text-embedding-3-large vs Cohere embed-v3 throughput

Benchmarking text-embedding-3-large vs Cohere embed-v3 throughput isn’t just about raw tokens-per-second; it’s about how each model’s API shape, context window, and output dimensionality interact with your pipeline. Both are top-tier embedding models, but the throughput you’ll see in production depends as much on batching strategy and provider limits as on the underlying transformer. This head-to-head breaks down the tradeoffs engineers actually hit when shipping retrieval systems.

Capabilities

OpenAI’s text-embedding-3-large outputs 3072-dimensional vectors by default, but supports Matryoshka representation truncation via the dimensions parameter. You can request 256, 512, 1024, or 3072 dims from the same model checkpoint. It accepts up to 8192 tokens per input, which matters when embedding long PDF chunks or full articles without prior splitting.

Cohere’s embed-v3 family (embed-english-v3, embed-multilingual-v3) fixes output at 1024 dimensions but offers multiple embedding types: float, int8, and binary. Binary embeddings cut memory and dot-product cost by 32x versus float32, at a modest recall penalty. Cohere enforces a 512-token limit per input for English and 256 for multilingual, so long documents must be pre-chunked. A distinctive capability is the required input_type (search_document, search_query, classification, clustering), which biases the embedding space for asymmetric retrieval.

# OpenAI: dimension-flexible, long context
from openai import OpenAI
client = OpenAI()
r = client.embeddings.create(
    model="text-embedding-3-large",
    input=["long doc ...", "another doc ..."],
    dimensions=1024
)

# Cohere: input_type required, fixed dim, quantization options
import cohere
co = cohere.Client("key")
r = co.embed(
    texts=["doc ...", "doc ..."],
    model="embed-english-v3",
    input_type="search_document",
    embedding_types=["int8"]
)

Price / Cost Model

List prices are public and per-input-token only—there are no output token charges because vector size is fixed.

• text-embedding-3-large: $0.13 per 1M tokens.
• embed-english-v3: $0.10 per 1M tokens (multilingual is slightly higher on Cohere’s scale tier).

The cost delta looks small, but at billion-document indexing it compounds. Throughput directly converts to cost: if you can embed 2x tokens/sec on the same hardware via better batching, your compute bill at the provider stays the same but your pipeline wall-clock shrinks. Cohere’s int8/binary output also reduces downstream vector DB storage and query cost, which is where most production embedding spend actually hides.

Latency / Throughput

The phrase text-embedding-3-large vs Cohere embed-v3 throughput only becomes meaningful once you fix batch size, sequence length, and client concurrency. Both providers accept arrays of inputs in a single request; neither streams embeddings. Network round-trip dominates small batches (<8 items), while GPU compute dominates large batches.

OpenAI’s endpoint will reject requests exceeding 300k tokens total in a single call; Cohere’s practical limit is lower per call but both encourage chunked bulk jobs. In our load tests against the raw provider APIs, Cohere’s 1024-dim float output serializes faster over the wire and into FAISS than OpenAI’s 3072-dim default, but when we truncated OpenAI to 1024 dims the serialization gap narrowed. Raw embedding compute per token is comparable because both use similar-sized transformer backbones.

Provider-side rate limits are the real throughput ceiling. OpenAI applies tiered RPM/TPM limits; Cohere applies org-level caps. If you front either model through a gateway such as n4n.ai, automatic fallback masks a provider’s 429s by rerouting to a healthy region or model, but the underlying token generation speed is still bounded by the origin fleet.

# Batching pattern that maximizes throughput on either API
texts = [f"doc-{i}" for i in range(128)]
# OpenAI
client.embeddings.create(model="text-embedding-3-large", input=texts, dimensions=1024)
# Cohere
co.embed(texts=texts, model="embed-english-v3", input_type="search_document")

Ergonomics

OpenAI’s embeddings API is dead simple: one endpoint, optional dimensions, no input-type semantics. That lowers code complexity but pushes the retrieval-quality burden onto your chunking and query formulation.

Cohere forces input_type, which is annoying for trivial scripts but pays off in hybrid search: separate search_query and search_document embeddings measurably improve recall on asymmetric corpora. Its embedding_types parameter is a clean way to switch precision without model swaps.

Both SDKs are synchronous by default; for high throughput you’ll wrap them in asyncio or use the bulk endpoints. OpenAI’s ecosystem has near-universal compatibility—every LangChain retriever, every vector DB sample, assumes the text-embedding-* shape. Cohere is well-supported but occasionally needs an adapter for its input_type.

Ecosystem

OpenAI-compatible endpoints are ubiquitous. If you already call api.openai.com/v1/embeddings, swapping to any OpenRouter-class gateway or self-hosted vLLM is a base-URL change. Cohere’s API is also standardized but less likely to be the default in third-party tutorials. Both integrate with Pinecone, Weaviate, pgvector, and MongoDB Atlas. Cohere ships first-party rerankers (rerank-english-v3) that pair naturally with its embeddings; OpenAI has no equivalent rerank endpoint, so you’d reach for Cohere or a cross-encoder anyway.

Limits

Which to choose

Long-document multilingual indexing. Use text-embedding-3-large. The 8192-token window means fewer chunks per PDF, and the Matryoshka truncation lets you store 1024-dim vectors to keep parity with Cohere on memory.

High-volume English search with tight storage. Use Cohere embed-english-v3 with int8 or binary. The 512-token limit is fine for passage retrieval, and the 4 KB float vector (or 1 KB binary) slashes pgvector footprint. The input_type separation gives better query/doc alignment.

Pipeline simplicity over marginal recall. Use text-embedding-3-large with default dims. You avoid the input_type branching and keep one embedding call shape across languages and tasks.

Hybrid retrieval with reranking. Cohere embed-v3 plus Cohere rerank is the path of least resistance; the embeddings and reranker share tokenization assumptions.

The text-embedding-3-large vs Cohere embed-v3 throughput decision ultimately hinges on sequence length and vector precision. If you need long context and dimension flexibility, OpenAI wins. If you need cheap, dense, quantized English embeddings at scale, Cohere’s v3 is the better throughput-per-dollar engine.