Gemini Embedding 2 vs CLIP: Which Multimodal Embedding Model Should I Choose?

Mar 26, 2026

Cowrie

Dev @ Bswen

The Problem

I was building an image search feature for my application. Users wanted to search for products using natural language queries like “red sneakers for running” or find visually similar images by uploading a photo.

I knew I needed multimodal embeddings - a way to represent both images and text in the same vector space so they could be compared. But I got stuck at the first decision point:

Should I use CLIP or try the new Gemini Embedding 2?

CLIP has been around since 2021 and I’ve used it before. But I kept seeing announcements about Gemini Embedding 2 being “natively multimodal.” Was the newer model worth switching to?

What I Thought I Knew

I initially assumed newer meant better. I thought:

• Gemini Embedding 2 must be superior because it’s from Google and released in 2024
• CLIP is old (2021) and probably outdated
• A “unified architecture” sounds better than “separate encoders”

But when I started researching, I found out the reality is more nuanced.

The Reddit Thread That Changed My Perspective

I found a discussion on r/MachineLearning where someone asked about Gemini Embedding 2. The top comment threw me off:

“We already had multimodal embeddings for… quite a while though.”

Wait, what? If multimodal embeddings aren’t new, what makes Gemini Embedding 2 different?

Another user clarified:

“Yes, there’s a bit of nuance but the general idea of multimodal embeddings is that it covers multiple modalities right? We’ve had that for quite a while (look up CCA, that’s >20 years old already!). Then there’s Siamese networks, CLIP and the entirety of deep learning that comes into the frame.”

The OP then clarified what I had missed:

“It’s the first Google natively multimodal embedding model.”

So Gemini Embedding 2 isn’t the first multimodal embedding - it’s Google’s first native multimodal embedding model. That’s a crucial distinction I needed to understand.

The Architecture Difference

Once I understood the distinction, I dug into the architectures.

CLIP: Separate Encoders, Joint Space

CLIP uses two separate neural networks - one for images (ViT) and one for text (Transformer). They’re trained together with contrastive loss to align their outputs in a shared embedding space.

CLIP Architecture

Image Encoder (ViT) ──────┐
                          ├──> Joint Embedding Space
Text Encoder (Transformer)┘

This approach works well. I’ve used CLIP for image-text similarity tasks and it reliably finds semantic matches.

Gemini Embedding 2: Unified Encoder

Gemini Embedding 2 uses a single unified model that processes all modalities - text, images, and audio - through the same architecture.

Gemini Embedding 2 Architecture

Unified Multimodal Encoder
├── Image Input ────┐
├── Text Input ─────┼──> Shared Embedding Space
├── Audio Input ────┘

The key insight I got from the Reddit discussion:

“What Gemini does is encoding it into a shared space. This is a truly different architecture, but when looking at applications not really that different from the classical network that has graphs for each modality that embed first and later get joined in a single space.”

So in practice, for many applications, the difference may be subtle. But the unified approach could potentially capture deeper cross-modal relationships.

My Comparison Table

After researching, I built this comparison for my own decision-making:

Aspect	CLIP	Gemini Embedding 2
Architecture	Separate encoders	Unified encoder
Training	Contrastive learning on image-text pairs	Multimodal native training
Modalities	Image, Text	Image, Text, Audio
Embedding Space	Joint space via contrastive alignment	Natively shared space
Self-hostable	Yes (open weights)	No (API only)
Cost	Free for self-hosted	Pay per API call
Offline use	Yes	No

The last three rows became the deciding factors for my project.

Code Examples: What I Tried

I tested both approaches to see how they differ in practice.

Using CLIP for Image-Text Search

First, I tried the familiar CLIP approach:

clip_search.py

import torch
from transformers import CLIPProcessor, CLIPModel
from PIL import Image

# Load CLIP model (runs locally, no API key needed)
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")

# Compare image with text candidates
image = Image.open("product.jpg")
texts = ["a photo of a dog", "a photo of a cat", "a product photo"]

inputs = processor(text=texts, images=image, return_tensors="pt", padding=True)
outputs = model(**inputs)

# Get similarity scores
logits_per_image = outputs.logits_per_image
probs = logits_per_image.softmax(dim=1)

print(f"Most similar text: {texts[probs.argmax()]}")
print(f"Probabilities: {probs}")

CLIP worked immediately. I liked that I could run it entirely offline after downloading the model weights.

