Have Multimodal LLMs Matured: Early 2026 Real Testing

Simi included in AI

2026-03-10 354 words 2 minutes

Contents

What Multimodal LLMs Can Do

        
        
        
    
# Image understanding
image = "screenshot.png"
prompt = "What bug is in this screenshot?"
response = llm.analyze(image=image, text=prompt)

# Audio understanding
audio = "meeting.mp3"
prompt = "What are the key points in this meeting recording?"
response = llm.analyze(audio=audio, text=prompt)

# Video understanding
video = "demo.mp4"
prompt = "What features does this video demonstrate?"
response = llm.analyze(video=video, text=prompt)

Horizontal Comparison

Image Understanding Tests

Test	GPT-4o	Gemini 2.0	Claude 3.7
UI screenshot bug finding	88%	82%	92%
Chart data extraction	95%	97%	93%
Handwriting recognition	85%	88%	80%
Flowchart understanding	90%	85%	88%
Code screenshot	92%	87%	95%

Claude 3.7 strongest on code screenshots, Gemini slightly better on data extraction.

Audio Understanding Tests

Test: 1-hour meeting recording, summarize key decisions and action items

GPT-4o:
  - accuracy: 85%
  - key decision identification: ✅
  - action item identification: ✅
  - speaker differentiation: ✅

Gemini 2.0:
  - accuracy: 90%
  - key decision identification: ✅
  - action item identification: ✅
  - speaker differentiation: ✅

Claude 3.7:
  - accuracy: 87%
  - key decision identification: ✅
  - action item identification: ✅
  - speaker differentiation: ❌

Video Understanding Tests

Test: 5-minute product demo video, describe core features

GPT-4o:
  - accuracy: 75%
  - inter-frame consistency: ✅
  - key frame identification: ✅

Gemini 2.0:
  - accuracy: 82%
  - inter-frame consistency: ✅
  - key frame identification: ✅

Claude 3.7:
  - accuracy: 78%
  - inter-frame consistency: ✅
  - key frame identification: ✅

Real Application Scenarios

1. Code Screenshot Review

        
        
        
    
# Claude 3.7 strongest
response = claude.analyze_image(
    image="buggy_code.png",
    prompt="What bug is in this code screenshot?"
)
# 95% accuracy, strongest

2. UI Design Review

        
        
        
    
# Gemini 2.0 or GPT-4o both work
# Gemini slightly better (data extraction)
response = gemini.analyze_image(
    image="ui_design.png",
    prompt="What usability issues does this UI design have?"
)

3. Meeting Notes Summary

        
        
        
    
# Gemini 2.0 strongest
response = gemini.analyze_audio(
    audio="meeting.mp3",
    prompt="Summarize key decisions and action items"
)
# 90% accuracy

Conclusion

Early 2026 multimodal has matured:

Image understanding: Claude 3.7 strongest (especially code)
Audio understanding: Gemini 2.0 strongest
Video understanding: Gemini 2.0 strongest

But differences are small—selection more about overall toolchain integration.

Multimodal is standard LLM capability, no longer a differentiator.