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Cohere Embed English v3 (Bedrock)

Up to 30%

Embed text at scale with Cohere Embed English v3 (Bedrock) on LLM.API—ideal for search, RAG, and clustering via the OpenAI-compatible embeddings API.

Start Using API

What is Cohere Embed English v3 (Bedrock)?

Choose Cohere Embed English v3 (Bedrock) when your pipeline needs dense representations instead of generated prose. Cohere Embed English v3 via AWS Bedrock. 1024 dimensions, 512-token input window, native array input (up to 96 texts per call). LLM.API exposes it with OpenAI-compatible embedding calls.

Providers

LLM.API routes Cohere Embed English v3 (Bedrock) to the providers below, with discounted effective rates versus list price.

List price by provider ($ / 1M tokens)

InputOutput
aws-bedrock$0.1 in
$0 out

Provider list prices; the LLM.API discount applies on top.

ProviderPricingContextCapabilities
aws-bedrock30% offin $100; out $0 per 1M tokens1K tokens—

Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 21 Sept 2026.

Try this model

Test Cohere Embed English v3 (Bedrock) right here — free to start.

Cohere Embed English v3 (Bedrock)
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Code snippet

Call Cohere Embed English v3 (Bedrock) through the OpenAI-compatible API — POST /v1/embeddings.

python
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_LLMAP_KEY",
    base_url="https://api.llmapi.ai/v1",
)

resp = client.embeddings.create(
    model="cohere.embed-english-v3",
    input="Semantic search starts with strong embeddings.",
)
print(len(resp.data[0].embedding))
{
  "model": "cohere.embed-english-v3",
  "input": "Semantic search starts with strong embeddings."
}

5 Core Capabilities

  • Semantic vectorization

    Maps text into dense vectors for similarity search and clustering. Relevant for `cohere.embed-english-v3` workloads on LLM.API.

  • Cross-document matching

    Supports near-duplicate detection and topical grouping. Grounded in the model's embedding role rather than generic chat claims.

  • Multilingual text

    Embeds content across languages depending on the underlying model family. Reflects Cohere positioning for this endpoint.

  • Batch throughput

    Designed for embedding large corpora via the OpenAI-compatible API. Tuned to how teams typically call Cohere Embed English v3 (Bedrock).

  • Retrieval readiness

    Fits RAG pipelines that need stable document and query embeddings.

6 Most Valuable Use Cases

  • Recommendation features based on text similarity with Cohere Embed English v3 (Bedrock)
  • Duplicate and near-duplicate content detection
  • RAG retrieval for grounded chat answers with Cohere Embed English v3 (Bedrock)
  • Anomaly grouping in incident descriptions
  • Topic clustering for content and support queues with Cohere Embed English v3 (Bedrock)
  • Multilingual document matching in one index

Why Build on LLM.API?

One unified API. Every major model. Built-in reliability, cost control, and observability.

  • Intelligent AI Routing

    Automatically route each request to the best model across providers based on latency, cost, and quality—without changing your integration or redeploying code.

    One endpoint, every model.

  • Cost-Aware Execution

    Control spend with per-request cost estimation, smart model selection, and centralized quotas so teams can experiment fast without runaway bills or manual tracking.

    More performance, less spend.

  • Resilient Fallback Flows

    Define automatic, provider-agnostic fallbacks to keep your app up during outages, rate limits, or timeouts—no brittle failover logic scattered through your codebase.

    Never go dark on users.

  • Deep LLM Observability

    Trace every call across providers with logs, metrics, and request replay so you can debug, tune prompts, and optimize model choices from one unified dashboard.

    See every token, everywhere.

  • Task-Level Orchestration

    Describe tasks, not models. LLM.API maps them to the right tools, models, and prompts so you ship complex AI workflows with minimal glue code.

    Think tasks, not models.

  • High-Throughput Batch APIs

    Process millions of inferences efficiently with optimized batch pipelines, concurrency controls, and retry logic—all behind the same simple interface you use for single calls.

    Scale from 1 to millions.

Why run Cohere Embed English v3 (Bedrock) on LLM.API?

  • Unified AI Routing

    Reach Cohere Embed English v3 (Bedrock) and sibling models through one OpenAI-compatible endpoint.

  • Cost Control

    Compare provider price points and keep spend visible as you scale Cohere Embed English v3 (Bedrock).

  • Reliability Layer

    Retry and route across configured providers when a single upstream blips.

  • Observability

    Trace prompts, tokens, and errors for Cohere Embed English v3 (Bedrock) alongside the rest of your stack.

  • Drop-in SDKs

    Production: Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.

  • Model Breadth

    Swap Cohere Embed English v3 (Bedrock) for chat, media, or embedding alternatives without rewriting auth.

When to Use — When NOT to Use

Use it if...

  • You need consistent vectors from an OpenAI-compatible embeddings API (Cohere Embed English v3 (Bedrock))
  • You are building search, RAG, or clustering on text (Cohere Embed English v3 (Bedrock))
  • Throughput and cost matter more than generative prose quality (Cohere Embed English v3 (Bedrock))

Avoid if...

  • You require cross-model vector compatibility without re-indexing
  • You only need OCR or speech features
  • You need generative answers instead of vectors

COMMUNITY

What developers say about Cohere models

Summarised from publicly published developer and community reviews of this model family. Opinions are the sources’, not LLM.API’s, and may not be specific to Cohere Embed English v3 (Bedrock).

  • Command A is reviewed as an enterprise-first, multilingual open-weight model with strong retrieval-augmented generation behaviour.
  • Independent testing reports materially fewer RAG hallucinations than comparable general models.
  • Reviewers position it for grounded enterprise search and agents rather than creative or frontier reasoning work.

SOURCES

Frequently Asked Questions

  • What are limitations of Cohere Embed English v3 (Bedrock)?

    Like other API models, Cohere Embed English v3 (Bedrock) can be wrong, incomplete, or uneven on edge cases. Validate outputs for high-stakes use. Media/OCR/STT models additionally depend on input quality.

  • Can I use tools or structured outputs with Cohere Embed English v3 (Bedrock)?

    Tooling support varies; for pure embedding models, prefer the modalities listed rather than assuming chat tools.

  • When should I choose Cohere Embed English v3 (Bedrock)?

    Throughput and cost matter more than generative prose quality — especially when you specifically need Cohere Embed English v3 (Bedrock).

  • What is the context length for Cohere Embed English v3 (Bedrock)?

    Reported context for Cohere Embed English v3 (Bedrock) is 512 tokens. Always verify the active provider row if multiple providers are listed.

  • Does Cohere Embed English v3 (Bedrock) support streaming?

    Streaming depends on the active provider; check the providers table on this page for flags.

  • How is Cohere Embed English v3 (Bedrock) priced on LLM.API?

    Listed pricing metadata shows: In $0.1 / 1M tokens. LLM.API may offer discounted effective rates (illustrative ~30% callout vs list when available).. Confirm live rates in the LLM.API dashboard or docs before production budgeting.

  • How do I call Cohere Embed English v3 (Bedrock) via API?

    Send OpenAI-compatible requests to https://api.llmapi.ai/v1 with model "cohere.embed-english-v3" and your LLM.API key. See the code snippet on this page.

  • Where is the canonical page for Cohere Embed English v3 (Bedrock)?

    https://llmapi.ai/models/cohere-embed-english-v3/

  • What modalities does Cohere Embed English v3 (Bedrock) support?

    Cohere Embed English v3 (Bedrock) accepts text and produces embedding according to its architecture metadata on LLM.API.

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