Text-to-Video AI Models Compared: Quality, Speed & Price Breakdown (Veo, Kling, Seedance & more)

At Animax, we’re ushering in a new era of video creation — one where AI does the heavy lifting, and creators, marketers, and businesses can bring ideas to life faster than ever before. Whether you’re building product explainers, crafting YouTube or TikTok shorts, or launching video ad campaigns, Animax is your all-in-one platform for creating compelling, professional-grade videos with minimal effort.

Our platform integrates a powerful suite of AI tools: text-to-video generation, AI voiceovers, auto-generated music, and dynamic, editable elements like animated charts, counters, transitions, and kinetic text. The goal is simple: make high-quality video production accessible to everyone — no complex timelines or editing skills required.

As part of our commitment to innovation and transparency, we’ve put five of the most advanced text-to-video models to the test: Seedance, Kling, WAN, Hunyuan, and Veo.

To ensure a fair and consistent benchmark, we used the same prompt across all models to generate a 5-second video at 720p resolution:

“A modern athletic woman in her late 20s, wearing sleek gymwear (black leggings, sports top, and running shoes), jogs through a green park in the early morning light. She moves at a steady pace. Sunlight filters through the trees, and the atmosphere feels fresh, calm, and energizing.”

I deliberately chose this prompt because it mirrors the kind of content many Animax users are likely to generate — especially within the increasingly popular healthy lifestyle, wellness, and fitness verticals. It reflects a real-world use case for creators making short-form content, ads, or motivational videos.

On the surface, the prompt might seem relatively simple, but it actually introduces several challenges for AI models:

• It requires motion — the character must be jogging with natural body dynamics.
• It involves environmental awareness — the model must render trees, lighting, and atmosphere to feel authentic.
• It includes subtle emotional tone — the feeling of a fresh, calm morning needs to come through visually.
• And importantly, it’s not just a static object or face — it pushes the model to compose a full scene with background, character, and movement, all within a tight 5-second window.

This makes it an excellent stress test for gauging how well each model handles action, composition, realism, and storytelling potential — the core ingredients of compelling video content.

Hunyuan by Tencent: Realism in Details, But Struggles With Motion

Hunyuan is an advanced multimodal model developed by Tencent, one of China’s tech giants. While Hunyuan isn’t as widely known in the West as Veo or Kling, it’s a powerful platform designed for a range of AI capabilities — including text-to-image, code generation, and more recently, text-to-video.

Tencent has positioned Hunyuan as part of its broader vision for intelligent content generation across media, entertainment, and productivity tools. The text-to-video capability is one of its more recent additions, aimed at creators, brands, and platform partners within China’s enormous content ecosystem.

The Result: Strong Appearance, Weak Motion

In our 5-second prompt test, Hunyuan delivered a visually solid result, though with some clear limitations. Here’s what stood out:

Strengths

• The woman’s outfit, physique, and silhouette were impressively lifelike — especially her head and hair movement, which felt organic and believable.
• Facial features and body posture remained consistent across frames, avoiding the usual morphing artifacts some models struggle with.
• The early morning park atmosphere was well-conveyed, with decent lighting and a believable environment on first glance.

Weaknesses

• The jogging motion felt unnaturally slow, almost like slow motion — breaking the immersion.
• There were noticeable glitches in body mechanics and transitions between frames, especially around arm and leg movement.
• On closer inspection, the nature and background details began to feel synthetic, with some repetitive textures and lack of depth in the scene.

Generation Time & Cost

• Time to generate: 79 seconds
• Token cost: 400 tokens

This places Hunyuan in the moderate range both in terms of generation speed and cost, compared to other models tested.

Seedance by ByteDance: Impressive Speed with a Pro Option That Delivers

Seedance is a text-to-video generation model developed by ByteDance, the tech powerhouse behind TikTok, one of the world’s most influential platforms for short-form video. It’s no surprise that ByteDance is investing heavily in AI-driven video creation and Seedance is one of its flagship attempts to bring high-quality, prompt-driven video generation to both creators and developers.

Currently, Seedance offers multiple tiers, including a Lite and Pro version. We tested both to get a full sense of the range this model offers – from fast, budget-friendly output to more cinematic, production-level results.

Seedance Lite: Fast and Cheap, But Visibly Limited

The Lite version of Seedance is clearly optimized for cost-efficiency and accessibility. While it’s not the fastest model in the test, it still delivered the video in under a minute, making it suitable for quick iteration and budget-conscious use cases.

Strengths

• The jogger’s appearance was decent — proportions, outfit, and movement were all presentable.
• No obvious glitches or distortions in body or facial animations.
• The model is affordable enough for high-volume testing or early-stage creative exploration.

Weaknesses

• The background was completely static — essentially a still image, which clashed awkwardly with the moving figure.
• The motion felt slow and unnatural, similar to slow motion, which broke the illusion of realistic jogging.
• On closer inspection, the environment resembled computer-generated graphics rather than photorealistic nature.

