism.

Vidu leans into speed and reference-driven generation, with strong performance on illustration and anime styles. It is often the pragmatic pick when you need many variations quickly and can refine the best one later.

Open-weight releases occupy a different niche entirely. They trade some polish for control: local execution, custom fine-tunes, and predictable running behaviour once you own the hardware. For teams with steady shot volume and someone comfortable maintaining a pipeline, that trade is often worth it.

The pattern worth noticing is tension. Motion realism, style fidelity, speed, and controllability pull against each other, and each team has optimised for a different balance. No single system dominates every category, and the one that wins today may not win next quarter.

Seven questions to ask before adopting a model

Run every candidate through the same questions. This keeps comparisons honest and prevents the common mistake of judging an engine by its marketing rather than your shot list.

1. How does it handle motion under stress? Test the hardest thing you plan to shoot. If your video involves two people passing an object, generate that instead of a drifting cloudscape. Motion failure is the most visible defect and the least forgiving in an edit.

2. Does it follow long, specific prompts? Write a sixty-word prompt with subject, action, environment, lens, lighting, and mood, then write a ten-word version of the same idea. If the long prompt produces clearly better output, the model rewards craft. If both are identical, stop writing essays.

3. How faithful is image-to-video? Feed it a reference frame with distinctive details such as an unusual jacket, a plain product, or an odd colour palette. Watch whether those details survive. This is the single best predictor of production usability.

4. What are the duration and resolution limits? Most engines generate short clips that you extend or cut together. Find out how many extensions a ten-second shot needs and whether quality degrades on the second or third. Drift on extension is a dealbreaker for dialogue scenes.

5. Is there audio, lip sync, or dialogue support? If your project needs speaking characters, evaluate this separately. Video quality and audio quality are independent pipelines, and a beautiful render with mismatched mouth shapes is unusable.

6. How fast is the iteration loop? Speed is a creative variable, not an administrative detail. An engine that returns a draft in under a minute invites eight experiments. One that takes ten minutes per attempt pushes you toward safe choices.

7. How clean is the export? Check codec, frame rate, alpha support, and whether metadata or watermarks interfere with editing. A model that produces gorgeous clips you cannot cleanly integrate is a hobby, not a tool.

A reusable test kit for fair evaluation

What belongs in the kit

Prepare ten reference images and ten prompts, then reuse them for every evaluation. Cover a range: a portrait, a product on a plain surface, a wide landscape, an interior with mixed lighting, a stylised illustration, and a couple of shots with deliberate motion. Lock the aspect ratio and, where the model supports it, the seed. Keep a notes file with dates and prompt versions.

How to score without fooling yourself

Run all ten through a model you already know to establish a baseline, then the same ten through the newcomer. Do not rewrite prompts to flatter the new arrival; that is how bad decisions get made. Score each result from one to five on motion, fidelity to the reference, and prompt accuracy. Rename the files and review them the next day. Immediate impressions favour novelty, and novelty is not quality.

Turn scores into a role, not a ranking

A model does not need to win overall. It needs to win a category: the one you use for product shots, the fast option for storyboard previews, or the reliable choice for character close-ups. Assigning roles keeps your pipeline stable while letting you adopt genuinely better options as they appear.

Prompt patterns that transfer across engines

The encouraging news is that prompt craft is increasingly portable. Most modern systems respond to the same underlying structure.

Shot grammar: subject, action, environment, camera, light, mood

A prompt such as "a cyclist rounding a wet corner at dawn, low tracking shot, backlit mist, muted teal palette, quiet and tense" describes a shot rather than a theme. Keep a set of reusable building blocks in a prompt library so you are not rewriting from scratch every session.

Name the camera move explicitly

"Slow dolly in," "handheld follow," and "static wide" produce visibly different results. Ambiguity here is the most common cause of disappointing output. If you cannot describe the move in three words, the model cannot execute it.

Front-load what matters

If the subject is the point, describe it first. Later clauses tend to carry less weight in most implementations, so burying the character at the end of a long sentence is a quiet way to lose control.

Keep negatives short

Long exclusion lists often confuse the model rather than constrain it. State what you want, then add one or two specific exclusions if they matter. Iterate one variable at a time — change the lighting or the camera move, never both, or you learn nothing from the comparison.

Plan continuity across shots

For sequences, decide in advance which elements stay fixed: wardrobe, palette, time of day, lens feel. Generate the wide shot, the medium, and the close-up from related reference frames rather than from unrelated text prompts. Continuity is a planning problem before it is a generation problem.

Building a model-agnostic pipeline

The teams that adapt fastest share one habit: their process does not depend on any single engine. That comes from a few deliberate choices.

