Early Fusion vs. Late Fusion: How Claude 4.5, Llama 4, and Gemini 3 process text and vision tokens together in the LLM for true cross-modal reasoning.
Step 5/5 in Chapter 2 "Modern Architecture Variants"
Technical details of Early Fusion. Shows how vision tokens are created and projected into the LLM space.
A 1024×1024 image = ~1000-4000 vision tokens (depending on patch size and compression). Understanding how many tokens an image costs is critical for context management.
Traditional multimodal models process vision and text sequentially: Image encoder → Feature vector → LLM → Text. Early Fusion breaks this paradigm: Vision and text tokens are processed together in the LLM, enabling true cross-modal attention.
Tokens are processed interleaved:
↓ Shared Attention & FFN Layers ↓
Key: Vision tokens receive attention from text tokens and vice versa. No special cross-modal layers needed.
True attention between all modalities. The model understands spatial relationships between text and image natively.
Single pass instead of separate encoders. 2-3× latency reduction compared to Late Fusion.
Efficient vision tokenization + adaptive sparsity. 1M context practically possible.
No special cross-modal layers. All information through same attention. Elegant + Powerful.
Claude 4.5 > GPT-4o. Early Fusion scales better with model size.
Text + Image + Audio = Single token stream. Full multimedia reasoning next year.