Voyage Finance 2 vs OpenAI Embeddings Cost Calculator (2026)

# financial_embeddings.py - High-precision retrieval for SEC 10-K filings
import voyageai
from openai import OpenAI

vo = voyageai.Client(api_key="VOYAGE_API_KEY")
oai = OpenAI(api_key="OPENAI_API_KEY")

sample_filing_text = [
    "In Q3 2025, operating cash flow expanded by $450M driven by deferred software revenue amortization.",
    "Non-GAAP adjusted EBITDA margin improved by 340 bps year-over-year to 28.4%."
]

# 1. Voyage Finance-2: Compact 1024-dimension vector fine-tuned on accounting syntax ($0.12/M)
voyage_res = vo.embed(sample_filing_text, model="voyage-finance-2", input_type="document")
print(f"Voyage Vector Dimensions: {len(voyage_res.embeddings[0])} (1024 dims = Low DB RAM)")

# 2. OpenAI 3-large: 3072-dimension vector ($0.13/M)
oai_res = oai.embeddings.create(model="text-embedding-3-large", input=sample_filing_text)
print(f"OpenAI Vector Dimensions: {len(oai_res.data[0].embedding)} (3072 dims = 3x DB RAM!)")

Domain-Specific Vector Retrieval: Voyage Finance-2 vs OpenAI text-embedding-3-large

Financial retrieval-augmented generation (RAG) applications—such as SEC 10-K filing analysis, quarterly earnings call indexing, and algorithmic trading intelligence—fail when using generic domain embedding models. While OpenAI text-embedding-3-large ($0.13/1M tokens, 3072 dimensions) performs well across broad open-domain benchmarks, Voyage AI's voyage-finance-2 ($0.12/1M tokens, 1024 dimensions) is fine-tuned specifically on balance sheets, tabular revenue metrics, and financial regulatory filings, yielding a 14.2% higher NDCG@10 retrieval accuracy.

Production Pipeline: Asynchronous Financial Batch Embedder in Python

Generating embeddings for thousands of 10-K report chunks using Voyage AI's asynchronous client with automatic retry logic:

import asyncio
import voyageai

vo = voyageai.AsyncClient()

async def embed_financial_corpus(chunks: list[str]) -> list[list[float]]:
    # Voyage AI supports up to 128 documents per batch request
    batch_size = 128
    all_embeddings = []
    
    for i in range(0, len(chunks), batch_size):
        batch = chunks[i:i + batch_size]
        result = await vo.embed(
            texts=batch,
            model="voyage-finance-2",
            input_type="document" # Optimizes vector geometry for document indexation
        )
        all_embeddings.extend(result.embeddings)
        print(f"[Voyage] Embedded {len(all_embeddings)}/{len(chunks)} financial chunks")
        
    return all_embeddings

Production Guardrails & Vector Storage TCO

• Vector Database Storage Multiplier: A 3,072-dimension vector requires 12.28 KB of RAM per vector in Pinecone, Qdrant, or pgvector. Voyage's 1,024-dimension vector cuts RAM footprint by 66%, slashing vector database cloud bills by thousands annually.
• Asymmetric Input Types: Always specify `input_type="document"` when indexing text passages and `input_type="query"` when embedding search queries. Voyage uses distinct projection layers to maximize retrieval recall.
• Context Window Ceiling: Voyage Finance-2 supports up to 32,000 tokens per chunk, allowing entire multi-page financial statements to be embedded without artificial sentence truncation.