DeepSeek V3/R1 vs V4 Architecture Comparison Infographic AI Image Prompt

A side-by-side AI architecture comparison infographic. Style: clean technical diagram, white background, thin black outlines, rounded rectangles, dashed callout boxes, color-coded highlights, presentation-slide aesthetic, vector infographic. Canvas: aspect ratio 2:1, wide horizontal resolution. Title row: left title "DeepSeek V3/R1 (671 billion)" in bright orange-red, right title "DeepSeek V4 (1.2 trillion)" in bright blue. Layout: 2 columns, sections: left half "DeepSeek V3/R1 (671 billion)" with 9 labels: Vocabulary size of 129k, FeedForward (SwiGLU) module, Intermediate hidden layer dimension of 2,048, MoE layer, Supported context length of 128k tokens, First 3 blocks use dense FFN with hidden size 18,432 instead of MoE, Sample input text, Embedding dimension of 7,168, 128 heads; right half "DeepSeek V4 (1.2 trillion)" with 9 labels: Vocabulary size of 160k, FeedForward (SwiGLU) module, Intermediate hidden layer dimension of 3,072, MoE layer, Supported context length of 256k tokens, First 3 blocks use dense FFN with hidden size 24,576 instead of MoE, Sample input text, Embedding dimension of 8,192, 128 heads; bottom comparison table full width with 10 labels: Total parameters, Active parameters per token, Hidden size, Esmple dimesiegn, DeepSeek V3/R1, Intermediate (FF), Attention heads, Context length, Embedding dimension, Vocabulary size. Left panel: background very light gray rounded rectangle, main stack 8 blocks: Tokenized text, Token embedding layer, RMSNorm 1, Multi-head Latent Attention, RMSNorm 2, MoE, Final RMSNorm, Linear output layer; side module RoPE attached to attention block on left; attention block label "Multi-head Latent Attention" with orange-red accent on "Latent"; feedforward inset title "FeedForward (SwiGLU) module" with 4 blocks: Linear layer, SiLU activation, Linear layer, Linear layer, diagram two branches multiplied then projected; MoE inset title "MoE layer" with 5 blocks: top combine node, Feed forward, Feed forward, Router, expert count badge 256, details small black square with 1 selected expert, arrows routing upward to experts, dotted divider line; annotations: vocab 129k, ff_dim 2,048, context 128k tokens, dense_first_blocks first 3 blocks use dense FFN hidden size 18,432, resource_savings model size 671B but only 1 shared+8 experts active per token, only 37B active per inference step; bottom stats 10 items: Total parameters 671B, Active parameters per token 37B (1+8 experts), Hidden size 7,128, Esmple dimesiegn 28,432, Intermediate (FF) 2,048, Attention heads 128, Context length 128k, Embedding dimension first 3 blocks, Context ler length 22G7, Vocabulary size 129k. Right panel: background very light blue rounded rectangle, main stack 8 blocks same as left; side module RoPE attached to attention block on left; attention block label "Multi-head Latent Attention" with blue accent on "Latent"; feedforward inset structure same as left; MoE inset title "MoE layer" with 5 blocks: top combine node, Feed forward, Feed forward, Router, expert count badge 384, details blue border emphasis; annotations: vocab 160k, ff_dim 3,072, context 256k tokens, dense_first_blocks first 3 blocks use dense FFN hidden size 24,576, resource_savings model size 1.2T but only 1 shared+8 experts active per token, only 52B active per inference step; bottom stats 10 items: Total parameters 1.2T, Active parameters per token 52B (1+8 experts), Hidden size 7,2B, Esmple dimesiegn 28,432, Intermediate (FF) 3,072, Attention heads 128, Context length 256k, Embedding dimension first 3 blocks, Context ler length 22G7, Vocabulary size 160k. Global notes: Create a highly detailed transformer architecture comparison diagram with mirrored layouts. Each half contains one large model stack diagram plus 2 inset diagrams: 1 feedforward module and 1 MoE layer. Use arrows between blocks, tiny technical labels, and connector lines from labels to the relevant components. Keep the typography dense and slide-like, with orange-red used for all V3/R1 emphasis and blue used for all V4 emphasis. Include a small bottom row of compact tabular metrics spanning the width. Preserve the slightly imperfect, human-made infographic look with very small text and crowded annotations.