We gave LTX 2.5 and Wan 2.2 the same ten starting pictures, the same prompts and the same seed, on one DGX Spark. LTX followed the prompt more often and rendered about four times faster. Wan made the most physical motion in the set, a wet dog's shake, and also invented a coffee carafe nobody asked for. It's one seed per prompt, so treat the one-point calls as ties.
What we tested
Ten prompts from the lab's fixed suite, each built to stress one thing:
| Prompt | What it stresses |
|---|---|
| P01 talking host | face, idle motion |
| P02 coffee mug | slow push-in, steam, nothing else moving |
| P03 lighthouse | a drone orbit |
| P04 kickflip | fast limbs |
| P05 hands kneading dough | fingers |
| P06 neon sign | text that must stay legible |
| P07 cartoon fox | a stylized look held over time |
| P08 robot | two beats in order: tilt, then wave |
| P09 rainy crosswalk | many moving people |
| P10 wet dog | fur and water |
For each prompt we made one still with Z-Image Turbo, so both models start from the exact same pixels. Then we gave that still and the motion prompt to LTX 2.5 22B distilled and to Wan 2.2 I2V A14B with the 4-step LoRA. Both rendered 1280x704 clips of about five seconds.
The lab's harness saved the exact ComfyUI graph, the seed, the wall clock and free memory for every run. Each clip was then scored 1-5 from a 12-frame sheet on five criteria: prompt adherence, motion, consistency, artifacts and overall usable for publication. LTX was scored by the lab's test reviewer and Wan by this post's producer, on the same rubric, because the reviewer was paused when the Wan clips landed. Both are Claude models reading frames, not people watching motion.
The plan had a second seed on five prompts for both models. Those runs were dropped from the day's queue to make room for other jobs, so every call here is one draw.
What happened
| Prompt | LTX overall | Wan overall | Call |
|---|---|---|---|
| P01 talking host | 4 | 4 | Tie |
| P02 coffee mug | 4 | 2 | LTX |
| P03 lighthouse | 4 | 4 | Tie |
| P04 kickflip | 2 | 3 | Wan by 1 |
| P05 hands | 4 | 4 | Tie |
| P06 neon sign | 4 | 3 | LTX by 1 |
| P07 cartoon fox | 5 | 4 | LTX by 1 |
| P08 robot | 4 | 3 | LTX by 1 |
| P09 crosswalk | 4 | 4 | Tie |
| P10 wet dog | 4 | 4 | Tie |
LTX scored higher on four prompts, Wan on one, and five tied. Every sub-score and the reviewer's note for each clip are in the scorecard player on this page.
The one clear gap is the coffee mug. The prompt says push in slowly and nothing else moves. LTX did that. Wan made the best steam in the set, skipped the push-in, and at 1.8 s a glass carafe slides in and pours into the mug.
LTX was better at doing what it was told. The robot tilts its head and then waves, in that order; Wan waves and barely tilts. The neon sign dips twice and then holds; Wan's pulses four times and never settles. The fox waves, winks and swishes its tail.
Wan was better at motion. The wet dog tied overall, but Wan's shake has weight: the head whips, the ears fly and the coat stays wet, where the LTX coat dries out in about a second. On the crosswalk Wan held the static camera the prompt asked for; LTX dollied back.
Both broke on the kickflip. Both riders float far too long. LTX also put a swoosh-style logo on the hoodie, which we blurred in the clips on this page.
Speed and memory
| LTX 2.5 (tests 021-030) | Wan 2.2 (tests 046-055) | |
|---|---|---|
| Wall time per clip | 78.1-87.1 s, median 81.2 s | 315.3-366.3 s, median 334.7 s |
| Seconds of compute per second of video | median 16.1 | median 66.1 |
| Lowest free memory sampled | 13 GB | 23 GB |
| Output | 24 fps, with an ambient audio track | 16 fps, no audio |
Wan took about 4.1 times as long per second of video. Wall time runs from submitting the graph to ComfyUI reporting done, including the VAE decode and the mp4 encode. A separate Wan model-load cost is not measured here: the first clip was 31.6 s above the median, but the second was close behind it.