Generation Time & Cost

• Generation time: 55 seconds
• Cost: 160 tokens

In short: while fast and cheap, Seedance Lite lacks the cinematic depth and realism required for polished content. It’s best used for early drafts, concept testing, or internal use where production quality isn’t a priority.

Seedance Pro: Dynamic, Lifelike, and a Major Step Up

The Pro version of Seedance showed a clear leap in quality — and arguably outperformed some of the more expensive models in this comparison.

Strengths

• The background was dynamic and realistic, giving the video a natural sense of depth and movement that the Lite version lacked.
• Character motion was smooth and much more lifelike, closely resembling a natural jogging pace.
• The park environment — lighting, trees, and atmosphere — looked convincingly real, with none of the synthetic feel found in Hunyuan or Lite.
• The model also handled hair and outfit movement well, with subtle wind and physics simulation.

Minor Drawback

• The jogger’s facial expression may have been a touch too cheerful — unless she genuinely loves her morning runs, it felt slightly exaggerated.

Generation Time & Cost

• Generation time: 50.22 seconds (fastest among all tested)
• Cost: 300 tokens

That makes Seedance Pro 13x cheaper than Veo2, while still delivering a high-quality, usable result — a very attractive trade-off for creators.

WAN: A Promising Model Undermined by Critical Motion Flaws

WAN is a lesser-known but rapidly evolving text-to-video model developed by a Chinese AI lab. While it doesn’t carry the brand power of Veo or ByteDance’s Seedance, WAN has been gaining attention for its fast generation times and decent visual fidelity — particularly in static or semi-dynamic prompts.

Designed primarily for creative applications and visual storytelling, WAN shows potential in short-form video generation. However, in our test, it ran into major issues that severely impacted usability.

The Result: One Glitch Too Many

On first impression, the output from WAN looked promising. The jogger’s outfit, body shape, and environment were reasonably realistic, with the park setting rendered with decent detail and soft early-morning lighting.

But then… things went off-track — literally.

Critical Issue

• The jogger appeared to be running backwards, in a jittery, reversed motion that completely broke the scene. This wasn’t a subtle animation hiccup — it was a major motion error that rendered the clip unusable for any serious application.

Other Observations

• The background, though static, was visually coherent and blended well with the character.
• The jogger’s face showed visible artifacts and an unnatural AI-generated expression, which felt off-putting and clearly synthetic.
• No major body glitches or outfit distortions, but the reversed motion overshadowed these positives.

Generation Time & Cost

• Generation time: 56.25 seconds
• Cost: 300 tokens

While WAN performs reasonably well in static elements, the core action in our test – jogging – was fundamentally broken. A runner moving in reverse is not just a visual oddity; it undermines the entire prompt.

This may indicate that WAN is better suited for scenes with less movement or simpler interactions, where character motion is minimal or not the focal point. In our case, however, it was not fit for purpose — the output failed to meet even baseline expectations for realism and usability.

Kling by Kuaishou: High-End Realism with a Price Tag to Match

Kling is a powerful text-to-video model developed by Kuaishou, one of China’s largest video-sharing platforms and a key rival to TikTok (ByteDance). Known for its focus on AI-powered visual effects, real-time filters, and deep generative media, Kuaishou has positioned Kling as its flagship solution for next-generation content creation — especially short-form, creator-first video.

Kling has undergone rapid development over the past year, with frequent version upgrades focused on realism, camera control, and motion accuracy. For this comparison, we tested two versions: Kling 1.6 and the newer, more advanced Kling 2.1.

Kling 1.6: Good Visuals, Slow Delivery, and a Motion Caveat

Kling 1.6 showed a generally solid result, especially in terms of visual fidelity:

Strengths

• The background and environment were rendered well, with good lighting and texture.
• The jogger’s outfit and body structure looked natural and proportionate.
• No major glitches — the video is clean and commercially usable in less dynamic or demanding contexts.
• Moderate cost at 250 tokens makes it attractive for mid-tier content.

Drawbacks

• The most noticeable issue: the jogging movement appeared to be in slow motion, which gives the scene an odd, almost dreamlike quality.
• While not a dealbreaker in every case, this could limit the use of the video for action-oriented or realistic storytelling.
• Long generation time – 209 seconds – puts it among the slowest models tested, nearly 4x longer than some others.

Generation Time & Cost

• Generation time: 209 seconds
• Cost: 250 tokens

Kling 2.1: Cinematic, Natural, and Among the Most Impressive

Kling 2.1, on the other hand, took a major leap forward. The result was arguably the most polished and lifelike output of all the models tested:

Strengths

• The body movement was smooth, natural, and perfectly matched the jogging pace — no slow-mo effect here.
• Camera movement added dynamic realism, with subtle pans and framing that felt professionally directed.
• The background environment was vibrant and convincing, with realistic lighting and depth.
• Facial expressions were expressive and believable, showing moments of the jogger looking ahead, down, or simply focused — a rare strength in most AI-generated faces.

