First-Pass Retrieval,state-of-the-art recall
zembed-1 is the current #1 embedding model on graded retrieval benchmarks. 4B parameters, up to 2560 dimensions, multilingual, and instruction-aware. Because your embedding search's ceiling is your entire pipeline's ceiling.
Dense Recall is a ceiling
Embeddings are fast and cheap and how you find a hundred plausible candidates out of ten million in milliseconds. But Recall@100 is the silent ceiling on every RAG pipeline: everything downstream – LLM calls, rerankers, your agents – can only sort what the embedding surfaces, and most models simply leave relevant documents on the floor.
Across 28 datasets and 3 LLM judges. Ahead of voyage-4 (0.712) and harrier-27b (0.706).
Highest of any embedding model we test: +2.0 pts over voyage-4, +2.2 over harrier-27b.
P50 ~280 ms vs ~2500 ms at 2 QPS / 2560-dim / 512-token inputs. Fast mode goes lower.
“Better recall on our long-tail queries was the entire reason to switch. The reranker downstream got a better candidate set on every query, and our metrics moved.”
“Multilingual recall held up across our European and Asian markets where the previous embedding fell off a cliff.”
zembed-1: The World's Best Text Embedding Model
Bi-encoder embedding model, distilled directly from zerank-2 so the relevance signal you fit downstream is the same one the model was trained on. Tunable at inference: dimensions and quantization both swap on the fly, no retraining.
- Parameters
- 4B · open weights
- Context window
- 32K tokens
- Languages
- 100+ · >50% non-English training data · cross-lingual parity
- Dimensions
- 40 → 2560, truncatable at inference (no retraining; not Matryoshka)
- Quantization
- float32 · int8 (4× smaller) · binary (32× smaller) — pick per call
- Storage example
- 8 KB at full / 256 dims @ int8 → 256 B / under 128 B at the smallest end
- Training
- Distilled from zerank-2 (zELO-derived continuous Elo targets)
- API
- input_type: query|document · latency: fast|slow per call
- NDCG@10 (28 datasets, 3 LLM judges)
- 0.715 — #1 of every embedding model we tested
- Recall@100
- 0.771 — +2.0 over voyage-4, +2.2 over harrier-27b
- vs the field on Recall@100
- Up to +7% over OpenAI Large, Qwen3-4B, BGE-M3, Gemini, Cohere v4, Voyage-4-nano
- P50 latency · 2 QPS · 2560-d · 512 tokens
- ~280 ms (fast) vs ~2500 ms voyage-4 — ~9× faster
- Vertical strength
- Largest gains on finance, healthcare, legal — domain-vocabulary-heavy verticals
Trained on similarity, not boundaries.
Most embedding models train against binary relevant/not-relevant labels. zembed-1 is trained on continuous relevance scores derived from pairwise LLM preferences — the same signal behind zerank-2. This is why it does disproportionately well on graded evaluations where binary-trained competitors plateau.
Continuous relevance scores
Pairwise LLM preferences are converted into absolute ELO-style scores via Thurstone fitting — a graded signal, not a binary one.
Broad-domain training
Legal, medical, financial, code, multilingual, and technical corpora — chosen so the model generalizes to private enterprise data, not just the public benchmark.
Flexible dimensions
Output 1024 / 1536 / 2560-dim vectors. 1536 is the production sweet spot; 2560 for last-mile accuracy; 1024 for index-cost-sensitive consumer search.
Query/document asymmetry
An `input_type` parameter (`query` vs `document`) embeds the same string at slightly different points in space — reflecting the inherent asymmetry of search.
Integrate ZeroEntropy models in minutes. Production-ready, latency-optimized, available everywhere.
Access all models through a single, latency-optimized API, or through our partner providers.
# Create an API Key at https://dashboard.zeroentropy.dev
from zeroentropy import ZeroEntropy
zclient = ZeroEntropy()
response = zclient.models.rerank(
model="zerank-2",
query="What is Retrieval Augmented Generation?",
documents=[
"RAG combines retrieval with generation...",
],
)
for doc in response.results:
print(doc)
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