Model overview
This model is a fine-tuned version of Qwen/Qwen2.5-VL-7B-Instruct, introduced in the paper ShotBench: Expert-Level Cinematic Understanding in Vision-Language Models. It is trained on the largest and high-quality dataset for cinematic language understanding to date. It currently achieves state-of-the-art performance on ShotBench, a comprehensive benchmark for evaluating cinematography understanding in vision-language models. Abbreviations: SS = Shot Size , SF = Shot Framing , CA = Camera Angle , LS = Lens Size , LT = Lighting Type , LC = Lighting Conditions , SC = Shot Composition , CM = Camera Movement . Underline marks previous best in each group. Our ShotVL models establish new SOTA. Open-Sourced VLMs Qwen2.5-VL-3B-Instruct 54.6 56.6 43.1 36.6 59.3 45.1 41.5 31.9 46.1 Qwen2.5-VL-7B-Instruct 69.1 73.5 53.2 47.0 60.5 47.4 49.9 30.2 53.8 LLaVA-NeXT-Video-7B 35.9 37.1 32.5 27.8 50.9 31.7 28.0 31.3 34.4 LLaVA-Video-7B-Qwen2 56.9 65.4 45.1 36.0 63.5 45.4 37.4 35.3 48.1 LLaVA-Onevision-Qwen2-7B-Ov-Chat 58.4 71.0 52.3 38.7 59.5 44.9 50.9 39.7 51.9 InternVL2.5-8B 56.3 70.3 50.8 41.1 60.2 45.1 50.1 33.6 50.9 InternVL3-2B 56.3 56.0 44.4 34.6 56.8 44.6 43.0 38.1 46.7 InternVL3-8B 62.1 65.8 46.8 42.9 58.0 44.3 46.8 44.2 51.4 InternVL3-14B 59.6 82.2 55.4 40.7 61.7 44.6 51.1 38.2 54.2 Internlm-xcomposer2d5-7B 51.1 71.0 39.8 32.7 59.3 35.7 35.7 38.8 45.5 Ovis2-8B 35.9 37.1 32.5 27.8 50.9 31.7 28.0 35.3 34.9 VILA1.5-3B 33.4 44.9 32.1 28.6 50.6 35.7 28.4 21.5 34.4 VILA1.5-8B 40.6 44.5 39.1 29.7 48.9 32.9 34.4 36.9 38.4 VILA1.5-13B 36.7 54.6 40.7 34.8 52.8 35.4 34.2 31.3 40.1 Instructblip-vicuna-7B 27.0 27.9 34.5 29.4 44.4 29.7 27.1 25.0 30.6 Instructblip-vicuna-13B 26.8 29.2 27.9 28.0 39.0 24.0 27.1 22.0 28.0 InternVL2.5-38B 67.8 85.4 55.4 41.7 61.7 48.9 52.4 44.0 57.2 InternVL3-38B 68.0 84.0 51.9 43.6 64.4 46.9 54.7 44.6 57.3 Qwen2.5-VL-32B-Instruct 62.3 76.6 51.0 48.3 61.7 44.0 52.2 43.8 55.0 Qwen2.5-VL-72B-Instruct 75.1 82.9 56.7 46.8 59.0 49.4 54.1 48.9 59.1 InternVL3-78B 69.7 80.0 54.5 44.0 65.5 47.4 51.8 44.4 57.2 Proprietary VLMs Gemini-2.0-flash 48.9 75.5 44.6 31.9 62
Key features
特点摘要待依据来源补齐，不凭模型名称推断能力。
Limitations & considerations
Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.
Source prices & cost analysis
Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.
| Source / channel | Input | Output | Billing unit | Collected at |
|---|---|---|---|---|
| No verified prices yet. Check the source channel. | ||||
Estimate monthly base token costs
Estimates exclude cache discounts, tiered pricing, multimodal surcharges, web search, taxes and additional reasoning output.
Public benchmarks & run configuration
暂无已采集的可核验公开评分。模型未上榜不代表能力较低。
View full indexed rankings →Frequently asked questions
Do open weights permit free commercial use?
Not necessarily. Check the version license, commercial conditions and usage policies. Self-hosting incurs compute and maintenance costs.
Are these real-time billing prices?
These are timestamped source snapshots, not live billing prices. Check the channel for current versions, regions, discounts and billing methods.
Sources & traceability
许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。