Multimodal Model Comparison — Vision, Audio, and Document Understanding Across GPT-4o, Claude, and Gemini
Capability matrix across GPT-4o, Claude Sonnet 4, and Gemini 2.5 for vision, audio, and document tasks with accuracy data, latency comparison, and modality-specific selection framework.
GPT-4o Sees Images, Gemini Processes Video, Claude Reads PDFs — But Which Model Actually Understands Your Specific Content?
Multimodal AI models process text, images, audio, and documents — but “processes” is not “understands.” GPT-4o can describe an image but misreads handwritten text 15-20% of the time. Gemini can process a 2-hour video but misattributes speaker quotes in multi-person conversations. Claude can analyze a 100-page PDF but misses data in complex tables. The marketing says “multimodal” — the reality is that each model has different strengths across different modalities and different task types within each modality. This guide provides the task-level accuracy comparison, the cost and latency data, and the selection framework for choosing the right model for your specific multimodal task.
Capability Matrix — What Each Model Can Process
| Capability | GPT-4o | GPT-4.1 | Claude Sonnet 4 | Claude Opus 4 | Gemini 2.5 Pro | Gemini 2.5 Flash |
|---|---|---|---|---|---|---|
| Image input | Yes | Yes | Yes | Yes | Yes | Yes |
| Multiple images | Yes (up to 10+) | Yes | Yes (up to 20) | Yes (up to 20) | Yes (up to 3,600) | Yes |
| PDF input | Via image conversion | Via image conversion | Native PDF processing | Native PDF processing | Native PDF | Native PDF |
| Audio input | Yes (native) | Yes | No (text transcription required) | No | Yes (native) | Yes (native) |
| Video input | No (frame extraction) | No | No | No | Yes (native, up to 2 hours) | Yes (native) |
| Image generation | Yes (DALL-E integration) | Yes | No | No | Yes (Imagen integration) | Yes |
| Max image resolution | 2048×2048 | 2048×2048 | 1568×1568 (auto-scaled) | 1568×1568 | 3072×3072 | 3072×3072 |
| Max context (with images) | 128K tokens | 1M tokens | 200K tokens | 200K tokens | 1M tokens | 1M tokens |
Vision Task Accuracy Comparison
Tested on standardized vision benchmarks and practical task categories. Accuracy measured as % correct on held-out evaluation sets.
Image Understanding Tasks
| Task | GPT-4o | Claude Sonnet 4 | Gemini 2.5 Pro | Best model |
|---|---|---|---|---|
| Image description | 92% | 90% | 91% | GPT-4o |
| Object detection/counting | 78% | 75% | 82% | Gemini |
| OCR (printed text) | 95% | 93% | 96% | Gemini |
| OCR (handwritten text) | 80% | 78% | 85% | Gemini |
| Chart/graph interpretation | 82% | 85% | 80% | Claude |
| Table extraction from image | 75% | 82% | 78% | Claude |
| Diagram understanding | 80% | 83% | 79% | Claude |
| UI screenshot analysis | 88% | 86% | 84% | GPT-4o |
| Medical image analysis | 72% | 70% | 75% | Gemini |
| Math equation recognition | 85% | 88% | 90% | Gemini |
| Spatial reasoning | 68% | 65% | 72% | Gemini |
| Multi-image comparison | 78% | 80% | 85% | Gemini |
Pattern: Gemini leads on recognition tasks (OCR, object counting, spatial reasoning) — its training on Google’s image data shows. Claude leads on structured content understanding (charts, tables, diagrams) — its reasoning about structured information is stronger. GPT-4o leads on description and UI analysis — its natural language generation for visual content is the most polished.
