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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
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暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。
