Wan 2.6 First Day Thoughts: Better Than 2.5 But Not a Major Leap

Wan 2.6 dropped and I did what I always do: cleared my entire afternoon to obsessively test it. My wife has learned to accept this about me.

After a full day of generating videos, comparing outputs frame by frame, and specifically trying to break it with the prompts that made 2.5 struggle, I have thoughts. The short version: Wan 2.6 is genuinely better than 2.5. Also genuinely not the revolution some people online are claiming.

Let me tell you what I actually found.

Quick Answer: Wan 2.6 delivers meaningful improvements in motion coherence, hand rendering, and temporal consistency. Generation is maybe 10-15% faster. The improvements are real but incremental. If you were expecting AI video to suddenly "just work" without any of the weirdness... keep waiting.

The Motion Thing Is Actually Noticeable

Here's what used to drive me crazy with Wan 2.5. You'd generate a beautiful video of someone walking, and then for exactly one frame, their left leg would phase through the sidewalk. Or a car would be driving smoothly and just... hiccup... like it briefly entered another dimension.

Wan 2.6 does this less. I ran 50 of my standard test prompts through both versions with identical settings. The motion glitches that forced me to regenerate maybe 1 in 3 clips with 2.5? Now it's more like 1 in 5.

This matters more than it sounds. When you're generating AI video for actual projects, every regeneration costs time and compute. Reducing the "this is basically good but has one weird frame" rate is genuine progress.

What still happens:

• Very complex scenes with many moving elements still get confused sometimes
• Fast camera movements can still produce artifacts
• Physics-defying moments still occur, just less frequently

But the baseline for "normal" prompts improved measurably.

Hands: Still AI Hands, But Less Nightmare Fuel

I have a folder of screenshots I call "AI Hand Crimes." It's full of six-fingered abominations, hands that merge into flesh mittens, fingers that phase through keyboards, thumbs where thumbs should never be.

Wan 2.6 contributes to this folder less often.

I ran my hand-stress-test prompts: close-ups of typing, people gesturing while talking, detailed hand-object interactions. The results are... better? The finger count stays more stable across frames. The merging happens less frequently. Hands actually interact with objects more convincingly.

But I want to be clear: hands remain the weakest point of AI video. We haven't solved this problem. We've just... taken the edge off it. If your video prominently features hands doing detailed work, you're still rolling the dice.

My hot take: the improvement is significant enough that I've started being less paranoid about prompts involving hands. I'll actually generate "person typing on laptop" now instead of always framing shots to hide hands. That's progress.

The Temporal Consistency Improvements Are Subtle But Real

This one's harder to screenshot but matters a lot for the uncanny valley.

With 2.5, I'd sometimes notice things like:

• Earrings that changed style mid-video
• Clothing patterns that shifted impossibly
• Background elements that appeared and disappeared
• Skin tones that drifted frame to frame

These small inconsistencies scream "AI generated" even when the overall quality looks good. Your eye picks up on them even if you can't articulate what's wrong.

Wan 2.6 maintains persistent details better. I generated a 5-second clip of someone in a patterned shirt, and the pattern stayed the same pattern the whole time. Sounds basic. Was not guaranteed before.

Speed: Nice But Not Transformative

Generation times dropped about 10-15% on my setup. A clip that took 4 minutes now takes about 3.5.

Look, I'll take it. But this isn't the kind of speed improvement that changes workflows. It's more like "oh nice, slightly less waiting." Compound it over dozens of generations and it matters. For any individual clip, you probably won't notice.

What Stayed Exactly The Same

Some things I was hoping would improve but didn't:

VRAM requirements: If you couldn't run 2.5 comfortably, you can't run 2.6 comfortably. Same GPU hungriness, same memory footprint. The 12GB minimum / 16GB comfortable / 24GB ideal framework still applies.

Maximum useful length: You're still getting 2-6 second coherent clips depending on complexity. Longer videos still require segment stitching. If you were hoping for native 30-second generations, nope.

Prompt interpretation: Feels identical. My prompt library works the same way. Style keywords do the same things. The model's "personality" in how it interprets requests hasn't noticeably shifted.

The learning curve: If you struggled to get good results from 2.5, you'll struggle similarly with 2.6. The improvements are in execution, not in making it easier to use.

My Actual Test Results

Let me share specifics from comparing identical prompts.

Test 1: Person Walking Through Park

Prompt: "A person walking through a park, sunny day, camera follows from the side"

2.5 result: Generally smooth, but foot sliding (that thing where the feet move but don't quite match the ground) happened, and hands occasionally clipped through pockets.

2.6 result: Smoother walking cycle. Foot sliding still present but less severe. Hands maintained position relative to body more consistently.

