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There is no defensible single winner for “most realistic” AI video. Google DeepMind reports that Veo 3.1 led several human-rated MovieGenBench and VBench evaluations, while Runway reported Gen-4.5 at the top of the Artificial Analysis Text-to-Video leaderboard on November 30, 2025. Those results measure different things, with different prompts and methods.
For a practical choice, judge the exact mode you need—text-to-video or image-to-video—against matched prompts. Inspect the complete clip for motion, physical interactions, identity continuity, prompt adherence, and audio synchronization rather than choosing from a single impressive frame.
What “realistic” means in an AI-generated video
Realism is not one score. A clip can have convincing skin and lighting yet fail when a hand touches an object, a liquid pours, or a person speaks. Evaluate these dimensions separately:
- Visual fidelity: skin, hair, fabrics, materials, lighting, reflections, and fine scene detail.
- Motion and physics: believable acceleration, impacts, gravity, collisions, liquids, cloth, and cause-and-effect.
- Temporal consistency: objects, faces, body parts, text, and backgrounds remain stable from frame to frame.
- Prompt adherence and control: the camera, composition, subjects, actions, and style match the request.
- Input mode: text-to-video and image-to-video have different failure patterns; a model that excels at one may not lead in the other.
- Audio: dialogue, ambience, effects, and synchronization are part of realism when the output is intended to be finished video.
Benchmarks also differ in prompt sets, clip duration, resolution, sound settings, model version, and rating method. A leaderboard number is meaningful only with those conditions attached.
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What the latest published comparisons actually show
The strongest public evidence in the supplied evaluations points to two leaders, but not under one shared test.
| Model and source | Evaluation or result | What the figure means | Important qualification |
|---|---|---|---|
| Google DeepMind Veo 3.1 | MovieGenBench text-to-video; 1,003 prompts and videos | Google reports Veo 3.1 performed best for overall preference, text alignment, and visual quality. | Google’s page labels these results as last updated October 2025; they are Google-reported human ratings. |
| Google DeepMind Veo 3.1 | VBench image-to-video; 355 image-and-text pairs | Google reports overall preference for Veo outputs, including text alignment and visual quality. | The comparison could not include Sora 2 Pro because it did not support realistic human images in that test. |
| Google DeepMind Veo 3.1 | MovieGenBench text-to-video-with-audio; 527 prompts | Google reports results for video generation with sound. | This is a separate audio-enabled evaluation, not interchangeable with the silent text-to-video result. |
| Runway Gen-4.5 | Artificial Analysis Text-to-Video leaderboard | Runway reported a number-one position with 1,247 Elo as of November 30, 2025. | The announcement was dated December 1, 2025 and says the ranking came from blind human preference voting across thousands of pairwise comparisons. |
| Veo 3 | Same Artificial Analysis comparison reported by Runway | 1,226 Elo. | This is a dated leaderboard comparison, not the same protocol as Google’s MovieGenBench or VBench results. |
| Kling 2.5 | Same Artificial Analysis comparison reported by Runway | 1,225 Elo. | Do not treat the one-point difference from Veo 3 as a universal quality distinction. |
| Sora 2 Pro | Same Artificial Analysis comparison reported by Runway | 1,206 Elo. | The figure is tied to that dated comparison and does not establish a permanent ranking. |
These results cannot be merged into a single league table. Google’s page compares capabilities in named evaluations; Runway’s announcement reports a text-to-video Elo ranking at a particular date. They may use different prompts, durations, resolutions, model settings, and judging interfaces.
Why benchmark choice changes the answer
The CVPR 2025 Video-Bench paper explains that conventional metrics and embedding-based benchmarks do not always align with human judgments. LLM-based judging can also have limited understanding of video-quality dimensions and cross-modal consistency. That is why a model can rank highly on one test while another looks better for a specific production task.
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A search-result summary for Stanford HAI’s AI Index 2026 mentions a VBench-2.0 result in which Veo 3 led at 66.7% and Vidu Q1 reached 62.7%. The original report and benchmark definitions were not available in the supplied material, so those figures should not be used as a central buying claim without independently checking the report.
Model-by-model interpretation
Veo 3.1: strongest published evidence across several axes
Google DeepMind’s current Veo page presents Veo 3.1 and links to Gemini, Flow, and developer access. In the MovieGenBench text-to-video evaluation, participants viewed 1,003 prompts and corresponding videos; Google says Veo 3.1 was preferred overall and led for text alignment and visual quality. In a separate VBench image-to-video evaluation, participants viewed 355 image-and-text pairs and preferred Veo outputs overall, including for text alignment and visual quality.
