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Cohere Embed Multilingual v3 (Bedrock)

Up to 30%

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

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What is Cohere Embed Multilingual v3 (Bedrock)?

Cohere Embed Multilingual v3 (Bedrock) is an embedding model on LLM.API (`cohere.embed-multilingual-v3`). Cohere Embed Multilingual v3 via AWS Bedrock. 1024 dimensions, 512-token input window, native array input (up to 96 texts per call). Supports 100+ languages. It maps text into embedding vectors for retrieval, clustering, and semantic search—not for chat completions.

Providers

LLM.API routes Cohere Embed Multilingual 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 Multilingual v3 (Bedrock) right here — free to start.

Cohere Embed Multilingual v3 (Bedrock)
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Code snippet

Call Cohere Embed Multilingual 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-multilingual-v3",
    input="Semantic search starts with strong embeddings.",
)
print(len(resp.data[0].embedding))
{
  "model": "cohere.embed-multilingual-v3",
  "input": "Semantic search starts with strong embeddings."
}

5 Core Capabilities

  • Batch throughput

    Designed for embedding large corpora via the OpenAI-compatible API. Relevant for `cohere.embed-multilingual-v3` workloads on LLM.API.

  • Semantic vectorization

    Maps text into dense vectors for similarity search and clustering. Grounded in the model's embedding role rather than generic chat claims.

  • Multilingual text

    Embeds content across languages depending on the underlying model family. Tuned to how teams typically call Cohere Embed Multilingual v3 (Bedrock).

  • Cross-document matching

    Supports near-duplicate detection and topical grouping.

  • Low-latency lookups

    Suitable for online retrieval when paired with a vector store. Relevant for `cohere.embed-multilingual-v3` workloads on LLM.API.

6 Most Valuable Use Cases

  • Semantic search over docs, tickets, and wikis with Cohere Embed Multilingual v3 (Bedrock)
  • Duplicate and near-duplicate content detection
  • Anomaly grouping in incident descriptions with Cohere Embed Multilingual v3 (Bedrock)
  • Topic clustering for content and support queues
  • Multilingual document matching in one index with Cohere Embed Multilingual v3 (Bedrock)
  • Recommendation features based on text similarity

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 Multilingual v3 (Bedrock) on LLM.API?

  • Unified AI Routing

    Reach Cohere Embed Multilingual 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 Multilingual v3 (Bedrock).

  • Reliability Layer

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

  • Observability

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

  • Drop-in SDKs

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

  • Model Breadth

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

When to Use — When NOT to Use

Use it if...

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

Avoid if...

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

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 Multilingual 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

  • Which providers serve Cohere Embed Multilingual v3 (Bedrock)?

    LLM.API currently lists: aws-bedrock. Availability can vary by region and account.

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

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

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

    Like other API models, Cohere Embed Multilingual 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 Multilingual v3 (Bedrock)?

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

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

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

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

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

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

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

  • Can Cohere Embed Multilingual v3 (Bedrock) generate paragraphs of text?

    No. Cohere Embed Multilingual v3 (Bedrock) returns embeddings (vectors). Pair it with a chat model if you need generated language.

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