Multimodal input processing in AI chatbots (ChatGPT, Claude, Gemini...): text, image, audio, video

How ChatGPT, Claude, and Gemini integrate and interpret different data types within transformer-based architectures.

Modern AI chatbots are evolving beyond text-only interaction, becoming multimodal systems capable of interpreting and generating insights from images, audio, video, and structured documents. While ChatGPT, Claude, and Gemini all support multimodal input, they differ significantly in how these capabilities are implemented at the architectural level, the latency-performance trade-offs, and the depth of reasoning possible across multiple data formats.

Here we explore the technical foundations behind multimodal processing in the leading chatbot architectures, analyzing their fusion strategies, inference pipelines, and real-world limitations.

Multimodal AI integrates multiple data streams into unified representations.

Text, images, audio, and video are mapped into shared latent spaces where the model performs cross-modal reasoning.

In traditional large language models (LLMs), text sequences are tokenized and processed exclusively through transformer layers. By contrast, multimodal models extend this by adding specialized encoders for non-text data:

Input Type	Processing Mechanism	Output to Transformer
Text	Tokenizer + embedding layers	Vector embeddings
Images	Vision Transformer (ViT) or CLIP-like encoders	Visual embeddings aligned to text
Audio	Spectrogram-based convolution + transformer fusion	Temporal embeddings
Video	Sequential frame encoding + spatiotemporal attention	Combined video-language latent vectors

Once encoded, all inputs are projected into a shared multimodal latent space where cross-attention allows tokens from different modalities to interact, enabling reasoning over complex inputs like “Summarize this 30-page PDF, highlight financial anomalies, and describe this embedded chart.”

OpenAI ChatGPT integrates multimodality natively in GPT-4o and GPT-5.

A unified transformer backbone processes text, images, and audio without separate inference pipelines.

GPT-4o introduced real-time multimodal capabilities, allowing users to upload PDFs, images, and spreadsheets and receive contextual insights. Unlike older models where vision modules were bolted onto text transformers, GPT-4o integrates multimodal encoders directly into its dense transformer stack.

Key design features:

• Vision-text unification: Uses shared latent embeddings for seamless reasoning across graphs, documents, and natural images.

• Low-latency audio pipelines: Converts speech to embeddings directly in transformer-attention space.

• Direct video frame parsing: While limited, GPT-5 extends real-time extraction from video streams into temporal embeddings.

• Streaming multimodal reasoning: Simultaneous token and pixel interpretation during inference.

Feature	GPT-4o	GPT-5
Multimodal Inputs	Text, images, audio	Text, images, audio, video (beta)
Latency Profile	Optimized for live response	Improved pipeline parallelism
Image Parsing	Native vector alignment	Higher-resolution segmentation
Video Processing	Experimental	Integrated attention layers
Speech Support	Yes, fully bidirectional	Expanded contextual voice memory

In GPT-5, multimodal alignment accuracy improved, enabling complex workflows like reading scanned spreadsheets, describing embedded graphs, and reconciling extracted numerical data into structured outputs.

Claude uses staged multimodal reasoning for structured document comprehension.

While less optimized for video and real-time vision, Claude excels at extracting structured meaning from dense documents.

Anthropic’s Claude models introduced multimodal input support gradually, focusing initially on text + image integration in Claude 3 Sonnet and later expanding capabilities in Claude Opus. Unlike OpenAI’s fully unified pipeline, Claude applies a staged attention architecture:

1. Primary image-text encoder: Maps visual features into a latent space aligned with tokens.

2. Hierarchical cross-attention: Combines extracted visual embeddings with the full conversational context.

3. Reflective validation layers: Claude evaluates multiple interpretation pathways to improve accuracy.

Claude’s focus remains on complex PDF and spreadsheet reasoning, especially for cases where numbers, tables, and embedded figures must be analyzed precisely.

Claude Model	Multimodal Inputs	Processing Pipeline	Strengths
Claude 3 Sonnet	Text, images	Single-stage alignment	Document snapshots, diagrams
Claude 3 Opus	Text, images, partial tables	Hierarchical visual parsing	Advanced PDF analysis
Claude 4.1 Opus	Text, images, OCR layers	Reflective vision integration	Better table-cell extraction

Unlike GPT-4o, Claude has no live audio/video support today but delivers superior structured data interpretation when processing financial statements or long-form legal filings.

Gemini integrates deep multimodal grounding through unified vision-language transformers.

Google’s Gemini 2.5 models natively combine visual, textual, and auditory processing inside a single inference graph.

Gemini represents the most architecturally advanced multimodal implementation among current AI chatbots. It uses joint vision-language transformers rather than separate encoders, allowing direct cross-modal tokenization where image patches, speech spectrograms, and text sequences coexist inside the same latent representation.

Technical differentiators:

• Frame-sequenced attention: Interprets video as structured event timelines.

• Grounding integration: Real-time cross-referencing via Google Search APIs for contextual enhancement.

• Live spatial reasoning: Generates structured representations of layouts and embedded objects.

• Streaming multimodal fusion: Optimized for documents with high-density graphs and mixed media.

Gemini Model	Multimodal Scope	Vision Processing	Audio + Video Support	Grounding Capabilities
Gemini 1.5 Pro	Text, images	Early fusion layers	No native video/audio	Yes, via Google Search
Gemini 2.5 Flash	Text, images, PDFs	Latency-optimized encoding	Limited audio	Contextual enrichment
Gemini 2.5 Pro	Text, images, audio, video	Joint transformer embeddings	Yes, real-time	Full deep integration

Gemini’s combination of joint latent spaces and retrieval-augmented grounding makes it highly effective for enterprise applications where structured visuals, conversational insights, and contextual accuracy must merge seamlessly.

Performance comparison of multimodal capabilities.

Feature	ChatGPT (GPT-4o / GPT-5)	Claude 3.5 / 4.1	Gemini 2.5 Pro
Architecture	Dense unified multimodal	Staged cross-attention	Joint vision-language transformers
Text Processing	Native	Native	Native
Image Parsing	High accuracy, fast	Precise for structured layouts	Optimized for mixed media
Audio Support	Yes, fully integrated	No	Yes, streaming-ready
Video Support	Beta in GPT-5	Not supported	Full sequence parsing
Grounding	Limited tool calling	Not natively integrated	Native Google grounding
Best For	General multimodal workflows	Document + table parsing	Complex cross-modal analytics

Key differences in multimodal engineering strategies.

OpenAI focuses on unification, Claude on structured precision, and Gemini on deep integration.

• ChatGPT aims for universal multimodality with low latency across everyday use cases, emphasizing real-time streaming.

• Claude focuses on document intelligence, optimizing visual parsing for PDFs, tables, and structured layouts.

• Gemini uses a fully unified multimodal backbone with native grounding, designed for complex enterprise scenarios involving high-density mixed media.

These divergent strategies explain why ChatGPT performs best in live workflows, Claude leads in structured comprehension, and Gemini dominates cross-modal analytics.

____________

FOLLOW US FOR MORE.

DATA STUDIOS

datastudios.org