Cohere Embed API
Generates high-quality text embeddings via embed-english-v3.0 and embed-multilingual-v3.0 models, supporting batch encoding of up to 96 texts with configurable compression and input-type-aware representations.
Score Breakdown
⚙ Agent Friendliness
🔒 Security
API keys have no scope restriction — a leaked key grants full account access. Key rotation is supported via dashboard. No IP allowlisting on free tier.
⚡ Reliability
Best When
You need production-quality text embeddings with fine-grained input-type control and optional int8/binary compression for cost-efficient vector storage.
Avoid When
You need sub-100ms p50 latency for single-text online serving or require OpenAI-compatible drop-in embedding endpoints without code changes.
Use Cases
- • Encoding document chunks for RAG pipelines with search_document input type
- • Encoding user queries at retrieval time with search_query input type for asymmetric search
- • Classifying support tickets or emails using classification input type embeddings
- • Building multilingual semantic search across 100+ languages with embed-multilingual-v3.0
- • Reducing vector storage costs with int8 or binary compression on v3 models
Not For
- • Generative text completion or chat — use Cohere Command models instead
- • Real-time single-token latency requirements under 50ms
- • Audio, image, or multimodal embedding tasks
Interface
Authentication
API key passed as Bearer token in Authorization header or as X-API-Key header. Trial keys available without credit card.
Pricing
Trial keys are rate-limited to ~100 calls/min. Production keys require billing setup. Batch size of 96 texts per request helps minimize per-request overhead costs.
Agent Metadata
Known Gotchas
- ⚠ input_type parameter is required for v3 models — omitting it causes degraded retrieval quality, not an error, so silent quality regression is possible
- ⚠ Max 96 texts per batch; agents chunking documents must implement their own batching loop
- ⚠ Text longer than the model's token limit (512 tokens for v3) is silently truncated without warning
- ⚠ Embedding dimensions differ by model (1024 for v3 English, 768 for multilingual); mixing models in the same vector index causes silent similarity failures
- ⚠ int8 and binary compression options change vector semantics — vectors compressed differently are not comparable and must use matching compression at query time
Alternatives
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Scores are editorial opinions as of 2026-03-07.