Multimodal LLM APIs Compared: GPT-4V vs Gemini Vision vs Claude 3

Compare vision, audio, and multimodal capabilities across leading LLM providers to find the best fit for your needs.

As AI models evolve beyond text, choosing the right multimodal LLM API has become critical for applications requiring vision, audio, and cross-modal understanding. This comprehensive analysis compares the leading multimodal offerings from OpenAI, Google, and Anthropic.

Quick Comparison Matrix

Feature	GPT-4V/GPT-4o	Gemini 1.5 Pro	Claude 3 Vision
Image Understanding	✅ Excellent	✅ Excellent	✅ Excellent
Video Processing	❌ Not Available	✅ Native Support	❌ Not Available
Audio Input	✅ GPT-4o Only	✅ Native Support	❌ Not Available
Document OCR	✅ Very Good	✅ Excellent	✅ Very Good
Max Image Resolution	Detail: high mode	Native resolution	5MB limit
Context Window	128K tokens	2M tokens	200K tokens

Vision Capabilities

• Object detection and recognition
• Scene understanding
• OCR and text extraction
• Chart and diagram analysis
• Mathematical equation reading
• UI/UX screenshot analysis

Video Features

• Frame-by-frame analysis
• Temporal understanding
• Action recognition
• Scene transitions
• Content summarization
• Event detection

Audio Processing

• Speech transcription
• Multi-speaker detection
• Sound event recognition
• Music analysis
• Emotion detection
• Language identification

Detailed Model Analysis

GPT-4V / GPT-4o (OpenAI)

Industry-leading vision capabilities with optional audio

Strengths:

Superior reasoning and instruction following^[1]
Excellent chart and diagram understanding^[2]
Strong OCR capabilities across languages^[3]
GPT-4o adds native audio understanding^[4]

Limitations:

No native video support
Higher pricing compared to alternatives^[5]
Audio only available in GPT-4o variant

Best For:

Complex visual reasoning, document analysis, UI/UX testing, medical imaging analysis

Gemini 1.5 Pro (Google)

Most versatile with native video and audio support

Strengths:

Native video processing up to hours of content^[6]
Largest context window (2M tokens)^[7]
Unified multimodal architecture^[8]
Competitive pricing for high-volume use

Limitations:

Occasional hallucinations in complex scenes
Regional availability restrictions
Rate limits on video processing

Best For:

Video content analysis, long-form document processing, surveillance systems, content moderation

Claude 3 Vision (Anthropic)

Privacy-focused with strong safety features

Strengths:

Best-in-class safety and content filtering^[9]
Excellent technical diagram understanding
Strong privacy guarantees
Detailed explanations and reasoning

Limitations:

No video or audio support
5MB image size limit
More conservative outputs

Best For:

Enterprise deployments, regulated industries, educational content, technical documentation

Performance Benchmarks

Benchmark	GPT-4V	Gemini 1.5 Pro	Claude 3 Opus	Test Type
MMMU	56.8%	58.5%	59.4%	Multi-discipline
MathVista	49.9%	52.1%	50.5%	Math reasoning
AI2D	78.2%	80.3%	88.1%	Diagram understanding
ChartQA	78.5%	81.3%	80.8%	Chart analysis
DocVQA	88.4%	90.9%	89.3%	Document QA

Pricing and Limits

Model	Image Size Limit	Audio Limit	Video Limit	Pricing
GPT-4V	20MB^[10]	N/A	N/A	$0.01/1K tokens
GPT-4o	20MB^[9]	25MB audio files^[9]	<60s clips^[13]	$2.50-30/1M tokens^[10]
Gemini 1.5 Pro	Hundreds of MB^[11]	Multi-hour audio^[11]	Up to 1 hour^[11]	$0.00125-0.005/1K tokens^[11]
Claude 3 Opus	5MB^[12]	N/A	N/A	$15/1M input tokens^[12]

Implementation Guide

Quick Start Examples

Vision API Calls

All three providers follow similar patterns for image analysis:

Base64 encode images or provide URLs
Include images in the messages array
Specify detail level for optimal processing
Handle multi-image inputs in single requests

Best Practices

Resize images to optimal dimensions before sending
Use appropriate compression for your use case
Batch multiple images when possible
Implement retry logic for rate limits
Cache responses for repeated queries

References

[1] OpenAI. "GPT-4V System Card" (2023)
[2] Liu, H., et al. "Evaluating GPT-4V on Visual Math Problem Solving" arXiv preprint (2023)
[3] Shi, J., et al. "GPT-4V(ision) for Robotics: Multimodal Task Planning from Human Demonstration" (2023)
[4] OpenAI. "Hello GPT-4o" Blog post (2024)
[5] OpenAI. "Pricing" (2024)
[6] Reid, M., et al. "Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context" (2024)
[7] Google DeepMind. "Gemini Capabilities" (2024)
[8] Gemini Team. "Gemini: A Family of Highly Capable Multimodal Models" (2023)
[9] Anthropic. "Introducing the Claude 3 Model Family" (2024)
[10] Artificial Analysis. "LLM Model Pricing and Performance Comparison" (2024)
[11] Google AI. "Gemini 1.5 Pro Technical Specifications" (2024)
[12] Anthropic. "Claude 3 Model Card and Evaluation" (2024)
[13] Zhou, P., et al. "Video Understanding in Large Vision-Language Models: A Comparative Study" arXiv preprint (2024)