AlibabaVideo model
Wan 2.6
In image to video, Wan 2.6 is in place 14 of 21, with text only in place 15. A 5 s clip costs about $0.75.
1,310Arena ratinghigher is better
1,327Arena ratinghigher is better
$0.75$0.15 per secondlower is better
15slongest single cliphigher is better
24out of 100same as last month
Summary
At $0.15 per second of video it is fairly expensive; a typical video model costs $0.12.
What is distinctive about Wan 2.6 is its three hosted paths. Depending on the variant, T2V and I2V generate up to 15 seconds at 720p or 1080p, while R2V uses reference images and videos, supports multiple characters, and generates 2 to 10 seconds.
Wan 2.2 A14B belongs to a different Wan branch with open weights and no native audio.
Wan 2.6 benchmarks
Image to video arena
How people rate generated videos in blind comparisons, Elo points · higher is better
20 of 21 models
as of September 22, 2026 · Source: LMArena
Text to video arena
How people rate generated videos in blind comparisons, Elo points · higher is better
20 of 23 models
as of September 22, 2026 · Source: LMArena
Show all 108 individual results from 17 sources
Page 1 of 9
Published benchmarks
LMArena Text to Video
Elo ratings summarize pairwise comparisons within this leaderboard. They are not percentages. The scale does not express absolute quality or linear gaps between models.
higher is betterAs of November 5, 2026Scale: 0 Elo - 1,516 Elo
Methodology and context
- Metric
- elo-rating
- Scale
- No upper bound is documented. The chart spans the recorded values and zero.
- Participants
- 23
- Sample
- 54,050
LMArena Image to Video
Elo ratings summarize pairwise comparisons within this leaderboard. They are not percentages. The scale does not express absolute quality or linear gaps between models.
higher is betterAs of November 5, 2026Scale: 0 Elo - 1,495 Elo
Methodology and context
- Metric
- elo-rating
- Scale
- No upper bound is documented. The chart spans the recorded values and zero.
- Participants
- 21
- Sample
- 121,082
Measurements without matching peer values
These measurements have no matching peer values under the same test conditions. Their original values and sources remain available here.
WorldReasonBench (Show measurement, test conditions, and source)
- Source value
- 34.7
- Score
- 34.7
- Metric
- AccQA (question-answer accuracy)
- Unit
- %
- Benchmark version
- arXiv:2605.10434v1, Appendix Table 37 (overall)
- Category
- video-world-reasoning
- Direction
- Higher is better
- Sample
- 80
- Lower confidence bound
- 28.8
- Upper confidence bound
- 40.5
- Source type
- independent-evaluator
- Evaluator
- WorldReasonBench
- Status
- active
- Retrieved at
- 2026-10-16
- Methodology
- Independent evaluation. WorldReasonBench shared 80-item evaluation cohort; exact Wan 2.6 row. Scores are reported on a 0-100 percentage scale with 95% bootstrap confidence intervals.
- Context
- This is a task-specific paper result, not a universal video-quality score. Do not compare its numeric value directly with other benchmark scales.
WorldReasonBench (Show measurement, test conditions, and source)
- Source value
- 32.4
- Score
- 32.4
- Metric
- Score_PR (process-reasoning score)
- Unit
- %
- Benchmark version
- arXiv:2605.10434v1, Appendix Table 37 (overall)
- Category
- video-world-reasoning
- Direction
- Higher is better
- Sample
- 80
- Lower confidence bound
- 26.4
- Upper confidence bound
- 38.7
- Source type
- independent-evaluator
- Evaluator
- WorldReasonBench
- Status
- active
- Retrieved at
- 2026-10-16
- Methodology
- Independent evaluation. WorldReasonBench shared 80-item evaluation cohort; exact Wan 2.6 row. Scores are reported on a 0-100 percentage scale with 95% bootstrap confidence intervals.
- Context
- This is a task-specific paper result, not a universal video-quality score. Do not compare its numeric value directly with other benchmark scales.
WorldReasonBench (Show measurement, test conditions, and source)
- Source value
- 50.3
- Score
- 50.3
- Metric
- S(v) (weighted human rating)
- Unit
- %
- Benchmark version
- arXiv:2605.10434v1, Appendix Table 37 (overall)
- Category
- video-world-reasoning
- Direction
- Higher is better
- Sample
- 80
- Lower confidence bound
- 43.3
- Upper confidence bound
- 57.4
- Source type
- independent-evaluator
- Evaluator
- WorldReasonBench
- Status
- active
- Retrieved at
- 2026-10-16
- Methodology
- Independent evaluation. WorldReasonBench shared 80-item evaluation cohort; exact Wan 2.6 row. Scores are reported on a 0-100 percentage scale with 95% bootstrap confidence intervals.
