Key Takeaways
- OpenAI text-embedding-3-small is the default choice for most applications: strong quality, low cost ($0.02/M tokens), and 1536 dimensions.
- Cohere embed-v3 excels at multilingual content with native support for 100+ languages and task-specific model variants (search, classification, clustering).
- Open-source models eliminate per-token costs but require GPU infrastructure. BGE-large and E5-large-v2 match or exceed commercial models on many benchmarks.
- Dimension reduction can save 50% storage costs with minimal quality loss: OpenAI's text-embedding-3 models support native dimension truncation.
- Always benchmark on your domain data: MTEB benchmarks use general datasets. Your specific content (legal, medical, technical) may perform differently across models.
Embedding Model Comparison
| Model | Provider | Dimensions | Cost (per 1M tokens) | MTEB Score | Best For |
|---|---|---|---|---|---|
| text-embedding-3-small | OpenAI | 1536 | $0.02 | 62.3 | General purpose, cost-effective |
| text-embedding-3-large | OpenAI | 3072 | $0.13 | 64.6 | Maximum quality (English) |
| embed-v3 (English) | Cohere | 1024 | $0.10 | 64.5 | English search and RAG |
| embed-v3 (multilingual) | Cohere | 1024 | $0.10 | 63.8 | Multi-language applications |
| BGE-large-en-v1.5 | BAAI (open-source) | 1024 | Free (self-hosted) | 63.9 | Self-hosted, data privacy |
| E5-large-v2 | Microsoft (open-source) | 1024 | Free (self-hosted) | 62.7 | Self-hosted, good quality |
| Nomic-embed-text | Nomic (open-source) | 768 | Free (self-hosted) | 62.4 | Lightweight, efficient |
| GTE-large | Alibaba (open-source) | 1024 | Free (self-hosted) | 63.1 | Self-hosted alternative |
Selection Criteria and Trade-offs
| Criterion | Commercial (OpenAI/Cohere) | Open-Source (BGE/E5) | Impact |
|---|---|---|---|
| Setup effort | API key, minutes | GPU server, hours | Time to prototype |
| Per-query cost | $0.00002-0.00013 per 1K tokens | $0 (infrastructure only) | Total cost at scale |
| Data privacy | Data sent to provider | Data stays on your server | Compliance requirements |
| Latency | Network round-trip | Local inference (faster) | User experience |
| Model updates | Provider updates automatically | Manual updates required | Maintenance burden |
| Quality | Consistently strong | Varies by model/domain | Application accuracy |
At approximately 10 million embedding requests per month, self-hosted open-source models become cheaper than OpenAI (factoring in GPU server costs of $200-500/month). Below that volume, API-based models are more cost-effective when you include setup and maintenance time.
Dimension Optimization and Storage
| Dimensions | Storage per 1M vectors | Quality (relative) | Query Speed (relative) |
|---|---|---|---|
| 256 | ~1 GB | 85-90% | Fastest |
| 512 | ~2 GB | 92-95% | Fast |
| 768 | ~3 GB | 95-97% | Good |
| 1024 | ~4 GB | 97-99% | Good |
| 1536 | ~6 GB | 99-100% | Moderate |
| 3072 | ~12 GB | 100% (baseline) | Slowest |
OpenAI's text-embedding-3 models support native Matryoshka dimension reduction: generate a 3072-dim embedding, then truncate to 1024 or 512 dimensions with minimal quality loss. This lets you optimize the storage-quality trade-off without changing models.
Benchmarking on Your Data
- Create an evaluation dataset: 50-100 queries with annotated relevant documents from your actual content
- Generate embeddings with each candidate model: embed all documents and queries using each model you are evaluating
- Measure retrieval quality: calculate precision@k and recall@k for each model. The model that retrieves the most relevant documents for your queries wins.
- Factor in practical constraints: latency, cost, data privacy, and operational complexity. The best-performing model may not be the best choice if it violates a constraint.
- Test edge cases: evaluate with short queries, long queries, technical jargon, and queries in non-English languages if applicable
MTEB benchmark scores reflect performance on general datasets. For specialized domains (legal, medical, scientific), open-source models fine-tuned on domain data often outperform general-purpose commercial models. If your content is highly specialized, test domain-specific models.
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