CineRecon
Agents use tools to rebuild reference videos from film, real footage and games as editable dynamic 3D scenes. The set has 50 videos and 360 evaluation clips.
It had 30 Hugging Face paper upvotes and 13 GitHub stars.
- Made by
- The University of Hong Kong
- Released
- October 2026
- Kind
- Video understanding
- Level
- Expert
- Scored
- Graded by a model
- Answers
- Agent task
- Tasks
- All public
- Strongest model tested
- GPT-6 Astra #3 on the capability index
- Last model added
- September 2026
- Scores refreshed
- November 12th, 2026
In the authors’ words
Reconstructing reference videos as editable dynamic 3D scenes is an important path toward controllable video generation for filmmaking: these scenes provide modifiable, renderable visual proxies that enable explicit control over scene layout, subject actions, and camera behavior. To evaluate agents' ability to reconstruct editable dynamic 3D scenes from dynamic reference videos, we introduce CineRecon, a benchmark comprising 50 reference videos and 360 annotated evaluation clips. The reference videos span film, real-world footage, and games. Given a complete video, an agent uses tools to construct, animate, render, and refine its reconstruction. We evaluate this task under three representation settings: Proxy, Simplified, and Detailed. These settings increase geometric and appearance requirements while retaining the same requirements for spatial relations, camera behavior, and subject dynamics. This design tests whether agents can reproduce the events in a video when detailed appearance is unnecessary, and how performance changes as representation requirements increase. For evaluation, we construct checklists that associate observable properties and events with specific entities and times. A vision-language judge compares timestamp-aligned reference and rendered frames across 11 categories, grouped into camera, static, and dynamic consistency dimensions. We evaluate 12 models using their corresponding agent harnesses and find failures across all three dimensions. Subject motions and interactions remain difficult to reproduce even under the Proxy setting.
Claude Opus 5.5 leads with 76.6 among models released by September 2026
Best score per model from the benchmark's own leaderboard, read on November 12th, 2026. Where a model ran with several settings or harnesses, we keep its best. Every run is in the next chart.
The newest model with a score here came out in September 2026, and nobody has tested a newer one. Among the 20 highest-ranked models on the capability index, these have no score yet:
- Claude Fable 5.2
-
Gemini 4 Carbon
- GPT-6.1 Sol
- GPT-6.1 Terra
- and 13 more
- 1 Claude Opus 5.5 All settings mean; high effort 76.6
- 2 GPT-6 Astra All settings mean 63.8
- 3 Kimi K3 All settings mean 52.4
- 4
DeepSeek V4.1 Flash All settings mean 49.5
- 5 Grok 4.6 All settings mean 49.0
- 6 GLM-5.3-Flash All settings mean 47.6
- 7
Gemini 3.8 Flash All settings mean 47.1
- 8
Qwen 3.8 Max All settings mean 45.2
- 9 Claude Opus 4.8 All settings mean 40.3
- 10
Qwen3.8 Flash All settings mean 39.3
- 11 GPT-5.6 Sol All settings mean 35.9
- 12
Gemini 3.1 Pro All settings mean 20.1
Source: Official CineRecon leaderboard
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Claude Opus 5.5 | 76.6 | All settings mean; high effort | Sep 2026 | Official leaderboard |
| 2 | GPT-6 Astra | 63.8 | All settings mean | Sep 2026 | Official leaderboard |
| 3 | Kimi K3 | 52.4 | All settings mean | Jul 2026 | Official leaderboard |
| 4 | | 49.5 | All settings mean | Sep 2026 | Official leaderboard |
| 5 | Grok 4.6 | 49.0 | All settings mean | Aug 2026 | Official leaderboard |
| 6 | GLM-5.3-Flash | 47.6 | All settings mean | Aug 2026 | Official leaderboard |
| 7 | | 47.1 | All settings mean | Sep 2026 | Official leaderboard |
| 8 | | 45.2 | All settings mean | Aug 2026 | Official leaderboard |
| 9 | Claude Opus 4.8 | 40.3 | All settings mean | May 2026 | Official leaderboard |
| 10 | | 39.3 | All settings mean | Aug 2026 | Official leaderboard |
| 11 | GPT-5.6 Sol | 35.9 | All settings mean | Jul 2026 | Official leaderboard |
| 12 | | 20.1 | All settings mean | Feb 2026 | Official leaderboard |
Performance and generation cost
Costs and chart scores average across representation settings. The line joins the runs that no other run beats on both measures. We redrew this chart from the numbers on the official leaderboard.
| Model | Setting | Mean checklist score | Generation API cost per video |
|---|---|---|---|
| Claude Opus 5.5 | All settings mean; high effort | 76.6% | $20.73 |
| GPT-6 Astra | All settings mean | 63.8% | $10.08 |
| Kimi K3 | All settings mean | 52.4% | $11.42 |
| Claude Opus 5.5 | All settings mean; low effort | 49.5% | $1.63 |
| | All settings mean | 49.5% | $0.39 |
| Grok 4.6 | All settings mean | 49% | $7.68 |
| GLM-5.3-Flash | All settings mean | 47.6% | $1.01 |
| | All settings mean | 47.1% | $2.94 |
| | All settings mean | 45.2% | $22.28 |
| Claude Opus 4.8 | All settings mean | 40.3% | $8.97 |
| | All settings mean | 39.3% | $2.4 |
| GPT-5.6 Sol | All settings mean | 35.9% | $1.07 |
| | All settings mean | 20.1% | $2.45 |
The best score, 76.6, costs $21 a task
Runs on the cost frontier score higher than any run that's cheaper. The costs are the leaderboard's own estimates, so compare them only within this benchmark.
Source: Official CineRecon leaderboard
| Model | Effort | Harness | Score | Cost per task | Output tokens |
|---|---|---|---|---|---|
| Claude Opus 5.5 | All settings mean; high effort | 76.6 | $21 | ||
| GPT-6 Astra | All settings mean | 63.8 | $10 | ||
| Kimi K3 | All settings mean | 52.4 | $11 | ||
| Claude Opus 5.5 | All settings mean; low effort | 49.5 | $1.63 | ||
| | All settings mean | 49.5 | $0.39 | ||
| Grok 4.6 | All settings mean | 49.0 | $7.68 | ||
| GLM-5.3-Flash | All settings mean | 47.6 | $1.01 | ||
| | All settings mean | 47.1 | $2.94 | ||
| | All settings mean | 45.2 | $22 | ||
| Claude Opus 4.8 | All settings mean | 40.3 | $8.97 | ||
| | All settings mean | 39.3 | $2.40 | ||
| GPT-5.6 Sol | All settings mean | 35.9 | $1.07 | ||
| | All settings mean | 20.1 | $2.45 |
Number one over time
Lab claims count from the day the card came out, independent runs from the model’s release.
Source: Official CineRecon leaderboard
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Source: Official CineRecon leaderboard
No lab model card reports it yet
We read the headline results of 522 launch posts from 52 labs, and none of them list this benchmark.
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