- Context
- This is a task-specific paper result, not a universal video-quality score. Do not compare its numeric value directly with other benchmark scales.
RISE-Video (Show measurement, test conditions, and source)
- Source value
- 70
- Score
- 70
- Metric
- Reasoning Alignment (RA)
- Unit
- %
- Benchmark version
- arXiv:2602.05986v1, Table 1
- Category
- text-image-to-video
- Direction
- Higher is better
- Source type
- independent-evaluator
- Evaluator
- RISE-Video
- Status
- active
- Retrieved at
- 2026-10-16
- Methodology
- Independent evaluation. RISE-Video Table 1, text-image-to-video evaluation; exact model label from the table. GPT-5 judges reasoning alignment, temporal consistency, and physical rationality; GPT-5-mini judges visual quality. The paper does not provide a per-model denominator for these metrics.
- Context
- This is a task-specific paper result, not a universal video-quality score. Do not compare its numeric value directly with other benchmark scales.
RISE-Video (Show measurement, test conditions, and source)
- Source value
- 88.8
- Score
- 88.8
- Metric
- Temporal Consistency (TC)
- Unit
- %
- Benchmark version
- arXiv:2602.05986v1, Table 1
- Category
- text-image-to-video
- Direction
- Higher is better
- Source type
- independent-evaluator
- Evaluator
- RISE-Video
- Status
- active
- Retrieved at
- 2026-10-16
- Methodology
- Independent evaluation. RISE-Video Table 1, text-image-to-video evaluation; exact model label from the table. GPT-5 judges reasoning alignment, temporal consistency, and physical rationality; GPT-5-mini judges visual quality. The paper does not provide a per-model denominator for these metrics.
- Context
- This is a task-specific paper result, not a universal video-quality score. Do not compare its numeric value directly with other benchmark scales.
RISE-Video (Show measurement, test conditions, and source)
- Source value
- 72.5
- Score
- 72.5
- Metric
- Physical Rationality (PR)
- Unit
- %
- Benchmark version
- arXiv:2602.05986v1, Table 1
- Category
- text-image-to-video
- Direction
- Higher is better
- Source type
- independent-evaluator
- Evaluator
- RISE-Video
- Status
- active
- Retrieved at
- 2026-10-16
- Methodology
- Independent evaluation. RISE-Video Table 1, text-image-to-video evaluation; exact model label from the table. GPT-5 judges reasoning alignment, temporal consistency, and physical rationality; GPT-5-mini judges visual quality. The paper does not provide a per-model denominator for these metrics.
- Context
- This is a task-specific paper result, not a universal video-quality score. Do not compare its numeric value directly with other benchmark scales.
RISE-Video (Show measurement, test conditions, and source)
- Source value
- 94.5
- Score
- 94.5
- Metric
- Visual Quality (VQ)
- Unit
- %
- Benchmark version
- arXiv:2602.05986v1, Table 1
- Category
- text-image-to-video
- Direction
- Higher is better
- Source type
- independent-evaluator
- Evaluator
- RISE-Video
- Status
- active
- Retrieved at
- 2026-10-16
- Methodology
- Independent evaluation. RISE-Video Table 1, text-image-to-video evaluation; exact model label from the table. GPT-5 judges reasoning alignment, temporal consistency, and physical rationality; GPT-5-mini judges visual quality. The paper does not provide a per-model denominator for these metrics.
- Context
- This is a task-specific paper result, not a universal video-quality score. Do not compare its numeric value directly with other benchmark scales.
RISE-Video (Show measurement, test conditions, and source)
- Source value
- 77.8
- Score
- 77.8
- Metric
- Weighted overall score (W.Score)
- Unit
- %
- Benchmark version
- arXiv:2602.05986v1, Table 1
- Category
- text-image-to-video
- Direction
- Higher is better
- Source type
- independent-evaluator
- Evaluator
- RISE-Video
- Status
- active
- Retrieved at
- 2026-10-16
- Methodology
- Independent evaluation. RISE-Video Table 1, text-image-to-video evaluation; exact model label from the table. GPT-5 judges reasoning alignment, temporal consistency, and physical rationality; GPT-5-mini judges visual quality. The paper does not provide a per-model denominator for these metrics.