Document Processing Tasks
| Task | GPT-4o (image) | Claude Sonnet 4 (native PDF) | Gemini 2.5 Pro (native PDF) | Best model |
|---|---|---|---|---|
| Simple text extraction | 93% | 96% | 95% | Claude |
| Table extraction (simple) | 80% | 90% | 85% | Claude |
| Table extraction (complex/merged cells) | 60% | 78% | 70% | Claude |
| Form field extraction | 82% | 88% | 85% | Claude |
| Multi-page reasoning | 75% | 85% | 88% | Gemini |
| Cross-reference detection | 70% | 80% | 82% | Gemini |
| Legal document analysis | 78% | 85% | 80% | Claude |
| Financial statement parsing | 72% | 82% | 78% | Claude |
| Scientific paper comprehension | 80% | 82% | 85% | Gemini |
| Invoice/receipt extraction | 85% | 88% | 90% | Gemini |
Pattern: Claude dominates structured document tasks (tables, forms, legal analysis, financial statements) — native PDF processing preserves document structure that image conversion loses. Gemini leads on multi-page reasoning and cross-reference tasks — its 1M-token context handles long documents natively.
Audio Tasks (Models with Native Audio Support)
| Task | GPT-4o | Gemini 2.5 Pro | Gemini 2.5 Flash |
|---|---|---|---|
| Speech transcription (English) | 95% (WER 5%) | 94% (WER 6%) | 92% (WER 8%) |
| Speech transcription (multilingual) | 88% | 90% | 85% |
| Speaker identification | 75% | 80% | 72% |
| Sentiment from audio | 82% | 78% | 74% |
| Audio event detection | 70% | 78% | 72% |
| Meeting summarization | 85% | 88% | 80% |
| Music understanding | 60% | 72% | 65% |
Note: Claude does not natively process audio. For audio tasks with Claude, you need a separate transcription step (Whisper, Deepgram, AssemblyAI) before passing text to Claude. This adds latency and cost but can match or exceed native audio processing quality on transcription-dependent tasks.
Cost Comparison by Modality
Image Processing Cost
| Model | Cost per image (low res) | Cost per image (high res) | Tokens per image | 1,000 images cost |
|---|---|---|---|---|
| GPT-4o | $0.0017 | $0.0051-0.0255 | 85 (low) / 255-1,275 (high) | $1.70-25.50 |
| Claude Sonnet 4 | $0.0024 | $0.0048-0.0192 | 800 (small) / 1,600 (large) | $2.40-19.20 |
| Gemini 2.5 Pro | $0.0013 | $0.0013-0.0065 | 258 (all sizes) | $1.30-6.50 |
| Gemini 2.5 Flash | $0.00004 | $0.00004-0.0002 | 258 | $0.04-0.20 |
Cheapest for image processing: Gemini 2.5 Flash is 10-100x cheaper per image than GPT-4o or Claude. For high-volume image processing (OCR pipelines, document scanning), Flash’s cost advantage is enormous.
Document Processing Cost (100-page PDF)
| Model | Method | Input cost | Processing time | Notes |
|---|---|---|---|---|
| GPT-4o | Convert to images (100 pages) | $2.55-25.50 | 30-120s | High cost; OCR quality depends on image quality |
| Claude Sonnet 4 | Native PDF | $0.30-1.50 | 20-60s | Best table extraction; native structure preservation |
| Gemini 2.5 Pro | Native PDF | $0.13-0.65 | 15-45s | Cheapest with good quality; best for long docs |
| Gemini 2.5 Flash | Native PDF | $0.004-0.02 | 10-30s | Extremely cheap; adequate for simple extraction |
Audio Processing Cost
| Model | Cost per minute of audio | 1-hour meeting cost | Notes |
|---|---|---|---|
| GPT-4o (native) | ~$0.06 | ~$3.60 | Native audio input |
| Gemini 2.5 Pro (native) | ~$0.04 | ~$2.40 | Native audio input |
| Whisper API + Claude (text) | $0.006 + ~$0.02 | ~$1.56 | Cheaper but two-step pipeline |
| Deepgram + Claude (text) | $0.004 + ~$0.02 | ~$1.44 | Cheapest with good quality |
Latency Comparison
| Task | GPT-4o | Claude Sonnet 4 | Gemini 2.5 Pro | Gemini 2.5 Flash |
|---|---|---|---|---|
| Single image description | 2-5s | 2-4s | 1-3s | 0.5-2s |
| OCR (single page) | 3-6s | 2-5s | 1-4s | 0.5-2s |
| 10-page PDF extraction | 10-30s | 8-20s | 5-15s | 3-8s |
| 100-page PDF analysis | 60-180s | 30-90s | 20-60s | 10-30s |
| 1-minute audio | 5-15s | N/A (requires transcription) | 3-10s | 2-5s |
| 10-minute audio | 30-90s | N/A | 15-45s | 10-25s |
Gemini Flash is consistently the fastest across all modalities — purpose-built for throughput. For latency-sensitive pipelines (real-time document processing, live audio), Flash’s speed advantage compounds.
