Head to head: HeyGen Video Image to Video vs MiniMax (Hailuo AI) Video 01
HeyGen Video Image to Video vs MiniMax (Hailuo AI) Video 01
By Ryan Merket · Published · Updated
A head-to-head across balloon launches, stormy camera moves, water-burst physics and ramen-bowl orbits tests how well each image-to-video model handles both action and continuity. The comparison focuses on what the clips actually show—not just how polished they look.
HeyGen’s clearest advantage in this sample is making requested motion legible. It more often showed the balloon being handled and released, the camera changing viewpoint, and an intact water balloon becoming a spreading sheet of water and droplets. Hailuo’s clips could look polished, but the balloon burst often read as spray around a mostly intact balloon, while some requested camera moves became slight reframings rather than convincing arcs. That pattern wasn’t universal. Hailuo won one subject-action comparison, with a clearer crouched setup and balloon lift; the models tied on one ramen-orbit task. HeyGen won the other ten task comparisons, but the saved aggregate scores—28.5 for HeyGen and 25.3 for Hailuo—do not change the saved overall result: a tie, with the editorial strength **Too close to call (not statistical)**. **Final call: a tie on this saved task sample.** HeyGen was the more consistent choice for readable action and specified motion here, while Hailuo had its moments; this assessment applies only to the saved task sample, not to general model capability.
Subject action & gesture
A single continuous 7-second shot of an alpine rescue meteorologist on a snowy ridge carefully deploying a compact orange weather balloon in rising wind: he crouches, untwists the tether spool, clips the line to the balloon neck with gloved fingers, then releases it in one clean motion while leaning into the gusts, his body mechanics natural and fluid and the action clearly readable; the camera performs a smooth low-to-high gliding gimbal move starting near his boots crunching on ice and lifting to a medium side view as the balloon pulls upward, with pale dawn light reflecting off blowing snow, a faint sun halo through thin cloud, and a mood of focused calm under harsh mountain weather, 16:9
OpenAI: GPT-6 Luna: Model A more clearly shows the crouched setup, tether handling, and balloon lifting into the dawn, with framing that progresses from boots to a medium side view. Model B has an attractive mountain setting, but the balloon and release action are less readable, and it does not convey the requested low-to-high camera move as well. (Second judge pass, order swapped — scores are the average of both: Model B maintains a more consistent subject and setting, with the orange balloon visibly rising from t | Anthropic: Claude Fable 5.1: Model A follows the choreography closely: the s…
Temporal consistency
A single continuous 8-second shot of a storm chaser named Imani Voss standing on the metal catwalk of a rural Doppler radar station as low thunderheads boil behind her; she is a tall woman with a copper-brown complexion, a shaved line through her left eyebrow, and a narrow silver nose ring, wearing the same mustard-yellow waterproof parka with a torn cuff, charcoal field pants, teal neck gaiter, and white bicycle helmet marked with a faded black sticker, and her appearance, clothing, and identity must remain perfectly consistent from first frame to last with no morphing, duplication, or flicker as she braces one hand on the railing and turns to glance over her shoulder; the camera makes a slow fluid gliding gimbal arc from her front-left to her front-right at chest height, wind pushing rain sideways across the frame, lit by cold blue storm light with brief distant lightning flicker, mood tense and watchful, 16:9
OpenAI: GPT-6 Luna: Model A better conveys the requested turn and gliding change of viewpoint while keeping the storm-catwalk setting and outfit recognizable. Model B is visually striking and highly consistent, but its nearly unchanged framing and pose miss the requested camera arc and glance. (Second judge pass, order swapped — scores are the average of both: Model A better conveys the changing viewpoint, braced stance, over-shoulder turn, torn parka, and helmet sticker, while Model B keeps the character’s appeara | Anthropic: Claude Fable 5.1: Model A delivers the requested gliding camera a…
Fluid & particle dynamics
Cinematic slow-motion of a water balloon bursting, the sheet of water expanding and droplets scattering through the air in convincing detail against a dark background, hard side light, 16:9.
OpenAI: GPT-6 Luna: Model A more clearly progresses from an intact water balloon into an expanding translucent sheet and scattered droplets, with striking side lighting against black. Model B is visually polished and consistent, but the balloon remains largely intact across the sampled frames, so the burst is less convincing. (Second judge pass, order swapped — scores are the average of both: Model A clearly shows the balloon rupturing into an expanding, detailed water sheet, though its tight framing and glassy-loo | Anthropic: Claude Fable 5.1: Model A begins with a convincing balloon but it…
Camera motion control
A single smooth orbit (roughly 180 degrees) around a steaming bowl of ramen on a wooden table, the camera circling at a constant speed and height in one continuous move, warm restaurant light, 16:9.
OpenAI: GPT-6 Luna: Model A shows a more coherent change in viewpoint across the bowl and background, with warm restaurant lighting and consistent food details, though the orbit appears much smaller than the requested 180 degrees. Model B is visually appealing and steamy, but its viewpoint changes very little and the framing shifts more like a slight push-in than a continuous orbit. (Second judge pass, order swapped — scores are the average of both: Model B better conveys a continuous camera move with a more appare | Anthropic: Claude Fable 5.1: Model A shows a discernible orbital move (the e…
Matchup powered by OpenRouter.