---
last_updated: 2026-07-20
tags: [embedding, cohere, cloud, farsi, insurance, rag, enterprise]
source: import-knowledge
---

# Cohere — embed-v4.0

## Overview

- **Type**: Cloud
- **Provider**: Cohere
- **Price**: Enterprise only (Model Vault: ~$4.00–$5.00/hr dedicated instance) — legacy embed-multilingual-v3 was ~$0.10/1M tokens ([source](https://cohere.com/pricing))
- **Max input tokens**: 512,000
- **Embedding dimensions**: 256, 512, 1024, or 1536 (configurable)
- **Language coverage**: 100+ languages including Farsi
- **Persian benchmark**: Cohere models score well on MIRACL-Farsi on the MTEB leaderboard. No isolated Farsi numbers published by Cohere.

## Why It's #1 Cloud Pick

**512K context window** — largest of any embedding API. Can embed full insurance documents without chunking. Strong Farsi support (100+ languages). MIRACL-Farsi scores on MTEB leaderboard.

**Caveat**: Enterprise-only pricing. No public per-token API anymore.

## Pros

- 100+ languages with explicit Farsi support
- 512K context — can embed full documents, no chunking needed
- Strong MIRACL-Farsi scores
- v2 API with input_type distinction (search_query vs search_document)
- Configurable dimensions (256–1536)

## Cons

- Enterprise-only pricing (Model Vault: ~$4–5/hr dedicated instance)
- No public per-token API pricing — requires sales engagement
- Smaller ecosystem than OpenAI

## API Test — Curl

**Endpoint**: `POST https://api.cohere.ai/v2/embed`
**Auth**: `Authorization: Bearer $COHERE_API_KEY`
**Get key**: https://dashboard.cohere.com/api-keys

```bash
curl -s https://api.cohere.ai/v2/embed   -H "Content-Type: application/json"   -H "Authorization: Bearer $COHERE_API_KEY"   -d '{
    "model": "embed-v4.0",
    "texts": ["بیمه نامه شخص ثالث چیست؟"],
    "input_type": "search_query",
    "embedding_types": ["float"]
  }' | python3 -m json.tool
```

### ⚠️ Key Differences from OpenAI

| Aspect | OpenAI | Cohere |
|--------|--------|--------|
| Input field | `"input"` (string/array) | `"texts"` (array) |
| Input type | N/A | Required: `"search_query"` or `"search_document"` |
| Output format | `data[0].embedding` | `data.embeddings.float[0]` |
| Token count | `usage.total_tokens` | `meta.billed_units.input_tokens` |

### Indexing Documents (use search_document)

```bash
curl -s https://api.cohere.ai/v2/embed   -H "Content-Type: application/json"   -H "Authorization: Bearer $COHERE_API_KEY"   -d '{
    "model": "embed-v4.0",
    "texts": ["بیمه نامه شخص ثالث شامل پوشش خسارات مالی و جانی است."],
    "input_type": "search_document",
    "embedding_types": ["float"]
  }' | python3 -c "import sys,json; d=json.load(sys.stdin); print(f'dims={len(d["data"]["embeddings"]["float"][0])}, tokens={d["meta"]["billed_units"]["input_tokens"]}')"
```

## API Test — Python

```python
import cohere

co = cohere.ClientV2(api_key="YOUR_COHERE_API_KEY")  # or use COHERE_API_KEY env var

response = co.embed(
    model="embed-v4.0",
    input_type="search_query",
    texts=["بیمه نامه شخص ثالث چیست؟"],
    embedding_types=["float"],
    output_dimension=1024
)

embedding = response.embeddings.float[0]
print(f"Dimensions: {len(embedding)}")
```

## TODO

- [ ] Contact Cohere sales for enterprise pricing
- [ ] Benchmark on MIRACL-Farsi subset
- [ ] Compare with OpenAI text-embedding-3-large on Farsi queries
- [ ] Verify on-premises deployment options