What is the best video understanding model right now?
Gemini 3.1 Pro. It reads long footage and its audio together and handles up to three hours in one request. Its score uses the earlier Gemini 3 Pro benchmark result, while Doubao Seed 2.0 Pro is the strongest directly measured current model and costs far less.
What is the best video understanding model for most people?
Gemini 3.5 Flash. You get the same direct video-and-audio workflow as 3.1 Pro, faster and much cheaper, with only a small quality drop. Test it against Doubao Seed 2.0 Pro on your own footage, since that pairing covers most hosted use at a sensible price.
What is the best open-weight video model?
Qwen3.5 397B A17B has the highest measured open score, but it is server-only. Kimi K2.5 is the easiest to actually use, with a first-party app and API on top of its weights. If you need the embedded audio track read in one model, MiMo V2.5 is the standout open pick.
What is the best video understanding model you can run locally?
On a typical machine, GLM-4.6V Flash and SmolVLM2 2.2B are the realistic options - GLM for more capability, SmolVLM2 for the lightest laptop footprint. With a high-end GPU, Qwen3.5 27B is meaningfully stronger, while Gemma 4 31B and Qwen3.6 35B A3B suit short clips and efficient self-hosting respectively.
Which of these models understand audio, not just the picture?
Gemini 3.1 Pro, Gemini 3.5 Flash, Doubao Seed 2.0 Pro, MiMo V2.5, Qwen3.5 Omni Plus, and Pegasus 1.5 read the embedded audio track directly. The other Qwen checkpoints, Gemma 4 31B, GLM-4.6V Flash, and SmolVLM2 2.2B handle only visuals, so you supply speech through a separate transcription step.
Is a video-analysis platform the same thing as a video understanding model?
No. Products like video indexers and search platforms often wrap a model in retrieval, OCR, and transcription. This list ranks the models themselves. Pegasus 1.5 is a genuine model, not a platform, but reach for a retrieval pipeline when useful moments are sparse across many hours of footage.
Do these benchmark scores match real-world use?
Roughly. They predict which models reason across time and handle long clips, but results shift with frame count, audio input, prompting, and each provider’s own preprocessing. Some scores here are estimates or predecessor proxies. Treat close rankings as ties and run a short test on your own videos before committing.
What matters most when choosing a model for video understanding?
Three things: how you want to access it (app, API, or self-hosted), how long your videos are and whether audio matters, and your tolerance for cost versus quality. Match the model to your longest, messiest real footage, because that is where the differences show up.