Multimodal Model Comparison 2026: GPT-4o vs Gemini vs Claude vs Llama Vision

A 512x512 image costs 170 tokens in GPT-4o, 258 tokens in Gemini 2.0, and 1,024 tokens in Llama Vision. The difference is architectural: GPT-4o uses a learned vision encoder that extracts compressed visual features, Gemini 2.0 uses native multimodal training where images are first-class tokens from pretraining, and Llama Vision uses CLIP patches with minimal compression. When you process 10,000 images per hour, the 6x token efficiency gap translates directly to compute cost and latency.

Architecture Overview

Each model takes a fundamentally different approach to multimodal fusion:

GPT-4o:     Early Fusion — visual tokens concatenated with text tokens
            All modalities share the same transformer backbone

Gemini 2.0: Early Fusion — native multimodal from pretraining
            Vision, audio, video encoded then interleaved with text

Claude 3.5: Cross-Attention — visual features injected at select layers
            Separate vision encoder, features attend into LLM layers

Llama 3.2:  Cross-Attention — adapter layers between ViT and LLM
            Open-weight, published architecture details

Why Architecture Matters for Inference

The fusion strategy determines:

1. Token count: Early fusion models consume context window with visual tokens. Cross-attention models keep visual features separate.
2. KV cache cost: Visual tokens in early fusion occupy KV cache entries. Cross-attention models store visual features in a separate cache.
3. Scaling with image count: Multi-image inputs scale linearly in context consumption for early fusion but can share encoder computation for cross-attention.

GPT-4o Architecture

GPT-4o (Omni) processes all modalities through a single transformer. Based on public information and inference behavior:

# GPT-4o multimodal processing (inferred architecture)
class GPT4oMultimodal:
    def __init__(self):
        # Vision encoder: proprietary, likely CLIP-variant
        self.vision_encoder = VisionEncoder(
            patch_size=14,
            resolution_tiers=[512, 768, 1024, 2048],
            output_dim=4096  # Projected to match LLM hidden dim
        )
        self.audio_encoder = WhisperVariant(output_dim=4096)
        self.transformer = GPT4Transformer(
            hidden_dim=4096,  # Estimated
            num_layers=120,   # Estimated for ~200B active params
            num_heads=64
        )

    def encode_image(self, image) -> torch.Tensor:
        # Dynamic resolution: higher res = more tokens
        tiles = self.tile_image(image)  # Split into 512x512 tiles
        tokens_per_tile = (512 // 14) ** 2  # ~1,369 tokens
        # Plus a low-res overview: 256 tokens

        visual_tokens = []
        for tile in tiles:
            features = self.vision_encoder(tile)
            visual_tokens.append(features)

        # Concatenate with separator tokens
        return torch.cat(visual_tokens, dim=0)

Token budget analysis for GPT-4o:

Gemini 2.0 Architecture

Gemini was designed as natively multimodal from pretraining, processing images, video, and audio alongside text:

# Gemini 2.0 multimodal processing (inferred from published papers)
class GeminiMultimodal:
    def __init__(self):
        # SigLIP-based vision encoder
        self.vision_encoder = SigLIPEncoder(
            variant="SO400M",
            output_dim=1152,
            num_registers=4  # Vision register tokens
        )
        self.vision_projector = nn.Linear(1152, 8192)

        # Audio: USM-based encoder
        self.audio_encoder = USMEncoder(output_dim=8192)

        # Video: frame sampling + temporal encoding
        self.video_processor = VideoFrameSampler(max_fps=1)

        # Core transformer: MoE
        self.transformer = GeminiTransformer(
            hidden_dim=8192,
            num_layers=64,   # Dense equivalent
            num_experts=16,
            active_experts=2,
            num_heads=64
        )

    def process_multimodal(self, text, images, video, audio):
        text_tokens = self.tokenize(text)

        visual_tokens = []
        for img in images:
            features = self.vision_encoder(img)
            projected = self.vision_projector(features)
            visual_tokens.extend(projected)

        if video is not None:
            frames = self.video_processor.sample(video)
            for frame in frames:
                features = self.vision_encoder(frame)
                visual_tokens.extend(self.vision_projector(features))

        audio_tokens = []
        if audio is not None:
            audio_tokens = self.audio_encoder(audio)

        # Interleave all modalities
        combined = self.interleave(text_tokens, visual_tokens, audio_tokens)
        return self.transformer(combined)

Gemini’s MoE backbone means visual tokens activate the same expert routing as text tokens. Different experts may specialize in visual vs textual processing, but the routing is learned during pretraining.

