Wan 2.5 | An Honest AI Video Generator Review

Nov 3

Note: This Review is Non-Biased and Not Affiliated with Wan.

In this article, we will give you an in-depth breakdown of theAI Video Generator, Wan 2.5.

Wan 2.5 was created by a company out of China, and has also created other tools, such as Wan 2.2 animate that is a popular performance capture tool.

Wan 2.5 Specs:

Up to 10 Seconds of Video
Generate Videos in 1080p
Accessible through third-party Platforms
Open Source
24 Frames Per Second
Native Audio synced with the Videos

You have most likely at least heard of Wan 2.5 because it is an open source model that also lives on some of the cloud-based platforms that offer multiple APIs.

You can find this model on Higgsfield and Krea if you have either of those subscriptions.

Wan 2.5 - Benchmark Score (6.3/10)

In our Curious Refuge Labs™ review, Wan 2.5 was scored across five categories: Prompt Adherence, Temporal Consistency, Visual Fidelity, Motion Quality, and Style & Cinematic Realism. The average scores were:

Prompt Adherence: 7.0/10
Temporal Consistency: 6.6/10
Visual Fidelity: 6.5/10
Motion Quality: 5.9/10
Style & Cinematic Realism: 5.7/10
Total Curious Refuge Labs™ Score: 6.3/10

Wan 2.5 ratings have definitely progressed from the previous model, Wan 2.2. Though its prompt adherence is strong, it lacks heavily in motion quality and realism.

Wan 2.5 | AI Video Expert Review

Below is a detailed review of how Wan 2.5 performs against the categories listed above.

In this article, we will not address the audio, but only the visual capabilities. We will address the audio abilities in a future review.

Prompt Adherence — 7.0/10

Across all eighteen tests, prompt adherence stands out as Wan 2.5 Pro’s most consistent strength.

Over-the-shoulder Convo and Two People Talking illustrate this strength, the eye-lines are locked, gestures/props hit their marks, and blocking unfolds exactly as written. But the subject’s movements aren’t subtle (as prompted), they’re quite large.

Animations show average adherence, with one shot scoring a nine.

In 2D Character Talking, the final shot reads like studio-ready, classic 2D character animation. The speaking frog even leans in to punctuate his phrases with rhythmic arm motion that captures our prompt’s “dynamic story.”

This is our simplest prompt, and it’s also helped by the fact that every frame happens in a contained setup i.e. a log in the woods.

This means that Wan can focus on following directions instead of worrying about complex environmental, motion, or lighting elements.

Wan’s own documents explicitly instruct users to use “camera-movement tokens.” The beneift of this was evident throughout the majority of our tests.

When using Wan 2.5, prompt adherence rises drastically when prompts do two things:

1) Describes a single, self-contained shot rather than a multi-beat sequence.
2) Use camera language, which means framing, angle, and motion, to anchor the composition.

The more your prompts sound like a director composing a shot, the higher your adherence will be.

Temporal Consistency — 6.6/10

Results with the greatest temporal consistency are generated from simple, clearly-framed compositions. This over-the-shoulder Convo is the standout, scoring a rare 10/10.

The 2 shots above seem to be outliers when it comes to Wan 2.5’s temporal consistency in the majority of its video generations.

When we begin to explore micro-movements like handling that chain is a challenge for any model; the model begins to struggle.

Where Wan still stumbles is anywhere multiple moving elements interact. Crowds remains the weakest clip in the set (4/10), with all the same issues other models have. The same fragility appears in Physical Simulation (Hand in Water).

Here, the ripples reset constantly, instead of multiplying and/or looping naturally. The water and the hand both have decent motion, but they have no relationship to each other, they feel like two elements badly composited together.

When too many surfaces overlap, Wan loses track of which pixels belong to which object, and the shot collapses.

These shots prove that Wan can keep motion, lighting, and framing locked when the scene stays simple, but it can fall apart once multiple elements start to interact.

Visual Fidelity — 6.5/10

Visual fidelity is where Wan 2.5 sees serious gains from Wan 2.2. Average fidelity scores rise from 5.2 to 6.4, a gain that tracks with a gain that tracks with Wan’s broader improvements in frame-to-frame stability.

Faces start and end the shot with consistent detail, fabrics hold their weave, and color-grading never feels synthetic.

There’s none of the soft textures and smeared edges that plagued 2.2. It’s a jump in clarity that you can literally see.

In both shots above, facial geometry and structure are clean, with stable pores.