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Veo 3.1 is also presented as supporting native audio. Google reports a 527-prompt text-to-video-with-audio evaluation, but audio quality must be judged separately from silent visual quality. Google’s own limitation statement is explicit: “While Veo continues to make incredible strides in video generation, creating videos with natural and consistent spoken audio, particularly for shorter speech segments, remains an area of active development.” Expect occasional problems with pronunciation, timing, or continuity in short dialogue.
Do not overextend the image-to-video result. Google notes that Sora 2 Pro could not be compared in that test because it did not support realistic human images for the evaluation. That is a limitation of the comparison setup, not proof that Veo wins every image-to-video category.
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Gen-4.5: the dated text-to-video leaderboard leader reported by Runway
Runway’s December 1, 2025 announcement says Gen-4.5 held the number-one position on Artificial Analysis Text-to-Video as of November 30, 2025, with 1,247 Elo. The same announcement lists Veo 3 at 1,226, Kling 2.5 at 1,225, and Sora 2 Pro at 1,206. Runway says the ranking used blind human preference voting across thousands of pairwise comparisons.
This is useful evidence for text-to-video preference at that date, not a permanent declaration that Gen-4.5 creates the most realistic video in every mode. Elo differences are also not a direct measurement of skin detail, physics, continuity, or audio. Gen-4.5 access was described as available across paid Runway plans in that announcement; plan availability and model versions can change, so verify the current product page before committing to a workflow.
Runway lists recurring realism failures: causal errors, objects that disappear or appear unexpectedly, and actions that succeed despite inadequate setup. In practice, inspect the setup and payoff of every action instead of judging only the final pose.
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Sora 2 Pro and the earlier Sora report
Sora 2 Pro appears as 1,206 Elo in Runway’s dated Artificial Analysis comparison. That number should be read only in that context. OpenAI’s “Video generation models as world simulators,” published February 15, 2024, remains useful background: it describes the largest Sora model generating up to one minute of high-fidelity video. The report does not provide model or implementation details and is not a current head-to-head ranking, so it should not be used to name today’s overall leader.
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Kling 2.5’s 1,225 Elo in the same Runway-reported snapshot places it close to Veo 3 in that particular text-to-video test. The evidence supplied here does not establish how it compares on native audio, image-to-video, long-shot continuity, or specific physics challenges. Treat the score as a dated preference result rather than a complete capability profile.
Which generator fits a particular job?
| Your priority | Best-supported starting point | Why | What to verify yourself |
|---|---|---|---|
| Broad text-to-video visual quality and prompt alignment | Veo 3.1 | Google reports the best overall preference, text alignment, and visual quality on 1,003 MovieGenBench prompts. | Whether your subject, camera movement, and clip length reproduce that advantage. |
| Text-to-video preference on a dated public leaderboard | Gen-4.5 | Runway reported 1,247 Elo and the number-one Artificial Analysis position as of November 30, 2025. | Current ranking, model version, and performance on your prompt set. |
| Image-to-video with a reference image | Veo 3.1 is a reasonable first test | Google reports preference on 355 VBench image-and-text pairs. | Identity preservation, camera motion, and results for your image type; the published comparison had modality limitations. |
| Native audio or spoken scenes | Test Veo 3.1 and Gen-4.5 side by side | Veo 3.1 is presented with native audio, while Gen-4.5 has a strong dated text-to-video result. | Dialogue consistency, lip synchronization, ambience, and effects; Google says short spoken audio remains under development. |
| Historical context rather than current selection | Sora report (2024) | It documents up-to-one-minute high-fidelity generation capabilities. | Current product access and benchmark performance, which the report does not establish. |
This table is a starting plan, not a guarantee. If your deliverable is image-to-video, do not select a tool solely because it topped a text-to-video leaderboard.
How to run a fair realism test
You can make a useful decision without treating a promotional demo as a controlled experiment. Keep the comparison matched and record failures as well as successes.
- Define the deliverable. Write down orientation, target duration, resolution, whether sound is required, and whether you will start from text or an image.
- Create a fixed prompt set. Include a portrait with subtle expression, a full-body walk, a camera move around a stationary object, a liquid or cloth interaction, a collision or handoff, and a short spoken line if audio matters.