Verdict: Clear improvement, not transformation.

Test 2: Close-Up Hand Typing

Prompt: "Close-up of hands typing on a keyboard, office setting"

2.5 result: Finger count varied between 4-6 per hand across frames. Some merging. Fingers occasionally passed through keyboard.

2.6 result: Finger count more stable (mostly 5, occasional variance). Less merging. Better keyboard interaction, though still not perfect.

Verdict: Meaningful improvement. Would actually use 2.6 output for background footage. Wouldn't have used 2.5.

Test 3: Busy City Street

Prompt: "Busy city street with cars, pedestrians, and storefronts, camera panning left to right"

2.5 result: Good overall, but cars occasionally position-jumped between frames. Some pedestrians popped in and out.

2.6 result: Smoother car movement. Better pedestrian persistence. Storefront signs remained more consistent.

Verdict: Incremental improvement. The complex scene still challenged it, just slightly less.

Test 4: High-Motion Action

Prompt: "Mountain biker racing down a trail, dynamic camera angles"

2.5 result: Impressive but with occasional frame-drop-feeling artifacts. Motion blur sometimes applied weirdly.

2.6 result: Smoother motion flow. More natural motion blur. Wheel spokes rendered better during rotation (this surprised me).

Verdict: This is where 2.6 shone brightest in my testing. High-motion content improved more than average.

Should You Upgrade?

Depends entirely on your situation.

Definitely Upgrade If:

• You generate AI video regularly and cumulative improvements matter
• Hand rendering issues have been causing regenerations
• Motion artifacts have been your main complaint
• You have the bandwidth to redownload model weights

Consider Waiting If:

• Wan 2.5 is already meeting your needs
• Storage is tight (new model = more disk space)
• You've heavily tuned workflows around 2.5 behavior
• You were hoping for capability jumps, not refinements

Stay on 2.5 If:

• Your workflows are working and you hate change
• You're disk-space constrained
• Your use case doesn't exercise the improved areas
• You're holding out for a bigger update

The Integration Story

Good news: if you're using ComfyUI, the upgrade is trivial.

Download the new weights. Point your workflow to the new model path. Done. Existing workflows work. Existing nodes work. I didn't have to change anything except the model reference.

If you're using hosted services like Apatero.com, they'll likely update server-side, and you'll just start getting better results without doing anything. Worth checking if they've deployed 2.6 yet.

What I'm Hoping For in 2.7 or 3.0

Since I'm giving honest impressions, let me be honest about what I still want:

• Native longer clip support (10+ seconds without stitching)
• Better audio integration
• Actual character consistency (not just "less drift")
• Lower VRAM requirements
• Faster generation (like, 3x faster, not 15% faster)

Wan 2.6 doesn't give us any of these. It polishes what we already had. That's valuable, but I'm still waiting for the generational leap.

Frequently Asked Questions

Is Wan 2.6 way better than 2.5?

Better yes, way better no. Think of it as Wan 2.5 with some edges polished off. Noticeable improvement if you look for it, not dramatic transformation.

Will it run on my RTX 3060?

With 12GB VRAM, technically yes but you'll be at minimum settings. Comfortable use wants 16GB. Full quality wants 24GB. Same as 2.5.

Do my existing workflows work?

Yes. Swap the model path and you're done. ComfyUI nodes, settings, everything transfers.

Is it worth downloading 10GB+ of new weights?

If you generate video regularly, yes. If you're casual about it, the improvements might not matter enough to justify the storage.

Did character consistency improve?

Marginally. The temporal consistency improvements help within a single clip. Cross-clip character consistency (same character in different videos) is still the hard problem that needs tools like Apatero.com or LoRA training to address.

What about text in videos?

Slightly improved, still a weakness. Don't trust it. Add text in post.

Is Wan 2.6 the best open video model now?

For general-purpose local generation, yes. Specialized models exist for specific use cases that might beat it in narrow domains, but for all-around capability, Wan 2.6 leads the open-source options.

The Bottom Line

Wan 2.6 is a solid incremental update. The motion coherence improvements alone justify upgrading if you generate video regularly. The hand improvements make previously-risky prompts more viable. The speed boost is nice.

It's not revolutionary. The fundamental limitations of AI video - short clips, occasional weirdness, no true consistency - remain. If you were waiting for the version that "just works," keep waiting.

But within the reality of where AI video is today, Wan 2.6 is the best open option for local generation. If you're in the ecosystem, upgrade. If you're considering entering, this is a good version to start with.

I'll keep testing over the coming days and update if I find anything surprising. For now: cautiously positive. Progress is progress, even when it's not revolution.