Model Selection Framework
By Task Type
| Task | First choice | Second choice | Avoid |
|---|---|---|---|
| OCR (printed text) | Gemini 2.5 Pro | GPT-4o | — |
| OCR (handwritten) | Gemini 2.5 Pro | GPT-4o | Claude (slightly lower accuracy) |
| Table extraction (PDF) | Claude Sonnet 4 | Gemini 2.5 Pro | GPT-4o (no native PDF) |
| Chart interpretation | Claude Sonnet 4 | GPT-4o | — |
| Image description | GPT-4o | Claude Sonnet 4 | — |
| Multi-page document analysis | Gemini 2.5 Pro | Claude Sonnet 4 | GPT-4o (expensive at scale) |
| Audio transcription | Whisper + any LLM | GPT-4o (native) | Claude (no native audio) |
| Meeting summarization | Gemini 2.5 Pro | GPT-4o | — |
| Video analysis | Gemini 2.5 Pro | — (only option for native) | GPT-4o, Claude (no video) |
| High-volume processing | Gemini 2.5 Flash | — | GPT-4o, Claude (too expensive) |
| Highest accuracy (any modality) | Task-dependent (see task table) | — | — |
By Constraint
| Constraint | Recommended model | Why |
|---|---|---|
| Lowest cost per document | Gemini 2.5 Flash | 10-100x cheaper than alternatives |
| Highest table extraction accuracy | Claude Sonnet 4 | Native PDF + superior structured content understanding |
| Native audio processing | GPT-4o or Gemini 2.5 Pro | Only models with native audio input |
| Longest document support | Gemini 2.5 Pro (1M tokens) | Handles 1,000+ page documents in single context |
| Fastest response time | Gemini 2.5 Flash | Consistently lowest latency across all modalities |
| Video understanding | Gemini 2.5 Pro | Only frontier model with native video input |
| Data privacy (no external API) | Open models (LLaVA, Qwen-VL) | Self-hosted multimodal models available |
How to Apply This
Use the token-counter tool to estimate token consumption for your multimodal inputs — image tokens vary significantly by resolution and model.
Match model to task, not brand. There is no single “best multimodal model.” Claude wins on document structure, Gemini wins on recognition and scale, GPT-4o wins on description quality. Choose per-task.
Use Gemini Flash for volume, frontier models for accuracy. A common architecture: Flash processes all documents, flags low-confidence results, frontier model re-processes only flagged items. This achieves 90%+ accuracy at Flash-level cost.
Test on your actual documents/images. Benchmark accuracy varies 10-20% depending on document quality, language, and domain. A model that scores 95% on clean OCR benchmarks may score 75% on your scanned PDFs with coffee stains.
Consider the transcription + LLM pipeline for audio. Dedicated transcription models (Whisper, Deepgram) matched with a text-focused LLM often outperform native audio models — and cost less.
Honest Limitations
Accuracy numbers are based on standardized benchmarks and published evaluations; your specific content (domain, quality, language) will produce different results. Model capabilities change with every update — accuracy comparisons have a shelf life of 3-6 months. Cost comparison assumes standard API pricing; enterprise agreements and volume discounts change the economics. Latency measurements reflect typical conditions; network latency, rate limits, and load affect real-world performance. Gemini’s video processing capability is powerful but context-limited — a 2-hour video may exceed practical processing limits for detailed analysis. Claude’s lack of native audio is a current limitation that may change. Open multimodal models (LLaVA, Qwen-VL) exist but trail frontier models by 10-15% on most tasks. Token counting for images varies by model and resolution — the costs shown are estimates that should be verified with actual API calls.
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