- Context
- This is a task-specific paper result, not a universal video-quality score. Do not compare its numeric value directly with other benchmark scales.
RISE-Video (Show measurement, test conditions, and source)
- Source value
- 21.3
- Score
- 21.3
- Metric
- Accuracy
- Unit
- %
- Benchmark version
- arXiv:2602.05986v1, Table 1
- Category
- text-image-to-video
- Direction
- Higher is better
- Source type
- independent-evaluator
- Evaluator
- RISE-Video
- Status
- active
- Retrieved at
- 2026-10-16
- Methodology
- Independent evaluation. RISE-Video Table 1, text-image-to-video evaluation; exact model label from the table. GPT-5 judges reasoning alignment, temporal consistency, and physical rationality; GPT-5-mini judges visual quality. The paper does not provide a per-model denominator for these metrics.
- Context
- This is a task-specific paper result, not a universal video-quality score. Do not compare its numeric value directly with other benchmark scales.
VIPER (Show measurement, test conditions, and source)
- Source value
- 26.2
- Score
- 26.2
- Metric
- POC@1.0, temporal
- Unit
- %
- Benchmark version
- ACL 2026 long paper, Table 2
- Category
- temporal
- Direction
- Higher is better
- Sample
- 43
- Source type
- independent-evaluator
- Evaluator
- VIPER (ACL 2026)
- Status
- active
- Retrieved at
- 2026-10-16
- Methodology
- Independent evaluation. VIPER Table 2 image-conditioned video reasoning, one generated video per prompt, 309 items overall; domain rows retain their published domain counts. Main metric is POC@1.0 with GPT-5 judging; OC and outcome-hacking rate are reported separately.
- Context
- This is a task-specific paper result, not a universal video-quality score. Do not compare its numeric value directly with other benchmark scales.
Recorded measurement details
LMArena Text to Video: LMArena Text to Video (Show measurement, test conditions, and source)
- Source value
- 1327
- Score
- 1327
- Metric
- elo-rating
- Unit
- Elo
- Task
- lmarena-text-to-video
- Task label
- LMArena Text to Video
- Direction
- Higher is better
- Source rank
- 21
- Sample
- 54050
- Lower confidence bound
- 1320
- Upper confidence bound
- 1334
- Retrieved at
- 2026-11-05
LMArena Image to Video: LMArena Image to Video (Show measurement, test conditions, and source)
- Source value
- 1310
- Score
- 1310
- Metric
- elo-rating
- Unit
- Elo
- Task
- lmarena-image-to-video
- Task label
- LMArena Image to Video
- Direction
- Higher is better
- Source rank
- 23
- Sample
- 121082
- Lower confidence bound
- 1303
- Upper confidence bound
- 1317
- Retrieved at
- 2026-11-05
Page 1 of 9
Video sample from our test
This video exists as a documented run for the exact model version. Playback is muted.
Motion and physical consistency
This sample tests Anatomy, object consistency, camera tracking, rain, and reflections.
Prompt and settings
Generate exactly one continuous five-second 16:9 cinematic video. A fictional bicycle courier in a mustard-yellow raincoat rides a red city bicycle from left to right across a rain-wet Hamburg street at blue hour for the entire clip. The camera tracks her smoothly at waist height. Both hands remain visible on the handlebars, the bicycle geometry stays physically correct, and a transparent umbrella fixed to the bicycle moves naturally in the wind. Wet-street reflections and raindrops follow realistic physics. No cuts, no slow motion, no text, no logos, no subtitles, and no audio.
Generated through OpenRouter with model alibaba/wan-2.6. Archived settings were 16:9, one request, first output, requested 5 s, request {"model":"alibaba/wan-2.6","prompt":"Generate exactly one continuous five-second 16:9 cinematic video. A fictional bicycle courier in a mustard-yellow raincoat rides a red city bicycle from left to right across a rain-wet Hamburg street at blue hour for the entire clip. The camera tracks her smoothly at waist height. Both hands remain visible on the handlebars, the bicycle geometry stays physically correct, and a transparent umbrella fixed to the bicycle moves naturally in the wind. Wet-street reflections and raindrops follow realistic physics. No cuts, no slow motion, no text, no logos, no subtitles, and no audio.","aspect_ratio":"16:9","duration":5,"resolution":"720p","generate_audio":false,"provider":{"options":{"atlas-cloud":{"parameters":{"enable_prompt_expansion":false,"audio":false}}}}}, live estimated cost $0.4000, actual cost $0.4000.
