KaLM Embedding Gemma3 12B 2511 vs Seed1.6 Embedding 1215
MTEB multilingual v2 · Mean over tasks · multilingual (1037 languages) Embeddings assign examples to categories, group similar examples, estimate semantic similarity between pairs, classify relationships between pairs, rank relevant documents for queries, find translated sentence pairs, rerank documents according to instructions, assign multiple labels to each example, reorder candidate documents by relevance on MTEB Multilingual v2 · mean over tasks in 1037 languages including 'Auhelawa, Abau, Achi, Achinese, Achuar-Shiwiar, Adzera, Afrikaans, Agarabi; tasks receive equal weight
KaLM Embedding Gemma3 12B 251172.3
Seed1.6 Embedding 121570.3
| Benchmark | KaLM Embedding Gemma3 12B 2511 | Seed1.6 Embedding 1215 |
|---|---|---|
| MTEB multilingual v2 · Mean over tasks · multilingual (1037 languages) | 72.3 | 70.3 |
Specs
| Spec | KaLM Embedding Gemma3 12B 2511 | Seed1.6 Embedding 1215 |
|---|---|---|
| Context | 33K | 33K |
| Weights | Open | Closed |
| Released | Nov 2025 | Dec 2025 |
| Dimensions | 3,840 | — |
| Max input | 131K tokens | 33K tokens |
| Memory (4-bit) | 7.1 GB | — |
| Licence | tencent-kalm-embedding-community | — |
| Commercial use | Restricted | — |
What breaks when you switch
KaLM Embedding Gemma3 12B 2511 → Seed1.6 Embedding 1215
Re-embed your whole corpus›
Vectors from different models can't be mixed.
Max input: 131K tokens → 33K tokens
Seed1.6 Embedding 1215 → KaLM Embedding Gemma3 12B 2511
Re-embed your whole corpus›
Vectors from different models can't be mixed.