Prompt Adherence Benchmark: Seedance 2.0 vs Runway, Sora & Kling in Complex AI Video Generation

I tested prompt accuracy across 4 models, Seedance 2.0’s results shocked me.

Prompt quality is not the bottleneck anymore. Model compliance is.

For power users working in AI video, especially those using Runway, Sora, Kling, or ComfyUI-based pipelines, the core challenge isn’t generating something cinematic. It’s getting the model to follow complex, multi-constraint prompts with measurable reliability.

This deep dive documents a structured benchmark designed to test prompt adherence under controlled conditions, focusing on:

• Complex physics and fluid simulation tasks
• Character consistency and micro-detail retention
• Iteration load required to achieve spec-accurate results

The goal: quantify which model actually respects instructions when the prompt becomes non-trivial.

Why Prompt Adherence Is the Real Bottleneck in AI Video

Most generative video systems operate on diffusion-based backbones with temporal consistency layers. Whether it’s a transformer-diffusion hybrid (like Sora) or a latent diffusion architecture with motion modules (Runway, Kling), they all face the same structural tension:

Creativity vs Constraint Enforcement.

Prompt adherence depends on several technical variables:

• Latent Consistency across frames
• Scheduler stability (Euler a vs DPM++ variants)
• Cross-attention strength and token weighting
• Motion prior bias
• Seed determinism and parity control

Most marketing demos showcase aesthetic quality. Very few showcase strict compliance under layered instructions. So we built a scientific testing framework.

Scientific Test Framework

To eliminate subjective bias, each model was evaluated under identical constraints:

• Same structured prompt hierarchy
• No post-editing
• No manual inpainting
• Max 3 iterations per test
• 5-second clips
• 24fps baseline

Each test was scored on:

• Instructional accuracy (0–10)
• Temporal coherence (0–10)
• Detail persistence (0–10)
• Iteration cost (number of retries needed)

Test 1: Complex Physics & Fluid Simulation

Prompt Structure (Abbreviated):

> A transparent glass cube floating in zero gravity. Inside, blue liquid forms a rotating vortex. Small metallic spheres orbit within the liquid. Light refracts accurately through the glass. Slow cinematic camera dolly forward.

Why this test matters:

• Requires volumetric behavior
• Demands fluid coherence across frames
• Tests refraction realism
• Multi-object motion hierarchy

Results Overview

Seedance 2.0

• Fluid rotation remained stable
• Metallic spheres respected orbital path
• Refraction distortion was directionally consistent
• 2 iterations to achieve spec match

Runway

• Fluid behavior devolved into texture warping
• Orbits collapsed into chaotic drift
• Refraction inconsistent across frames
• Required 3+ iterations, never fully accurate

Sora

• Strong volumetric simulation
• Excellent camera motion
• Minor sphere drift after 3 seconds
• 1–2 iterations

Kling

• Strong aesthetic output
• Fluid became semi-rigid mid-sequence
• Orbits partially ignored
• 3 iterations minimum

Key Insight

Models with stronger internal motion priors tend to override instruction specificity when physics complexity increases.

Seedance 2.0 demonstrated tighter cross-attention enforcement, suggesting stronger token-to-motion binding in latent space.

Test 2: Character Consistency & Detail Retention

Prompt structure tested persistent identity under motion stress:

A red-haired female astronaut with a small scar above her left eyebrow. White EVA suit with blue mission patch labeled “ARES-7”. Helmet visor reflective gold. She turns slowly to camera and smiles subtly. Mars landscape background. Wind lightly moves dust.

Scoring emphasis:

• Scar persistence
• Patch legibility
• Facial morphology stability
• Helmet reflectivity consistency

Results

Seedance 2.0

• Scar remained visible in 90% of frames
• Patch text legible in mid-shot
• Facial structure preserved during turn
• Minor visor shimmer artifact

Runway

• Scar intermittently vanished
• Patch text degraded into noise
• Subtle face reshaping mid-turn

Sora

• Strong identity retention
• Patch text readable but morphing slightly
• Excellent dust simulation

Kling

• High aesthetic realism
• Scar disappeared after camera motion
• Patch replaced with abstract symbol in iteration 2

Why This Happens

Character drift is often caused by:

• Weak latent anchoring
• Insufficient temporal conditioning
• Cross-frame attention dilution
• Overactive motion smoothing

Seedance 2.0 appears to apply stronger identity locking between frames, reducing morphological drift.

