Benchmark Almanac

CineRecon

Claude Opus 5.5 76.6 leads since October 2026
Not in model cards

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.

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.

Abstract of CineRecon: Benchmarking Frontier Models for Agentic Cinematic Reconstruction (arXiv:2610.16577, CC0)
12
Models scored on its own leaderboard

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:

  1. 1 Claude Opus 5.5 All settings mean; high effort 76.6
  2. 2 GPT-6 Astra All settings mean 63.8
  3. 3 Kimi K3 All settings mean 52.4
  4. 4 DeepSeek V4.1 Flash All settings mean 49.5
  5. 5 Grok 4.6 All settings mean 49.0
  6. 6 GLM-5.3-Flash All settings mean 47.6
  7. 7 Gemini 3.8 Flash All settings mean 47.1
  8. 8 Qwen 3.8 Max All settings mean 45.2
  9. 9 Claude Opus 4.8 All settings mean 40.3
  10. 10 Qwen3.8 Flash All settings mean 39.3
  11. 11 GPT-5.6 Sol All settings mean 35.9
  12. 12 Gemini 3.1 Pro All settings mean 20.1

Source: Official CineRecon leaderboard

#ModelScoreSettingDateSource
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 DeepSeek V4.1 Flash 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 Gemini 3.8 Flash 47.1 All settings mean Sep 2026 Official leaderboard
8 Qwen 3.8 Max 45.2 All settings mean Aug 2026 Official leaderboard
9 Claude Opus 4.8 40.3 All settings mean May 2026 Official leaderboard
10 Qwen3.8 Flash 39.3 All settings mean Aug 2026 Official leaderboard
11 GPT-5.6 Sol 35.9 All settings mean Jul 2026 Official leaderboard
12 Gemini 3.1 Pro 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.

0% 20% 40% 60% 80% $0$5$10$15$20$25 Generation API cost per video ($), lower is better Mean checklist score Gemini 3.1 Pro (All settings mean): Mean checklist score 20.1%, generation api cost per video $2.45 Claude Opus 4.8 (All settings mean): Mean checklist score 40.3%, generation api cost per video $8.97 GPT-5.6 Sol (All settings mean): Mean checklist score 35.9%, generation api cost per video $1.07 Kimi K3 (All settings mean): Mean checklist score 52.4%, generation api cost per video $11.4 Qwen 3.8 Max (All settings mean): Mean checklist score 45.2%, generation api cost per video $22.3 Grok 4.6 (All settings mean): Mean checklist score 49%, generation api cost per video $7.68 GLM-5.3-Flash (All settings mean): Mean checklist score 47.6%, generation api cost per video $1.01 Qwen3.8 Flash (All settings mean): Mean checklist score 39.3%, generation api cost per video $2.4 Gemini 3.8 Flash (All settings mean): Mean checklist score 47.1%, generation api cost per video $2.94 GPT-6 Astra (All settings mean): Mean checklist score 63.8%, generation api cost per video $10.1 DeepSeek V4.1 Flash (All settings mean): Mean checklist score 49.5%, generation api cost per video $0.39 Claude Opus 5.5 (All settings mean; high effort): Mean checklist score 76.6%, generation api cost per video $20.7 Claude Opus 5.5 (All settings mean; low effort): Mean checklist score 49.5%, generation api cost per video $1.63 Claude Opus 5.5 (All settings mean; high effort)GPT-6 Astra (All settings mean)Claude Opus 5.5 (All settings mean; low effort)DeepSeek V4.1 Flash (All settings mean) 0% 20% 40% 60% 80% $0$5$10$15$20$25 Generation API cost per video ($), lower is better Mean checklist score Gemini 3.1 Pro (All settings mean): Mean checklist score 20.1%, generation api cost per video $2.45 Claude Opus 4.8 (All settings mean): Mean checklist score 40.3%, generation api cost per video $8.97 GPT-5.6 Sol (All settings mean): Mean checklist score 35.9%, generation api cost per video $1.07 Kimi K3 (All settings mean): Mean checklist score 52.4%, generation api cost per video $11.4 Qwen 3.8 Max (All settings mean): Mean checklist score 45.2%, generation api cost per video $22.3 Grok 4.6 (All settings mean): Mean checklist score 49%, generation api cost per video $7.68 GLM-5.3-Flash (All settings mean): Mean checklist score 47.6%, generation api cost per video $1.01 Qwen3.8 Flash (All settings mean): Mean checklist score 39.3%, generation api cost per video $2.4 Gemini 3.8 Flash (All settings mean): Mean checklist score 47.1%, generation api cost per video $2.94 GPT-6 Astra (All settings mean): Mean checklist score 63.8%, generation api cost per video $10.1 DeepSeek V4.1 Flash (All settings mean): Mean checklist score 49.5%, generation api cost per video $0.39 Claude Opus 5.5 (All settings mean; high effort): Mean checklist score 76.6%, generation api cost per video $20.7 Claude Opus 5.5 (All settings mean; low effort): Mean checklist score 49.5%, generation api cost per video $1.63 Claude Opus 5.5 (All settings mean; high effort)GPT-6 Astra (All settings mean)Claude Opus 5.5 (All settings mean; low effort)DeepSeek V4.1 Flash (All settings mean)
ModelSettingMean checklist scoreGeneration 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
DeepSeek V4.1 Flash 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
Gemini 3.8 Flash All settings mean 47.1% $2.94
Qwen 3.8 Max All settings mean 45.2% $22.28
Claude Opus 4.8 All settings mean 40.3% $8.97
Qwen3.8 Flash All settings mean 39.3% $2.4
GPT-5.6 Sol All settings mean 35.9% $1.07
Gemini 3.1 Pro 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.

OpenAI DeepSeek Anthropic Cost frontier One model at each effort level
0.00 20.0 40.0 60.0 80.0 $0.10 $1.00 $10 $100 Cost per task, log scale Claude Opus 5.5, All settings mean; high effort GPT-6 Astra, All settings mean Claude Opus 5.5, All settings mean; low effort 0.00 20.0 40.0 60.0 80.0 $0.10 $1.00 $10 $100 Cost per task, log scale Claude Opus 5.5, All settings mean; high effort GPT-6 Astra, All settings mean

Source: Official CineRecon leaderboard

ModelEffortHarnessScoreCost per taskOutput 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
DeepSeek V4.1 Flash 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
Gemini 3.8 Flash All settings mean 47.1 $2.94
Qwen 3.8 Max All settings mean 45.2 $22
Claude Opus 4.8 All settings mean 40.3 $8.97
Qwen3.8 Flash All settings mean 39.3 $2.40
GPT-5.6 Sol All settings mean 35.9 $1.07
Gemini 3.1 Pro 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.

Anthropic
Own leaderboard 1 leader

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.

On the benchmark’s own leaderboard
20.0 40.0 60.0 80.0 2026 20.0 40.0 60.0 80.0 2026

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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