Using Gemini Embedding 2

Then I tried Gemini Embedding 2:

gemini_embedding.py

import google.generativeai as genai
from PIL import Image

# Configure API key
genai.configure(api_key="YOUR_API_KEY")

# Embed text
text_embedding = genai.embed_content(
    model="models/text-embedding-004",
    content="A sunset over mountains",
    task_type="retrieval_document"
)

# Embed image
image = Image.open("landscape.jpg")
image_embedding = genai.embed_content(
    model="models/gemini-embedding-2",
    content=image,
    task_type="retrieval_document"
)

# Calculate similarity
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

similarity = cosine_similarity(
    [text_embedding["embedding"]],
    [image_embedding["embedding"]]
)[0][0]

print(f"Text-Image similarity: {similarity}")

The API was straightforward, but I noticed two things:

1. I needed an internet connection and API key
2. Each call cost money (though the pricing is reasonable)

Building a Multimodal Search System

I built a simple abstraction to compare both approaches:

multimodal_search.py

import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from abc import ABC, abstractmethod

class MultimodalSearch(ABC):
    def __init__(self):
        self.embeddings = []
        self.metadata = []

    def index_image(self, image_path, metadata=None):
        """Index an image with optional metadata"""
        embedding = self._get_image_embedding(image_path)
        self.embeddings.append(embedding)
        self.metadata.append(metadata or {"path": image_path})

    def search_by_text(self, query_text, top_k=5):
        """Search indexed images using text query"""
        query_embedding = self._get_text_embedding(query_text)
        similarities = cosine_similarity([query_embedding], self.embeddings)[0]
        top_indices = np.argsort(similarities)[-top_k:][::-1]
        return [(self.metadata[i], similarities[i]) for i in top_indices]

    @abstractmethod
    def _get_image_embedding(self, image_path):
        pass

    @abstractmethod
    def _get_text_embedding(self, text):
        pass

This let me swap between CLIP and Gemini Embedding 2 without changing my application code.

What I Got Wrong

I made several mistakes in my initial evaluation:

Mistake 1: Assuming “native multimodal” means better quality

The unified architecture sounds impressive, but I couldn’t find benchmarks showing it’s significantly better than CLIP for my use case (image-text search). The architecture difference matters more for some applications than others.

Mistake 2: Ignoring deployment constraints

I initially focused only on embedding quality. But for my application:

• Running on edge devices? CLIP wins (can run offline)
• Processing sensitive data? CLIP wins (no API calls)
• Need audio support? Gemini Embedding 2 wins

Mistake 3: Not benchmarking on my actual data

I should have tested both on my specific images and queries. Generic benchmarks don’t capture how well a model works for your particular domain.

Mistake 4: Overlooking rate limits

Gemini Embedding 2 has API rate limits. For batch processing millions of images, CLIP’s unlimited self-hosted approach made more sense.

When to Use Each

Based on my research and testing, here’s when I’d choose each:

Use CLIP When:

1. You need offline processing - CLIP runs entirely locally after downloading weights
2. You have strict data privacy requirements - No data leaves your servers
3. You’re processing at scale - No per-call costs, no rate limits
4. You need predictable latency - No network calls means consistent response times
5. You’re already in the Hugging Face ecosystem - Easy integration with existing pipelines

Use Gemini Embedding 2 When:

1. You need audio embeddings - CLIP only handles image and text
2. You’re building within Google Cloud - Native integration with other Google AI services
3. You want simpler setup - No model management, just API calls
4. Your data is already in Google Cloud - Reduced data transfer costs
5. You need Google’s multimodal understanding - Potentially better for complex cross-modal queries

The Decision Framework I Used

I created this simple decision tree for my project:

Decision Framework

Need audio embeddings?
│
├── YES ──> Gemini Embedding 2
│
└── NO
    │
    Offline/edge deployment needed?
    │
    ├── YES ──> CLIP
    │
    └── NO
        │
        Sensitive data (can't send to API)?
        │
        ├── YES ──> CLIP
        │
        └── NO
            │
            Already using Google Cloud?
            │
            ├── YES ──> Gemini Embedding 2
            │
            └── NO ──> CLIP (simpler, free, proven)

What I Chose

For my image search application, I went with CLIP because:

• I need to process images offline on edge devices
• I want predictable latency without network calls
• I have no audio requirements
• I’m cost-sensitive and processing millions of images

But I’m keeping Gemini Embedding 2 in mind for future projects that might need audio or where I’m already invested in Google’s ecosystem.

Summary

In this post, I compared Gemini Embedding 2 and CLIP for multimodal embeddings. The key insight is that “native multimodal” doesn’t automatically mean better - it depends on your use case.

CLIP remains a solid choice for image-text tasks with proven reliability, offline capability, and zero marginal cost at scale. Gemini Embedding 2 offers audio support and simpler API-based integration, but requires network access and per-call costs.

The best approach is to benchmark both on your actual data before committing. Architecture differences matter less than how the model performs on your specific task.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

• 👨‍💻 Google AI Gemini Embedding Documentation
• 👨‍💻 OpenAI CLIP Paper
• 👨‍💻 Reddit Discussion on Multimodal Embeddings
• 👨‍💻 Hugging Face CLIP Models

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!

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Tags:

• llm
• ai
• career

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