Cohere Embed v4 costs $0.12 per 1 million tokens, positioning it directly against OpenAI’s high-end text-embedding-3-large ($0.13) rather than the commodity small model ($0.02). While most embedding models force you to chop documents into arbitrary 512-token chunks, Cohere Embed v4 offers a massive 128k context window. This allows you to embed entire legal contracts, technical manuals, or financial reports as single vectors, radically simplifying RAG pipelines by removing the complexity of chunking strategies.
For a production workload processing 50,000 corporate documents (avg 2,000 tokens each), you’re looking at ~100M tokens. With Cohere v4, that’s $12 initially. However, the real cost isn't the API fee—it's the storage and retrieval latency. Because v4 supports Matryoshka representation learning, you can store vectors at significantly reduced dimensions (e.g., 128 dimensions instead of 1024) without crashing retrieval quality. This can cut your vector database costs by 90% compared to storing full-fat OpenAI vectors.
The technical highlight is "noise robustness." Most embeddings treat all text as equally important. Cohere’s models are trained to prioritize high-quality information over filler, making them exceptionally good at retrieving relevant answers from messy, real-world enterprise data. The API is strictly typed and reliable, though the SDK feels less "lived in" than OpenAI's—expect fewer community wrappers and tutorials.
The downsides are specific. First, the 128k context is powerful but dangerous; embedding a whole book into one vector flattens too much detail. You still need intelligent segmentation for long-form content, just less aggressive than before. Second, the v3 models (still widely used) have a strict 512-token limit, which is a painful bottleneck for legacy implementations. Finally, if you need multimodal support (text+image), v4 handles it natively, but you must pass base64 encoded images, which adds payload overhead compared to passing URLs.
Use Cohere Embed v4 if you are building enterprise search over complex, multilingual documents and want to minimize engineering time spent on chunking logic. Skip it for simple chatbot memory or high-volume consumer apps where OpenAI’s $0.02 model is "good enough."
Pricing
The "free tier" is a Trial API key limited to 100 calls/minute, meant for development, not production. There is no free monthly credit allowance like OpenAI; it's purely rate-limited access.
For paid usage, the $0.12/1M token price tag (v4) is competitive with OpenAI's large model ($0.13) but expensive compared to the industry standard text-embedding-3-small ($0.02).
Cost Reality:
Processing 1GB of text (~200M tokens):
- Cohere v4: $24.00
- OpenAI Small: $4.00
The hidden value lies in Matryoshka embeddings. By shrinking vector dimensions from 1536 to 256, you save ~83% on vector DB storage and RAM, which often dwarfs the API generation cost at scale.
Technical Verdict
A specialized tool for serious engineers. The API is robust and supports features others ignore, like input_type parameters (search_query vs search_document) that materially improve ranking. Latency is higher than OpenAI (~200ms vs ~50ms) due to model complexity. The Python SDK is functional but lacks the massive ecosystem of community helpers found with competitors. Documentation is technical and accurate, avoiding marketing fluff.
Quick Start
# pip install cohere
import cohere
co = cohere.Client('YOUR_API_KEY')
response = co.embed(
texts=["What is the capital of Canada?"],
model="embed-v4.0",
input_type="search_query"
)
print(response.embeddings[0][:5]) # Print first 5 dimsWatch Out
- Embed v3 models have a hard 512-token limit; you must use v4 for long documents.
- Images for multimodal embeddings must be base64 encoded strings, not URLs, increasing payload size.
- You must specify
input_type('search_query' or 'search_document')—omitting this degrades performance significantly. - The 128k context is for input, but embedding a full book into one vector will dilute retrieval semantic density.