Gemini Token Efficiency

Gemini uses more aggressive token compression for images:

# Gemini's visual token compression
# Standard ViT: 224x224 / 14x14 = 256 tokens
# Gemini appears to use token merging:
# - Initial: 256 tokens per 224x224 patch
# - Merge redundant tokens: ~128 tokens per patch
# - For 1024x1024: 4 patches * 128 = 512 tokens

# Compared to GPT-4o for same 1024x1024 image:
# GPT-4o: ~5,732 tokens
# Gemini: ~512 tokens (estimated)
# 11x more token-efficient

Claude 3.5 Architecture

Claude uses a cross-attention architecture that keeps visual features separate from the main token sequence:

# Claude 3.5 multimodal (inferred from behavior and Anthropic research)
class ClaudeMultimodal:
    def __init__(self):
        self.vision_encoder = ViTLargeEncoder(
            patch_size=14,
            resolution=1024,
            output_dim=1024
        )
        self.vision_projector = MLPProjector(
            input_dim=1024,
            hidden_dim=4096,
            output_dim=8192  # Match LLM hidden dim
        )

        self.transformer = ClaudeTransformer(
            hidden_dim=8192,
            num_layers=80,  # Estimated
            num_heads=64,
            # Cross-attention layers at every 4th layer
            cross_attn_interval=4,
            cross_attn_heads=16
        )

    def forward(self, text_tokens, images):
        # Encode images separately
        visual_features = []
        for img in images:
            features = self.vision_encoder(img)
            projected = self.vision_projector(features)
            visual_features.append(projected)

        # Stack visual features
        visual_context = torch.cat(visual_features, dim=0)
        # Shape: [total_visual_tokens, hidden_dim]

        # Text tokens go through main transformer
        # Every 4th layer has cross-attention to visual_context
        hidden = self.transformer.embed(text_tokens)

        for i, layer in enumerate(self.transformer.layers):
            hidden = layer.self_attention(hidden)

            if i % 4 == 3:  # Cross-attention every 4 layers
                hidden = layer.cross_attention(
                    query=hidden,
                    key=visual_context,
                    value=visual_context
                )

            hidden = layer.mlp(hidden)

        return hidden

The cross-attention design means visual tokens do NOT consume positions in the context window. A 1024x1024 image generates visual features that are accessed via cross-attention, but the 200K token context remains fully available for text.

Llama 3.2 Vision Architecture

Llama 3.2 Vision is the only model with published architecture details:

# Llama 3.2 Vision - exact architecture from Meta's paper
class Llama32Vision:
    def __init__(self):
        # Vision encoder: ViT-H/14
        self.vision_encoder = ViTHuge(
            patch_size=14,
            image_size=560,      # 560x560 input
            hidden_dim=1280,
            num_layers=32,
            num_heads=16
        )
        # Cross-attention projector
        self.vision_projector = nn.Sequential(
            nn.Linear(1280, 4096),
            nn.GELU(),
            nn.Linear(4096, 4096)
        )

        # Core LLM with cross-attention adapters
        # 11B and 90B variants
        self.llm = LlamaTransformer(
            hidden_dim=4096,     # 11B variant
            num_layers=32,
            num_heads=32,
            # Cross-attention inserted at layers [3, 8, 13, 18, 23, 28]
            cross_attn_layers=[3, 8, 13, 18, 23, 28]
        )

    def forward(self, text_input_ids, images):
        # Encode image
        # 560/14 = 40, so 40*40 = 1600 patches
        visual_features = self.vision_encoder(images)
        # Shape: [batch, 1600, 1280]

        visual_projected = self.vision_projector(visual_features)
        # Shape: [batch, 1600, 4096]

        # LLM forward with cross-attention
        hidden = self.llm.embed(text_input_ids)

        for i, layer in enumerate(self.llm.layers):
            hidden = layer.self_attention(hidden)

            if i in self.llm.cross_attn_layers:
                hidden = layer.cross_attention(
                    query=hidden,
                    key_value=visual_projected
                )

            hidden = layer.mlp(hidden)

        return self.llm.lm_head(hidden)

Inference Cost Comparison

The architectural differences translate directly to inference costs:

# Cost model for multimodal inference
def compute_multimodal_cost(
    model_type: str,
    text_tokens: int,
    visual_tokens: int,
    output_tokens: int,
    num_layers: int,
    hidden_dim: int,
    num_kv_heads: int,
    head_dim: int,
    cross_attn_layers: int = 0
) -> dict:
    # KV cache per token per layer (bytes, FP16)
    kv_per_token = num_kv_heads * head_dim * 2 * 2  # K+V

    if model_type == "early_fusion":
        # Visual tokens are in KV cache
        total_kv_tokens = text_tokens + visual_tokens + output_tokens
        kv_cache_bytes = total_kv_tokens * kv_per_token * num_layers