Popularity compared
How often the model is searched for, used and mentioned, compared with every model of its category.
Popularity index
0 to 100, higher is more popular
20 of 98 models
As of November 2026 · Source: gradually.ai popularity index
Over time
Weekly values from October 18, 2026 to November 5, 2026
Wan 2.6Sora 2 ProPixVerse V5.6Wan 2.2 A14BHailuo 2.3
Wan 2.6 compared
Compare with another model
Predecessor and successor
The most popular comparisons
- 1Gemini Omni Flash vs. Wan 2.6
- 2FLUX.3 Video vs. Wan 2.6
- 3Dreamina Seedance 2.5 720p vs. Wan 2.6
- 4Dreamina Seedance 2.0 720p vs. Wan 2.6
- 5MiniMax H3 vs. Wan 2.6
- 6Sora 2 Pro vs. Wan 2.6
- 7Sora 2 vs. Wan 2.6
- 8Veo 3.1 vs. Wan 2.6
- 9Veo 3.1 Fast vs. Wan 2.6
Show all 47 comparisons
- 10Grok Imagine Video 1.5 vs. Wan 2.6
- 11Grok Imagine Video vs. Wan 2.6
- 12Wan 2.7 vs. Wan 2.6
- 13Wan 2.6 vs. Runway Gen-4.5
- 14Wan 2.6 vs. Kling 3.0 Pro
- 15Wan 2.6 vs. Kling 2.6 Pro
- 16Wan 2.6 vs. Vidu Q3 Pro
- 17Wan 2.6 vs. PixVerse V5.6
- 18Wan 2.6 vs. Hailuo 2.3
- 19Wan 2.6 vs. LTX-2 19B
- 20Wan 2.6 vs. HunyuanVideo 1.5
- 21Wan 2.6 vs. Pika 2.2
- 22Wan 2.6 vs. Gemini Omni 1.1 Flash
- 23Wan 2.6 vs. Muse Video
- 24Wan 2.6 vs. Wan 3.0
- 25Wan 2.6 vs. Wan 3.0 Prime
- 26Wan 2.6 vs. HappyHorse 1.0
- 27Wan 2.6 vs. HappyHorse 1.1
- 28Wan 2.6 vs. PixVerse V6
- 29Wan 2.6 vs. PixVerse C1
- 30Wan 2.6 vs. Seedance 2.0 Fast
- 31Wan 2.6 vs. Seedance 2.0 Mini
- 32Wan 2.6 vs. Vidu Q3 Turbo
- 33Wan 2.6 vs. Vidu Q2
- 34Wan 2.6 vs. Vidu Q1
- 35Wan 2.6 vs. Kling 4.0
- 36Wan 2.6 vs. Kling 3.0 Standard API
- 37Wan 2.6 vs. Kling Video O1
- 38Wan 2.6 vs. Hailuo 2.3 Fast
- 39Wan 2.6 vs. Wan 2.5 Preview
- 40Wan 2.6 vs. Wan 2.6 I2V Flash
- 41Wan 2.6 vs. Wan 2.2 TI2V 5B
- 42Wan 2.6 vs. Wan 2.2 I2V A14B
- 43Wan 2.6 vs. HunyuanVideo 1.0 T2V
- 44Wan 2.6 vs. HunyuanVideo I2V 1.0
- 45Wan 2.6 vs. Ray 3.2
- 46Wan 2.6 vs. Grok Imagine Video 1.5 Lite
- 47Wan 2.6 vs. LTX-2.5
All specifications
Input and output
Watermarking and provenance
AI watermarking refers to invisible signals embedded in the output. Visible marks and provenance metadata are listed separately. Unknown means there is insufficient evidence for that particular field.
