Overview

This document covers Large Language Models with native video understanding capabilities as of 2025, including both proprietary API-accessible models and open-source alternatives.

Proprietary Models

Google Gemini - Leading Video Support

Gemini 2.5 Pro and Gemini 2.0 Flash offer the most advanced native video understanding among proprietary LLMs.

Key Capabilities

  • Native video processing - Direct video file upload and understanding
  • Flexible frame sampling - Default 1 FPS, adjustable based on use case
  • Massive context windows:
    • Gemini 2.5 Pro: 2 million tokens with ‘low’ resolution setting (~6 hours of video)
    • Gemini 2.0 Flash: 1 million token context
  • Live API - Real-time streaming audio/video processing with low latency
  • YouTube integration - Process billions of videos directly via URL

Frame Rate Recommendations

  • Low FPS (< 1) - For long videos, general content analysis
  • High FPS - For granular temporal analysis, fast-action understanding, high-speed motion tracking

API Access

Available through:

  • Google AI Studio
  • Gemini API
  • Vertex AI

Cost: More cost-effective with ‘low’ media resolution parameter while maintaining competitive video understanding performance.

Sources:

OpenAI GPT-4o - Limited Video Support

GPT-4o (“omni”) was designed to accept text, audio, image, and video inputs, but native video input is not yet available in the API.

Current Status

❌ No native video API - As of 2025, video input not supported in OpenAI API ⚠️ Workaround available - Extract frames and send as individual images ✅ Text and vision API - Fully supported for text and image inputs 🔮 Planned capabilities - Audio and video features available to select trusted partners only

How to Process Video (Workaround)

  1. Split video into individual frames
  2. Sample frames strategically (GPT-4o has 1M token context window)
  3. Send frames as image inputs to the vision API
  4. GPT-4o processes better than GPT-4 Turbo at this task

Future Roadmap

OpenAI stated improvements will allow:

  • More natural, real-time voice conversation
  • Real-time video conversation with ChatGPT
  • Full audio-video chat functionality

No ETA provided for native video API access.

Sources:

Anthropic Claude - No Video Support

Claude 3.5 Sonnet and Claude 4.5 Sonnet do not support video input.

Current Capabilities

✅ Text and image input - All Claude models support text and vision ✅ Strong vision model - Claude 3.5 Sonnet surpasses Claude 3 Opus on standard vision benchmarks ✅ Visual reasoning - Excellent for charts, graphs, imperfect image transcription ✅ Document analysis - Superior for PDFs, spreadsheets, tables, embedded figures ✅ Large context - 200K tokens (base), up to 1M tokens (advanced models) ❌ No video support - No live audio/video capabilities

Best Use Cases

Claude excels at:

  • Complex PDF and spreadsheet reasoning
  • Structured data interpretation
  • Charts and diagram analysis
  • Text transcription from images

Sources:

According to industry analysis, multimodal integration including video, audio, and image understanding are becoming standard rather than premium features across leading LLMs.

Sources:

Open Source Models

Video-LLaMA Series

Video-LLaMA is a family of open-source audio-visual language models for video understanding.

Evolution

  • Video-LLaMA (2023) - EMNLP 2023 Demo, trained on Webvid-2M video captions + ~595K image captions from LLaVA
  • VideoLLaMA 2 - Advances in spatial-temporal modeling and audio understanding
  • VideoLLaMA 3 (Jan 2025) - Latest release with enhanced performance across image and video benchmarks

Key Features

✅ Audio-visual understanding ✅ Video-to-text generation ✅ Instruction-tuned for video tasks ✅ Self-hostable open source

Sources:

LLaVA Video Models

The LLaVA (Large Language and Vision Assistant) family includes several video-capable variants.

Video-LLaVA

  • Training approach - Fine-tuned on multimodal instruction data from LLaVA 1.5 and VideChat
  • Mixed dataset learning - Learns from both images and videos, mutually enhancing each other
  • Strong benchmarks - Outperforms Video-ChatGPT by 5.8%, 9.9%, 18.6%, and 10.1% on MSRVTT, MSVD, TGIF, and ActivityNet

LLaVA-NeXT Video

  • Zero-shot video - Strong performance without seeing video data during training
  • Modality transfer - Outperforms existing open-source LMMs trained specifically for videos (e.g., LLaMA-VID)
  • Comparable to proprietary - Achieves comparable performance to commercial models

LLaVA-OneVision 1.5

  • Native resolution - Trains on native resolution images for state-of-the-art performance with lower cost
  • Fully open source - Complete model weights and training code available
  • Superior benchmarks - Outperforms Qwen2.5-VL in most evaluation tasks

Sources:

SlowFast-LLaVA-1.5 (2025)

Token-efficient solution for long-form video understanding released in 2025.

Architecture

  • Two-stream SlowFast mechanism - Efficient modeling of long-range temporal context
  • Lightweight models - 1B to 7B parameters, mobile-friendly
  • Long-form optimization - Specifically designed for extended videos

Performance

✅ State-of-the-art on long-form benchmarks (LongVideoBench, MLVU) ✅ Excels at small scales - Strong performance at 1B and 3B parameters ✅ Efficient token usage - Reduces computational overhead for long videos

Sources:

Research Resources

Academic Surveys

“Video Understanding with Large Language Models: A Survey” (IEEE TCSVT 2025)

Comprehensive survey covering:

  • Video understanding techniques powered by Vid-LLMs
  • Training strategies
  • Relevant tasks, datasets, and benchmarks
  • Evaluation methods

Source: Awesome-LLMs-for-Video-Understanding GitHub

Evaluation Frameworks

  • lmms-eval - Consistent and efficient evaluation framework for Large Multimodal Models
  • VideoMMMU - Benchmark evaluating knowledge acquisition from educational videos across 6 professional disciplines

Summary Comparison

FeatureGemini 2.5 ProGPT-4oClaude 4.5Open Source
Native Video✅ Yes❌ No❌ No✅ Yes
API AccessGoogle AI APIFrame workaroundN/ASelf-hosted
Context Window2M tokens1M tokens1M tokensVaries
Live Streaming✅ Yes (2.0 Flash)❌ No❌ NoLimited
YouTube Support✅ Yes❌ No❌ No❌ No
Best ForVideo analysisImage framesDocumentsSelf-hosting

Recommendations

Choose Gemini 2.5 Pro if you need:

  • Native video understanding via API
  • Processing hours of video content
  • YouTube video analysis
  • Real-time video streaming (use 2.0 Flash)

Choose GPT-4o if you need:

  • Strong image understanding with video frame extraction
  • Large context window for many frames
  • OpenAI ecosystem integration

Choose Claude if you need:

  • Document/PDF analysis (no video)
  • Chart and graph interpretation
  • Structured data reasoning

Choose Open Source (Video-LLaMA, LLaVA) if you need:

  • Self-hosted deployment
  • Full model control
  • Audio-visual understanding
  • Cost optimization for high volume
  • Long-form video processing (SlowFast-LLaVA)

Future Outlook

As of 2025, the trend is clear: multimodal capabilities including video are transitioning from premium features to standard expectations. Gemini leads in proprietary video support, while open-source alternatives continue to advance rapidly with models like VideoLLaMA 3 and LLaVA-OneVision achieving competitive performance.

OpenAI’s roadmap suggests native video support is planned but not yet available in the API, leaving Gemini as the current leader for production video understanding use cases.