- Use identical inputs. Keep wording, reference image, aspect ratio, duration, and quality settings the same. Record model version and generation date.
- Generate multiple samples. A single seed can be unusually good or bad. Keep the number of attempts consistent across tools and do not discard failures selectively.
- Score the complete clips. Use separate 1–5 ratings for visual detail, motion and physics, temporal consistency, prompt adherence, and audio synchronization. Watch at normal speed and frame-by-frame when an action fails.
- Record failure types. Note object disappearance, extra fingers, identity drift, impossible contact, camera jumps, incorrect text, pronunciation errors, and sound that starts or ends out of sync.
- Weight the dimensions. For a product demonstration, physics and continuity may matter more than cinematic detail. For a mood shot, visual fidelity and camera control may dominate.
- Recheck volatile claims. Model versions, access, and leaderboard positions change. Confirm current availability and methodology immediately before choosing a paid plan or publishing a comparison.
Do not average incompatible published scores with your own ratings. Use public evaluations as context, then make the decision from matched outputs for your project.
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Prompting for more believable motion
Specific prompts help expose whether a model understands an action rather than merely producing a plausible still. Describe the subject, starting state, physical cause, camera, and endpoint in that order.
- State the setup: “A glass is half full on a level wooden table; a hand enters from the right.”
- Describe cause and effect: “The hand lifts the glass, liquid tilts with inertia, and a small amount spills when the glass stops.”
- Constrain the camera: “One continuous waist-up shot, locked camera, natural 24 fps motion, no cut.”
- Protect continuity: “Keep the same person, clothing, lighting, and background throughout.”
- Specify speech separately: Give the exact short sentence, speaker, tone, and when the mouth begins and ends moving.
Avoid stacking many unrelated actions into a short clip. Complexity increases the chance of causal errors and identity drift, especially when the model must coordinate hands, objects, camera movement, and speech simultaneously.
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Objects appear, vanish, or change shape
This is one of the limitations Runway identifies. Reduce the number of simultaneously moving objects, keep the camera closer, and describe where each object starts and ends. Generate a clean establishing shot and an action shot separately if continuity remains unstable.
An action succeeds without the necessary setup
When a character catches, opens, lifts, or pours without believable contact, add intermediate states: hand reaches, fingers close, object moves, then the result occurs. A longer clip or two-shot edit can be more credible than forcing every beat into one generation.
Faces or bodies drift across frames
Use a reference image when the workflow supports it, reduce rapid camera movement, and avoid changing lighting or wardrobe mid-shot. Check the entire clip; a stable opening frame does not prove identity consistency.
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Speech sounds unnatural or loses synchronization
Keep dialogue short, place the spoken line in a quiet scene, and test several takes. Google specifically says natural and consistent spoken audio—particularly short speech segments—remains an active development area. If the generated voice is unreliable, create the visual first and handle dialogue in a separate audio workflow, while clearly labeling the result as edited rather than native-audio output.
A single frame looks excellent but the clip fails
Do not select a winner from thumbnails. Scrub through transitions, impacts, hand-object contact, and the final second. Realism is a temporal property; continuity and causality matter as much as detail in any one frame.
Access and version caveats
Google’s page currently presents Veo 3.1 through Gemini, Flow, and developer access. Runway’s December 1, 2025 announcement described Gen-4.5 as available across paid Runway plans. These are time-sensitive availability statements, not permanent guarantees. Check regional access, plan limits, output resolution, audio support, and current model names before designing a production pipeline.
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Benchmark positions are equally volatile. Attach the date and methodology whenever you quote a score: Gen-4.5’s 1,247 Elo was reported as of November 30, 2025; Google’s Veo comparison sections were labeled last updated October 2025. A later model update can change results without making the earlier evaluation incorrect.
Bottom line: choose by task, then verify with matched clips
Veo 3.1 has the broadest published evidence in this material: Google reports leading human preference for overall result, text alignment, and visual quality on MovieGenBench, plus a favorable image-to-video result on VBench and a separate audio evaluation. Gen-4.5 has the clearest dated text-to-video leaderboard claim: Runway reported number one with 1,247 Elo on Artificial Analysis as of November 30, 2025. Neither claim proves a universal, permanent winner.
Start with the model whose published evidence matches your input mode, then run the same prompts through the alternatives you can access. Pick the generator that preserves motion, causality, identity, and sound in the complete clips your audience will actually see—not the one with the most impressive isolated frame or the oldest leaderboard headline.
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