Test 3: Iteration Requirements

This test measured production friction.

Power users care about:

• How many retries before usable output?
• Does seed locking produce stable variations?
• Can micro-adjustments be predictably applied?

We tested:

• Seed reuse
• Minor prompt modification
• Camera path adjustment

Iteration Efficiency

Seedance 2.0

• High seed parity
• Small prompt edits yielded proportional changes
• Predictable refinement behavior

Runway

• Seed reuse often diverged significantly
• Minor wording changes caused large structural shifts

Sora

• Strong determinism
• Predictable behavior under small deltas

Kling

• Moderate stability
• Occasionally overreacted to minor constraint edits

Iteration Load Summary

Average iterations to spec match:

• Seedance 2.0: ~1.8
• Sora: ~2.0
• Kling: ~2.8
• Runway: 3+ (often incomplete)

For production pipelines, iteration load equals cost.

Technical Breakdown: Why Some Models Fail

1. Latent Consistency

Models with stronger frame-to-frame conditioning reduce entropy accumulation. Weak systems treat each frame as semi-independent diffusion passes, increasing drift probability.

2. Scheduler Behavior

Schedulers like Euler a can introduce higher variance. DPM++ or hybrid schedulers often produce more stable constraint satisfaction, particularly for structured geometry.

3. Cross-Attention Weighting

When prompt tokens compete (“vortex,” “metal spheres,” “refraction,” “dolly forward”), weaker attention hierarchies dilute importance. Stronger models allocate more balanced token activation.

4. Motion Priors vs Instructional Control

Some engines favor cinematic realism over literal compliance. That’s visually pleasing—but technically inaccurate.

Seedance 2.0 and Sora leaned toward instruction-first generation.

Runway and Kling leaned toward aesthetic priors.

Final Benchmark Ranking (Prompt Adherence Focus)

1. Seedance 2.0 – Best overall constraint enforcement
2. Sora – Extremely strong, minor drift
3. Kling – High visual quality, weaker micro-detail control
4. Runway – Creative but least precise under layered constraints

Optimization Strategies for Power Users

Regardless of platform, you can improve adherence:

1. Token Hierarchy Structuring

Use explicit ordering:

• Primary subject
• Physical rules
• Motion constraints
• Camera movement
• Lighting

2. Constraint Reinforcement

Repeat critical constraints using semantic variation:

> “consistent orbital motion, stable circular path, no deviation”

3. Seed Locking for Micro-Refinement

When supported, maintain seed parity during incremental edits.

4. Reduce Instructional Entropy

If a model struggles, split generation into staged passes:

• Generate physics base
• Then refine character overlay
• Then enhance lighting

5. Avoid Competing Motion Directives

Models degrade when given multiple simultaneous high-complexity motion systems.

The Real Takeaway

The difference between hobbyist output and production-grade generative video is not resolution.

It’s compliance. If you’re building client-facing pipelines, testing simulation-heavy concepts, or engineering reusable prompt frameworks, prompt adherence is the metric that matters.

Seedance 2.0 surprised me because it behaved less like a creative improviser, and more like a disciplined rendering engine.

For prompt engineers, that difference changes everything.

Frequently Asked Questions

Q: What is prompt adherence in AI video generation?

A: Prompt adherence refers to how accurately a video generation model follows detailed, multi-constraint instructions across objects, physics behavior, character traits, and camera motion without drifting or ignoring elements.

Q: Why do AI video models struggle with complex physics prompts?

A: Complex physics prompts stress latent consistency and motion conditioning systems. Weak cross-attention weighting and strong motion priors can override detailed constraints, leading to unstable fluid behavior or object drift.

Q: How can I reduce character drift in AI-generated video?

A: Use explicit identity anchors (distinct features, clothing labels), reinforce critical traits semantically, maintain seed parity when possible, and reduce simultaneous competing motion directives.

Q: Is iteration count a meaningful benchmark metric?

A: Yes. Iteration load directly impacts production cost and workflow efficiency. A model that achieves spec-accurate output in fewer passes is significantly more viable for professional pipelines.