- AI watermarking
- Unknown
- no source
- Watermarking technique
- Not known
- no source
Licence and access
Prices
No Alibaba subscription includes this model. Through other providers from ≈ €6.36 a month (ElevenLabs Starter, with limited credits). 41 plans in Germany.
| Plan | Monthly price | Note |
|---|---|---|
| ElevenLabs Starter | ≈ €6.36 | with limited credit |
| Poe Basic | €6.83 | with limited credit |
| Krea Basic | ≈ €9.54 | with limited credit |
| Leonardo.Ai Essential | ≈ €12.72 | |
| Runway Standard | ≈ €15.90 | with limited credit |
| Magnific Premium | €19.04 | with limited credit |
| Recraft Pro | ≈ €21.20 | with limited credit |
| Higgsfield Starter | €22.61 | with limited credit |
| ElevenLabs Creator | ≈ €23.33 | with limited credit |
| Recraft Teams | ≈ €23.33 | with limited credit, for teams, per seat |
| Leonardo.Ai Team Starter | ≈ €25.45 | for teams, per seat |
| Poe Plus | €27.36 | with limited credit |
| Leonardo.Ai Premium | ≈ €31.81 | |
| Krea Pro | ≈ €37.11 | with limited credit |
| Runway Pro | ≈ €37.11 | with limited credit |
| Magnific Premium+ | €42.84 | with limited credit |
| Leonardo.Ai Team Growth | ≈ €50.89 | for teams, per seat |
| Leonardo.Ai Ultimate | ≈ €63.61 | |
| Poe Pro | €68.41 | with limited credit |
| Higgsfield Plus | €70.21 | with limited credit |
| Magnific Business | €70.21 | with limited credit, for teams, per seat |
| Runway Team | ≈ €73.16 | with limited credit, for teams, per seat |
| Higgsfield Team | €94.01 | with limited credit, for teams, per seat |
| Runway Max | ≈ €100.72 | with limited credit |
| Magnific Pro Starter | €104.72 | with limited credit |
| ElevenLabs Pro | ≈ €104.96 | with limited credit |
| Krea Max | ≈ €111.32 | with limited credit |
| Poe Advanced | €136.84 | with limited credit |
| Higgsfield Ultra | €153.51 | with limited credit |
| Higgsfield Scale | €201.11 | with limited credit, for teams, per seat |
| Krea Business | ≈ €212.05 | with limited credit, for teams |
| Magnific Pro | €273.70 | with limited credit |
| ElevenLabs Scale | ≈ €317.01 | with limited credit, for teams |
| Poe Max | €342.11 | with limited credit |
| ElevenLabs Business | ≈ €1,049.63 | with limited credit, for teams |
| ElevenLabs Enterprise | n/a | for enterprises |
| Higgsfield Enterprise | n/a | for enterprises |
| Krea Enterprise | n/a | for enterprises |
| Leonardo.Ai Custom | n/a | for enterprises |
| Magnific Enterprise | n/a | for enterprises |
| Runway Enterprise | n/a | for enterprises |
Frequently asked questions about Wan 2.6
How good is Wan 2.6?
Rank 15 of 23 in the text-to-video arena rating. With a start image: rank 14 of 21.
LMArena, as of Sep 22, 2026
How much does a video with Wan 2.6 cost?
About $0.75 for a 5 s clip, $0.15 per second of video. That is fairly expensive.
How long can a video with Wan 2.6 be?
Up to 15 seconds in one go. Resolution: 1080p.
According to Alibaba
- Price
- $0.15 per second$0.75 per 5 s clip
- Longest clip
- 15s
- Resolution
- 1080p
- Audio
- yes, generated natively
- Open weights
- no
Where can I use Wan 2.6?
Not in a Alibaba subscription. Including other providers that makes 41 plans in Germany.
Subscription comparison, prices in Germany incl. VAT
At Alibaba
No Alibaba subscription includes this model.
Through other providers
41 plans from 8 providers, from ≈ €6.36 a month (ElevenLabs Starter), with limited credits.
Sources and data date
Show all 33 sources
Benchmark sources at a glance
Which independent evaluators tested this model. This is coverage, not a quality ranking.
| Evaluator | Test series | Composite indices | Recorded active measurements |
|---|---|---|---|
| LMArena | 0 | 0 | 2 |
Without vendor claims and historical or disputed results; those stay labelled in the single values. 2 measurements are not yet assigned to a test series.
