Text Embedding 3 Large vs Gemini Embedding 2
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| Comparison | Text Embedding 3 Largetext-embedding-3-large | Gemini Embedding 2gemini-embedding-2 |
|---|---|---|
| Type | Embedding | Embedding |
| Best for | Semantic search, RAG and clustering | Semantic search, RAG and clustering |
| TokenRex price | $0.13 per 1M tokens | $0.20 per 1M tokens |
| Cached input / 1M tokens | No separate cached rate listed | No separate cached rate listed |
| Cache write / 1M tokens | Not applicable | Not applicable |
| Cached output | Not offered · standard output rate applies | Not offered · standard output rate applies |
| Web search | Not applicable | Not applicable |
| Market price | $0.13 per 1M tokens | $0.20 per 1M tokens |
| Input token limit (provider) | Within the context window | Within the context window |
| Vendor input modalities | Text | Text, Image, Audio, Video, Documents |
| Vendor output modalities | Embeddings | Embeddings |
| TokenRex endpoints | POST /v1/embeddings | POST /v1/embeddings |
| Benchmarks | ||
| Benchmark evidence | 2 source-linked results | 3 source-linked results |
| MTEB (English) | 64.6% Official January 2024 launch average score, full-dimension model, not an independent TokenRex evaluation. OpenAI · New embedding models | — |
| MIRACL | 54.9% Official January 2024 launch average score, full-dimension model, not an independent TokenRex evaluation. OpenAI · New embedding models | — |
| MTEB (Multilingual), Text-Text | — | 69.9mean task score Text-to-text evaluation only (model is natively multimodal but this metric isolates text-text performance). Published source text: Official Google performance table: Gemini Embedding 2 scores 69.9 Mean(Task) on MTEB (Multilingual), Text-Text, vs 68.4 for legacy gemini-embedding-001. Google DeepMind — Gemini Embedding 2 model card |
| MTEB (Code) | — | 84mean task score MTEB Code benchmark mean task score. Published source text: Official Google performance table: Gemini Embedding 2 scores 84.0 Mean(Task) on MTEB (Code) vs 76.0 for legacy gemini-embedding-001. Google DeepMind — Gemini Embedding 2 model card |
| Image-Text (TextCaps) | — | 97.4% Cross-modal image-to-text retrieval, TextCaps recall@1. Published source text: Official Google performance table: Gemini Embedding 2 Image→Text TextCaps recall@1 = 97.4 (vs 88.1 for legacy multimodalembedding@001, 88.9 Amazon Nova 2, 88.6 Voyage Multimodal 3.5). Google DeepMind — Gemini Embedding 2 model card |
No overall score, winner or cost-versus-score frontier is calculated without comparable, attributable benchmark evidence. Different test configurations appear separately. Vendor capabilities are distinct from TokenRex API support.
— means no published value or no matching capability. Provider token limits are not tested gateway guarantees; TokenRex media controls and response formats are listed separately.
Text Embedding 3 Large
Sources and verification
Provider specifications describe the upstream model, not a guarantee of every feature through TokenRex. Prices come from the enabled catalog; benchmark results belong to the exact tested model.
- Vendor model documentation
Vendor-reported
Last checked - OpenAI · New embedding models
Vendor-reported
Last checked
Gemini Embedding 2
Sources and verification
Provider specifications describe the upstream model, not a guarantee of every feature through TokenRex. Prices come from the enabled catalog; benchmark results belong to the exact tested model.
- Vendor model documentation
Vendor-reported
Last checked - Google DeepMind — Gemini Embedding 2 model card
Vendor-reported
Last checked
Research notes (1)
- Vendor multimodal embeddings do not guarantee the same input surface through TokenRex.
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