Reranking vs larger embedding model comparison
Quick answer
Use
reranking to improve search precision by reordering top candidates with a specialized model, while larger embedding models generate more accurate vector representations upfront. Reranking is cost-effective for large datasets; larger embeddings provide better semantic quality but at higher compute cost.VERDICT
Use
reranking for efficient, scalable search refinement; use larger embedding models when semantic accuracy outweighs cost and latency.| Approach | Model size | Latency | Cost | Best for | API access |
|---|---|---|---|---|---|
| Reranking | Small to medium specialized models | Moderate (two-step) | Lower overall | Improving top results precision | Widely available |
| Larger embedding model | Large, high-capacity models | Higher (single-step) | Higher per query | High semantic accuracy in embeddings | Widely available |
| Reranking | Uses cross-encoder or interaction models | Slower per query due to reprocessing | Cost-effective at scale | When initial retrieval is noisy | OpenAI, Anthropic, others |
| Larger embedding model | Uses large bi-encoder models | Faster single pass | Higher embedding generation cost | When embedding quality is critical | OpenAI, Google Vertex AI, others |
Key differences
Reranking applies a second-stage model to reorder a shortlist of candidates, improving precision by leveraging cross-attention between query and documents. Larger embedding models generate higher-quality vector representations upfront, reducing the need for reranking but increasing embedding computation cost and latency.
Reranking is a two-step process: first retrieve candidates with a smaller embedding model, then rerank with a more expensive model. Larger embedding models do everything in one step but require more compute and memory.
Side-by-side example: reranking
This example uses OpenAI's text-embedding-3-small for initial retrieval and gpt-4o-mini for reranking the top 3 candidates.
import os
from openai import OpenAI
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
query = "Explain the benefits of reranking in search."
# Step 1: Generate embeddings for query and documents
documents = [
"Reranking improves search precision by reordering results.",
"Larger embedding models produce better vectors upfront.",
"Embedding cost increases with model size.",
"Reranking uses cross-encoders for better context.",
]
query_embedding_resp = client.embeddings.create(
model="text-embedding-3-small",
input=query
)
query_embedding = query_embedding_resp.data[0].embedding
# Dummy function to simulate similarity search
# In practice, use a vector DB like Pinecone or FAISS
import numpy as np
def cosine_similarity(a, b):
return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
# Generate embeddings for documents
doc_embeddings = []
for doc in documents:
resp = client.embeddings.create(model="text-embedding-3-small", input=doc)
doc_embeddings.append(resp.data[0].embedding)
# Retrieve top 3 candidates by similarity
similarities = [cosine_similarity(query_embedding, d) for d in doc_embeddings]
top_indices = np.argsort(similarities)[-3:][::-1]
top_docs = [documents[i] for i in top_indices]
# Step 2: Rerank top candidates with cross-encoder (chat completion)
rerank_prompt = "Rerank these documents by relevance to the query:\nQuery: {}\nDocuments:\n".format(query)
for i, doc in enumerate(top_docs, 1):
rerank_prompt += f"{i}. {doc}\n"
rerank_prompt += "\nProvide the order as a list of numbers, most relevant first."
response = client.chat.completions.create(
model="gpt-4o-mini",
messages=[{"role": "user", "content": rerank_prompt}]
)
print("Reranking result:", response.choices[0].message.content) output
Reranking result: [1, 4, 2]
Larger embedding model equivalent
This example uses a larger embedding model text-embedding-3-large to generate higher-quality embeddings for direct retrieval without reranking.
import os
from openai import OpenAI
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
query = "Explain the benefits of reranking in search."
documents = [
"Reranking improves search precision by reordering results.",
"Larger embedding models produce better vectors upfront.",
"Embedding cost increases with model size.",
"Reranking uses cross-encoders for better context.",
]
query_embedding_resp = client.embeddings.create(
model="text-embedding-3-large",
input=query
)
query_embedding = query_embedding_resp.data[0].embedding
import numpy as np
def cosine_similarity(a, b):
return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
# Generate embeddings for documents
# Using larger model for better quality
doc_embeddings = []
for doc in documents:
resp = client.embeddings.create(model="text-embedding-3-large", input=doc)
doc_embeddings.append(resp.data[0].embedding)
# Retrieve top 3 candidates by similarity
similarities = [cosine_similarity(query_embedding, d) for d in doc_embeddings]
top_indices = np.argsort(similarities)[-3:][::-1]
top_docs = [documents[i] for i in top_indices]
print("Top documents by larger embedding model:", top_docs) output
Top documents by larger embedding model: ['Reranking improves search precision by reordering results.', 'Reranking uses cross-encoders for better context.', 'Larger embedding models produce better vectors upfront.']
When to use each
Reranking is best when you have a large candidate set and want to improve precision cost-effectively by applying a more expensive model only on top results. Larger embedding models suit scenarios where embedding quality is critical and latency or cost is less constrained.
Use cases:
- Reranking: E-commerce search, question answering with noisy retrieval, multi-stage pipelines.
- Larger embeddings: Semantic search with small to medium datasets, knowledge base indexing, when embedding quality drives downstream tasks.
| Use case | Recommended approach | Reason |
|---|---|---|
| Large-scale search with many candidates | Reranking | Cost-effective precision improvement on top results |
| High semantic accuracy needed upfront | Larger embedding model | Better embeddings reduce need for reranking |
| Latency-sensitive applications | Larger embedding model | Single-step retrieval is faster |
| Budget-constrained projects | Reranking | Lower overall compute cost by limiting expensive calls |
Pricing and access
Pricing varies by provider and model size. Reranking uses smaller embedding models plus a more expensive reranker on fewer items, often reducing total cost. Larger embedding models incur higher cost per embedding but avoid second-stage calls.
| Option | Free tier | Paid | API access |
|---|---|---|---|
| Reranking (small embedding + cross-encoder) | Yes (limited usage) | Lower total cost | OpenAI, Anthropic, Google Vertex AI |
| Larger embedding model | Yes (limited usage) | Higher cost per query | OpenAI, Google Vertex AI, Cohere |
| Vector DB integration | Depends on provider | Varies | Pinecone, Weaviate, FAISS (self-hosted) |
| Reranking with custom models | No | Depends on hosting | Self-hosted or cloud |
Key Takeaways
- Use reranking to improve precision efficiently by applying expensive models only on top candidates.
- Larger embedding models provide better semantic quality but increase embedding computation cost and latency.
- Choose reranking for large datasets and budget constraints; choose larger embeddings for accuracy and latency-sensitive use cases.