Write the story in plain text

Story beats, shot lists, and scripts should exist as documents before any model is opened. If you are writing your story inside a prompt box, you are letting tooling dictate narrative. Plain text survives every platform change.

Generate stills before motion

When a shot needs a specific composition, create the frame first with an AI image generator, then animate it. This is usually faster than coaxing a video model from text alone, and it gives you something to reject cheaply before spending render time.

Edit for rhythm, not for length

Cut sooner than feels comfortable. Short clips hide motion artefacts and give you more control in the edit. Extension should be a fallback, not a default habit.

Store assets in neutral formats

Reference frames, audio stems, and timelines should never be locked to one platform's export quirks. Neutral formats mean you can move a project between tools without redoing work.

Reuse structural templates

Product teasers, explainer openings, and social cutdowns all benefit from a consistent shape you refill with new footage, which is what video templates are for. A recognisable structure also makes a small team look consistent across a whole campaign.

Keep a small stable of models

Two or three engines, each with a defined role, beats chasing every release. When something genuinely better appears — the wider list of AI video generator alternatives is a reasonable starting point — swap it into one role only and re-run your test kit before trusting it broadly.

Mistakes that make a new model look worse than it is

Most frustration with a fresh engine traces back to habits imported from the previous one.

• Reusing prompts word for word. Every engine has its own vocabulary biases, and a prompt tuned for one may read as noise to another.
• Judging on first attempts. New models often need five to ten generations before you understand their sweet spot.
• Ignoring aspect ratio. Vertical and widescreen formats are not interchangeable. An engine that shines at 16:9 may fall apart at 9:16.
• Skipping the reference frame. Pure text-to-video is still the least controllable mode. If you have a frame, use it.
• Over-extending clips. Chaining four extensions for a long take usually produces drift. Cut sooner and shoot another angle.
• Testing on your most important project. Use a sandbox scene, not the client deadline.
• Comparing against curated showcases. Those were selected from hundreds of attempts. Expect your first pass to be rougher and plan accordingly.

When to switch models and when to stay put

Switching has a real cost: new vocabulary, new quirks, new failure modes to learn. Switch when a candidate wins a category you rely on by a clear margin in your own test kit, when your current tool changes in a way that hurts your workflow, or when a new capability such as longer clips or stronger camera control unlocks a shot you could not previously attempt.

Stay put when the difference is cosmetic, when the gain only shows up in someone else's demo, or when you have a deadline inside the learning curve. Set a review rhythm — twice a year, or after any release that changes what is possible in a category you depend on. Re-run the kit rather than trusting announcements.

FAQ

Is Wan 2.3 better than Kling or Hailuo? There is no universal winner. Wan-class models tend to reward detailed prompting and strong reference frames, Kling is often praised for human motion realism, and Hailuo for expressive, stylised animation. The right answer depends on your shot type.

Should I run open-weight models locally instead of using hosted tools? Local execution makes sense when you have predictable shot volume, suitable hardware, and someone comfortable maintaining a pipeline. For most creators, hosted tools win on speed, convenience, and the absence of maintenance work.

How long should a generated clip be? Aim for the shortest clip that covers the action, then cut. Short clips hide artefacts and give you more editorial control. Extension is a fallback, not a default.

Do I need a reference image? It is not required, but image-to-video consistently produces more controlled results. Starting from a strong frame removes ambiguity about composition and character design.

How do I compare engines without wasting render time? Use a fixed kit: ten reference images and ten prompts, the same aspect ratio, blind review the next day, and consistent scoring. Never tailor prompts to flatter the newcomer during a comparison.

What is the fastest way to improve output quality? Better inputs. A well-composed reference frame plus a prompt that specifies camera, light, and mood will outperform any amount of parameter tinkering.

Will prompt skills transfer if I change tools? Largely yes. Shot grammar, explicit camera language, and front-loading the subject are portable. Only the fine vocabulary shifts, and a short calibration session settles it.

Where this leaves creators

Model releases will keep arriving, and the differences between them will keep shrinking in the categories that matter most. That is good news. Competitive advantage shifts away from which tool you happen to have access to and toward the parts that were always hard: clear ideas, strong framing, and a process that survives the next upgrade.

Pick two or three models and give each a defined role. Build a fixed test kit and keep scoring notes. Write prompts that describe shots rather than themes, and keep your story in plain text where no platform can lock it in. Then keep the review rhythm going instead of chasing every headline.

When you are ready to put it together, start a project on Orelon and turn your cinematic ideas into motion. If you want more breakdowns like this one, the Orelon blog is updated regularly.