Likewise, the highlights on her forehead and cheekbones match her environment and stay that way, but when her expression changes, her wrinkles flatten and it gives her a, not-quite-uncanny, but definitely over-smoothed look.

We see the same effect in Commercial Hero Shots - Person, though not as pronounced. Why is that? In short, the difference comes down to complexity.

Woman Hailing a Cab stacks too many dynamic variables on top of each other, and since Wan (and most models) prioritizes continuity over realism, you end up sacrificing fine texture to keep the shot stable.

If you need any sort of change in emotion or significant movement (which you of course will if you are creating any sort of film) then that’s where Wan 2.5 will struggle with it’s output.

Motion Quality — 5.9/10

Ok, you’ve probably figured out the pattern by now. With almost a six, motion quality in Wan is better than average.

And you probably know by now that the quality of the motion in your shots depends on: describing a single character, in a single shot, using cinematic language.

But we found there was one other important detail that caused motion quality to spike in testing. Each high-scoring shot contains a single, unified movement.

Meaning, either the subject moves or the camera moves, but never both at once.

Subject and camera never compete. We’ve seen it again and again, but a clear, singular instruction, in this case a motion vector, allows Wan to put energy and focus into the other elements of your shot.

If you want it in tech-speak: It allows Wan’s temporal-solver to maintain coherence without having to re-map geometry mid-shot.

Look again at the over-the-shoulder convo, the camera is locked, and only the subject’s arm and head move. In both cases, motion feels planned, not improvised.

In the Aerial Orbit shot, the only movement is the camera’s steady rotation; the landscape stays fixed. And while the motion quality is above average, this shot reveals an unexpected weakness: footage that is too smooth.

The drone-style camera orbit almost works. The horizon lock stays true, parallax effect is even, a slight atmospheric haze even modulates with the movement of the camera.

But the motion quality here is so controlled that it actually goes too far. The absence of movement of any kind in a drone shot gives the whole thing a CG-simulation cleanness.

This same issue affects the landscape and animals as well; there’s no secondary movement, and those elements feel like they barely move at all. It feels like unnatural, perfect motion, with no realism.

It’s a simple formula: when your prompt gives Wan one clear task and only moves either the camera or the character, motion quality increases by almost one and a half points above average.

Whenever you need to prompt for movement in both the camera and subject, that is where you might run into some bigger issues when using Wan 2.5.

Style & Cinematic Realism — 5.7/10

Ok, by now, we’ve said a lot about Wan’s relationship with cinematic language.

It understands classic shot composition: consistent headroom, shot composition, and predictable blocking. But Wan’s sense of camera falters once multiple subjects or layers enter the frame.

Crowds collapse into visual noise due to too many priorities and no clear focal point. Misses like this happen because Wan is trying to balance competing cues, without a singular-framing anchor, whether it’s character or camera.

You can see this in the examples above, where one has a clear subject and the other is decently more complex.

Throughout our tests, we saw moments of human realism. But that doesn’t mean that Wan generates perfect human interactions every time.

Cinematic realism in Wan means that the model understands how a camera behaves, how light bounces across a surface, and how framing dictates the viewer’s focus.

We’ve seen plenty of shots that prove that when the scene is simple, one subject, one camera move, one source of light, Wan can produce images that feel genuinely filmed.

These shots have what so many other models can’t reproduce and that’s compositional logic. That means a clear foreground, a steady horizon, and rhythm.

But Crowds, Complex Direction, and most shots with natural elements all reveal the same underlying limitation.

Wan sometimes struggles to simulate physics that make shots believable. The results aren’t broken, but they aren’t seamless either.

Taken together, these tests show a model capable of rendering a coherent and photogenic world.

Do We Recommend Wan 2.5 for AI Video Artists?

If you are specifically looking for an open source AI Video model that you can run locally on your machine and customize it for specifics goals and projects then this might be an decent option for you.

If you aren’t worried about the tool being open source or needing to run locally on you rmachine, then we would probably recommend another AI Video model for your workflow.

You can definitely make some impressive videos and project with this tool, but there are better options avaialble. If this is all you have, it is still do able and could be worth utilizing!

How Does Wan 2.5 Fast Stack Up Against Other AI Video Tools?

Wan 2.5 is pretty popular for being an open source tool. Whenever we compare to to cloud based models, it doesn;t typically hold up as well. Below are our teams professional rankings based on significant amounts of testing and research.