Drawbacks

• In certain frames, facial artifacts were visible — not overly distracting, but noticeable on close inspection. This is likely due to the head motion and facial tracking under dynamic conditions.
• The generation time was high at 221 seconds, and the cost was substantial — 1300 tokens — making it one the most expensive model in the entire comparison.

Generation Time & Cost

• Generation time: 221 seconds
• Cost: 1300 tokens

Still, the elevated price is somewhat justified by the high quality and realism Kling 2.1 achieves. It’s a strong option when production value matters more than cost or speed.

Veo by Google DeepMind: The Most Hyped Model, But Is It Worth the Cost?

Veo is the flagship text-to-video model developed by Google DeepMind, and without a doubt, it’s the most well-known and hyped model in the AI video generation space. Veo has been heavily promoted by Google as a cutting-edge solution for cinematic-quality AI video, boasting high resolution, temporal consistency, and an understanding of camera motion and artistic direction.

As of this writing, two versions of Veo exist:

• Veo 2 – The version used in this comparison
• Veo 3 – Recently released, with even more advanced capabilities (but dramatically higher cost)

The Price of Prestige

One thing that immediately stands out about Veo is its cost — and not in a good way.

• Veo 2 costs 2500 tokens to generate a 5-second video
• Veo 3 jumps even higher — a staggering 8000 tokens per 5 seconds

This makes Veo 10–15x more expensive than most other models in this comparison — and even twice the price of Kling 2.1, which already sits in the premium range.

Given that cost, we focused our evaluation on Veo 2, which is the more “affordable” version — though still clearly positioned as a premium offering.

The Result: Solid, Reliable, but Not Spectacular

Veo 2 delivered what you’d expect from a flagship product — polished, realistic visuals with high consistency and no major glitches:

Strengths

• The jogger’s body and outfit were convincingly rendered, with no issues in proportions, texture, or motion.
• The facial expressions were clean and artifact-free, a standout achievement compared to most other models.
• The background environment was well-rendered, with consistent lighting, scene depth, and color tone.

Drawbacks

• Despite the high price tag, the video lacked the cinematic dynamism and richness seen in Kling 2.1.
• The motion was smooth but felt a bit flat or restrained, as if playing it safe rather than embracing visual complexity.
• Given that Seedance Pro — at just 300 tokens — produced a video of comparable quality, the value proposition for Veo 2 is questionable.

Generation Time & Cost

• Generation time: 50.95 seconds
• Cost: 2500 tokens

Veo 2 is reliable and safe — a solid choice if your budget is generous and you want clean, consistent output without the risk of glitches or weird animations. However, for most Animax users, the value just isn’t there.

When Seedance Pro delivers nearly the same quality at 1/7th the cost, and Kling 2.1 offers more visual punch for half the tokens, it’s hard to recommend Veo 2 unless you’re after Google-grade brand assurance or operating in high-stakes content production.

Model Comparison Table

Final Verdict: Value Trumps Hype

We tested a wide range of text-to-video AI models today — from cutting-edge industry flagships to newer, lesser-known contenders. What we discovered is clear: the most expensive models aren’t always the best.

While some low-cost models were unfortunately unusable due to glitches or unnatural motion, others in the mid-tier range managed to outperform expectations, offering the best balance between quality, generation time, and affordability.

Top Picks:

Seedance Pro

This model emerged as the best value-for-money option. It delivered reliable, high-quality output, with a dynamic and natural result — all while being the cheapest of the high-performing models and also the fastest to generate.

Kling 2.1

Though 4x more expensive and slower to render, Kling 2.1 produced the most vivid, cinematic, and realistic output of all models tested. Minor imperfections were present, but overall, the result was impressive and professional-grade.

Underwhelming:

Veo 2, while clean and technically solid, failed to justify its high price tag. At twice the cost of Kling 2.1 and over 8x more expensive than Seedance Pro, the video lacked energy and visual richness. It’s a safe option — but in many ways, too safe.

Conclusion

In conclusion, our tests show that the best AI video model isn’t always the most expensive one. With options like Seedance Pro and Kling 2.1, creators can achieve high-quality, dynamic video output without overspending or waiting too long. That’s exactly the philosophy behind Animax.

If you’re ready to create your own videos, you can register now and start generating content using simple text prompts — no editing experience required. But Animax goes far beyond cinematic video generation: it’s a full-featured AI-powered video editor that understands natural language. You can add transitions, kinetic text, images, animations, voiceovers, music, captions, charts, zooms, and more — all through an intuitive interface.

Whether you’re making social content, product explainers, ads, or tutorials, Animax is built to turn your ideas into videos — fast.