| Type | Evidence and data date |
|---|---|
| Research status | Research date October 16, 2026. 25 source URLs checked. This documents the inspected sources, not an exhaustive inventory of every publication. |
| Not found in the inspected sources | arena-video-edit-exact-row Note The inspected Video Edit table has no exact model-version row. T2V and I2V values were not reused. |
| Not found in the inspected sources | vabench-exact-model-row Note VABench has no exact model row. Sora 2 was not transferred to Sora 2 Pro or other models. |
| Not found in the inspected sources | artificial-analysis-t2v-exact-row Note No exact model version or resolution was found in the inspected Artificial Analysis table. |
| Not found in the inspected sources | artificial-analysis-i2v-exact-row Note No exact model version or resolution was found in the inspected Artificial Analysis table. |
| Unresolved | vbench-current-public-leaderboard Note The current VBench leaderboard is embedded in a dynamic Hugging Face iframe and was not retrievable; current scores remain unresolved. |
| Unresolved | vidbenchmark-performance-axes Note The source publishes speed-to-first-frame and cost-efficiency axes, but its inspected methodology does not define them as measured performance results or disclose units, direction, or a scoring formula. They are omitted. Model limits, maximum resolution, and list prices are not quality measurements. |
| Not published | VEFX-Bench sample count and generated resolution Note Table 7 publishes neither per-model evaluated sample count nor Wan 2.6 output resolution. The 399,360-pixel frame cap describes evaluator processing, not generated resolution. |
| Not published | servingRevision Original wording The dataset identifies the participant name but not provider API route, served checkpoint revision, quantization, or resolution. |
| Unresolved | datasetRowCount Original wording The pinned README states 21,125 matchup rows, while current Hugging Face viewer metadata reports 22,625. Neither aggregate is used as sampleCount or leaderboard N. |
| Not published | serving-route-and-output-settings Original wording The paper identifies Wan2.6 and commercial-API access, but does not report the exact endpoint, revision, request settings, output resolution, or duration. |
| Unresolved | per-model T2AV-Compass denominator and confidence interval Note Tables 2 and 3 show the Wan-2.6 values without a model-level N or confidence interval. The Table 2 caption says some closed-source generations failed security review; no denominator was inferred from the 500-item dataset size. |
| Additional source | Alibaba · Alibaba Cloud Model Studio video generation and editing · Editorial description reviewed October 2, 2026 |
| Additional source | Video pricing |
| Additional source | OpenRouter |
| Additional source | LMArena (retrieved November 5, 2026)12 |
| Additional source | WorldReasonBench (retrieved October 16, 2026; October 18, 2026) |
| Additional source | RISE-Video (retrieved October 16, 2026) |
| Additional source | VIPER (ACL 2026) (retrieved October 16, 2026) |
| Additional source | Video Models by Wiro AI (retrieved October 16, 2026; October 28, 2026) |
| Additional source | vidbenchmark (retrieved October 16, 2026) |
| Additional source | RBench, Rethinking Video Generation Model for the Embodied World (retrieved October 17, 2026; October 24, 2026) |
| Additional source | VEFX-Bench, Table 7 (retrieved October 17, 2026) |
| Additional source | Rapidata, image-to-video-physics-human-feedback-benchmark dataset README (retrieved October 17, 2026) |
| Additional source | Qwen Team, Qwen-RobotWorld Technical Report (retrieved October 17, 2026) |
| Additional source | T2AV-Compass, researcher-conducted evaluation (retrieved October 19, 2026) |
| Additional source | AVGen-Bench paper, Table 2 (retrieved October 21, 2026) |
| Additional source | Sci-VBench paper, arXiv:2608.09873v1 (retrieved October 23, 2026) |
| Additional source | Arena Video Image-to-Video (retrieved September 21, 2026; October 16, 2026; October 28, 2026) |
| Additional source | Arena Video Text-to-Video (retrieved September 21, 2026; October 16, 2026; October 28, 2026) |
| Additional source | Rapidata I2V Physics benchmark (retrieved October 22, 2026; October 28, 2026) |
| Additional source | Model metadata (retrieved October 16, 2026)123 |
| Additional source | Model metadata (retrieved October 16, 2026; October 28, 2026) |
| Additional source | Model metadata (retrieved October 17, 2026) |
| Additional source | Alibaba Cloud Wan 2.6 reference-to-video API reference · Editorial description reviewed October 2, 2026 |
| Additional source | Alibaba Cloud Wan 2.6 announcement · Editorial description reviewed October 2, 2026 |
| Additional source | OpenRouter: Popularity index (derived), CC BY 4.0 |
| Additional source | Vercel AI Gateway: Popularity index (derived), CC BY 4.0 |
| Additional source | Ollama: cumulative downloads for the popularity index |
| Additional source | Replicate: cumulative runs for the popularity index |
| Additional source | Google Trending Now: limited feed selection for Germany and the US |
| Additional source | Creative Commons Attribution 4.0 |