We tested every major multimodal model on image understanding, audio transcription, and video analysis. The results are surprising.
Multimodal AI has moved from a niche capability to a core differentiator. Every major lab now ships models that can process text, images, audio, and video in a single forward pass. But which model actually performs best? We ran a comprehensive benchmark to find out.
Our test suite covered five dimensions: image captioning accuracy (COCO-2026), visual question answering (VQAv3), audio transcription (LibriSpeech-Clean + noisy variants), video understanding (a new 100-clip benchmark we call VidQA-100), and cross-modal reasoning (questions that require integrating information across modalities).
Google's Gemini Ultra 2 dominated video understanding, correctly answering 87% of VidQA-100 questions—15 points ahead of GPT-5 Turbo. Its 10-million-token context window gives it a structural advantage for long-form video analysis that no other production model can match.
For image tasks, GPT-5 Turbo and Claude 4 were statistically tied, both scoring above 92% on VQAv3. OpenAI's model had a slight edge on fine-grained visual details (reading small text in images, counting objects), while Claude 4 excelled at spatial reasoning and diagram interpretation.
Audio transcription was the most competitive category. OpenAI's Whisper v4 (integrated into GPT-5 Turbo) still leads on clean speech, but Gemini Ultra 2 outperformed it on noisy, multi-speaker, and multilingual audio by a significant margin.
All models in this benchmark are available for side-by-side testing on Vincony.com. Upload your own images, audio clips, or video files and compare outputs across models in real time.