The two families ran eleven hours apart, and the box changed in between. It froze twice, rebooted, moved to a new kernel and NVIDIA driver, and put both ComfyUI servers inside a shared 85 GB memory cap. The day before, on the old kernel, the same Wan setup took 336 s per clip, within 2 s of today's median, so the kernel change doesn't explain the gap. The memory minimums were taken under different background load, so they are not a like-for-like comparison, and per-process GPU memory was not captured.
What it means for you
On a Spark, make LTX 2.5 your default for a five-second shot from a still. It's about four times faster, and on this seed it followed the prompt more often.
When the shot is about motion itself, like fur, water, steam or a body shake, render a Wan take too and compare. Watch it for invented objects.
Wan writes 16 fps and LTX writes 24, so conform or interpolate Wan before you cut the two together. And render two seeds before you trust a call like the ones above.
How to do it
One model family at a time. LTX runs on one ComfyUI instance and Wan on a second, and the harness unloads the other instance before each batch. On a GB10 the CPU and GPU share one memory pool, and with both families loaded there would be little left for anything else (we did not try it). The box froze twice on the day of this test, both times with an LTX job on the GPU and a headless browser playing video in a page. That cause is likely, not proven.
Wan 2.2 I2V is two models. A high-noise and a low-noise 14B expert, 14.3 GB each in fp8, take turns during sampling. The 4-step LoRA is what makes it usable here: the reference setup is 40 steps with guidance 3.5, and we did not run that. This is Wan 2.2 at its fast 4-step setting against LTX 2.5's distilled model at its two-stage setting. LTX 2.5 also ships a full dev model, which we did not test.
Negative prompts do nothing at CFG 1. Both graphs run at CFG 1.0, where the negative branch has no effect.
Mind the frame rates. The side-by-sides and the edits conform Wan to 24 fps by duplicating frames, which is honest but a little steppy. The players show both clips as rendered.
Settings
LTX 2.5 22B distilled. ltx-2.5-22b-distilled-transformer-comfy-int8-convrot with the int8 Gemma 4 12B text encoder, the LTX video and audio VAEs and the x2 latent upscaler. Stage one at 640x352 on the 8-step distilled schedule, then stage two at 1280x704 with 3 refinement steps. euler_ancestral, CFG 1.0, the still in at strength 1.0, 121 frames at 24 fps, seed 42.
Wan 2.2 I2V A14B, 4-step. wan2.2_i2v_high_noise_14B_fp8_scaled and wan2.2_i2v_low_noise_14B_fp8_scaled, umt5_xxl_fp8_e4m3fn_scaled, wan_2.1_vae, and the lightx2v 4-step LoRAs v1 (high and low) at strength 1.0, shift 5. Four steps in total, euler, simple, CFG 1.0: steps 0-2 on the high-noise model, 2-4 on the low-noise model. 81 frames at 16 fps, seed 42.
Stills. Z-Image Turbo, 1344x768, 8 steps, seed 42. Both video graphs resize the still to 1280x704.
The linked workflow is the LTX lighthouse graph (test 023), built from ComfyUI 0.43 core nodes.
Sources: LTX-2.5 model card, LTX-2.x Community License, Wan2.2-I2V-A14B model card and license, Wan 2.2 reference config, lightx2v Wan2.2-Lightning, Comfy-Org Wan 2.2 repackage.
About the videos
The short film shows the lighthouse from both models back to back. Then it cuts the higher-scoring clip for seven more prompts; on a tie it takes the better motion or framing. The talking host and the neon sign are left out.
The instructional is hosted by three AI hosts with AI voices (ElevenLabs): Daisy Lane, Rafael Duarte and Priya Vance. Their on-camera takes are LTX 2.5 image+audio renders from each host's studio still. Lip sync is loose at pauses (the mouth closed at 4 of 23 sampled pauses), so the edit cuts to b-roll over pauses longer than half a second. Music is ElevenLabs Music, AI-generated.