        # Prefill FLOPs: all tokens through all layers
        prefill_tokens = text_tokens + visual_tokens
        prefill_flops = prefill_tokens * num_layers * hidden_dim * 12

        # Decode FLOPs per step: attention over all cached tokens
        decode_attn_flops = (total_kv_tokens * num_layers *
                            hidden_dim * 4)

    elif model_type == "cross_attention":
        # Visual tokens NOT in self-attention KV cache
        self_attn_tokens = text_tokens + output_tokens
        kv_cache_bytes = self_attn_tokens * kv_per_token * num_layers

        # Plus cross-attention KV cache (only at cross-attn layers)
        cross_kv = visual_tokens * kv_per_token * cross_attn_layers
        kv_cache_bytes += cross_kv

        # Prefill: text through all layers + cross-attn
        prefill_flops = text_tokens * num_layers * hidden_dim * 12
        prefill_flops += (text_tokens * visual_tokens *
                         cross_attn_layers * hidden_dim * 4)

        # Decode: self-attention + cross-attention
        decode_attn_flops = (self_attn_tokens * num_layers *
                            hidden_dim * 4)
        decode_attn_flops += (visual_tokens * cross_attn_layers *
                             hidden_dim * 4)

    return {
        "kv_cache_mb": kv_cache_bytes / 1e6,
        "prefill_tflops": prefill_flops / 1e12,
        "decode_attn_gflops": decode_attn_flops / 1e9
    }

Video Understanding Comparison

Video extends the token count problem:

Gemini’s 2M context window and aggressive token compression make it the most capable for long video understanding. GPT-4o’s early fusion means a 30-second video at full resolution consumes nearly 40% of the context window.

Audio Processing Comparison

# Audio modality support as of 2026
audio_support = {
    "GPT-4o": {
        "input": True,
        "output": True,   # Can generate speech
        "encoder": "Whisper-variant",
        "native": True,    # Audio tokens in same transformer
        "latency": "real-time capable"
    },
    "Gemini 2.0": {
        "input": True,
        "output": True,   # Can generate speech
        "encoder": "USM-based",
        "native": True,
        "latency": "real-time capable"
    },
    "Claude 3.5": {
        "input": False,    # No native audio input
        "output": False,
        "encoder": None,
        "native": False,
        "latency": "N/A"
    },
    "Llama 3.2": {
        "input": False,    # Vision only
        "output": False,
        "encoder": None,
        "native": False,
        "latency": "N/A"
    }
}

Practical Serving Cost Comparison

For a production deployment processing 1 million multimodal requests per day:

Architectural Trade-offs Summary

Early Fusion (GPT-4o, Gemini):
  + Unified representation: model can jointly reason across modalities
  + Simplest architecture: one transformer for everything
  - Visual tokens consume context window
  - KV cache grows with visual input
  - Expensive for multi-image / video inputs

Cross-Attention (Claude, Llama):
  + Visual features don't consume context window
  + KV cache for self-attention is text-only sized
  + Efficient for dense visual inputs (many images)
  - Separate encoder adds latency during prefill
  - Cross-attention layers add parameters
  - Visual reasoning limited to cross-attention capacity

MoE + Early Fusion (Gemini):
  + Token compression reduces visual token count dramatically
  + Massive context window (2M) handles any input combination
  + Experts can specialize per modality
  - Requires enormous compute budget for pretraining
  - MoE routing adds complexity for serving

Summary

Frontier multimodal models in 2026 split into two architectural camps: early fusion (GPT-4o, Gemini) and cross-attention (Claude, Llama). Early fusion concatenates visual tokens with text in a single transformer, providing unified reasoning but consuming context window proportionally — GPT-4o uses approximately 5,700 tokens per 1024x1024 image while Gemini compresses to approximately 512 through token merging. Cross-attention keeps visual features in a separate cache accessed at specific transformer layers, preserving the full text context window but adding cross-attention compute at 6-20 layers. For inference cost, Gemini’s aggressive compression and 2M context make it the most efficient for multi-image and video workloads. For self-hosted deployments, Llama 3.2 Vision’s published architecture enables optimized serving at 5-50x lower cost than API pricing, with 1,600 visual tokens per image through its cross-attention design.