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.

Which endpoint do I call for Cohere Embed Multilingual v3 (Bedrock)?

POST https://api.llmapi.ai/v1/chat/completions with model "cohere.embed-multilingual-v3" and your LLM.API key. The request and response bodies are OpenAI-compatible.

How do I switch from OpenAI to Cohere Embed Multilingual v3 (Bedrock) on LLM.API?

Change two lines: set base_url to https://api.llmapi.ai/v1 and api_key to your LLM.API key. The rest of your OpenAI SDK code stays the same.

Does Cohere Embed Multilingual v3 (Bedrock) support function calling and structured outputs?

Function calling: not supported. Structured outputs: not supported.

Cohere Embed Multilingual v3 (Bedrock) API: Endpoint & Pricing

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.

Start Using API

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.

Can you self-host Cohere Embed Multilingual v3 (Bedrock)?

No. We found no public weights for Cohere Embed Multilingual v3 (Bedrock) on Hugging Face, so there is nothing to download, quantize or serve on your own GPUs: no VRAM budget, no vLLM or GGUF build to plan for. The only way to run it is over an API.

Skip the deploy — use LLM.API as your endpoint

No GPUs, no quantization trade-offs. Same model, OpenAI-compatible, up to 30% below list price.

Get API key See API code ↓

Providers

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

List price by provider

InputOutput

aws-bedrock$0.1 in

$0 out

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

Provider	Pricing	Context	Capabilities
aws-bedrock	in $0.1; out $0 per 1M tokens	1K tokens	—

Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 8 Oct 2026.

Try this model

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

Cohere Embed Multilingual v3 (Bedrock)

Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

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

Switching from OpenAI? Change 2 lines.

- base_url="https://api.openai.com/v1"
- api_key="YOUR_OPENAI_KEY"
+ base_url="https://api.llmapi.ai/v1"
+ api_key="YOUR_LLMAPI_KEY"

Everything else stays the same — SDK, message format, tools, streaming. Set model to cohere.embed-multilingual-v3 and the call runs unchanged.

bash

curl https://api.llmapi.ai/v1/chat/completions \
  -H "Authorization: Bearer $LLMAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "cohere.embed-multilingual-v3",
    "messages": [
      {"role": "user", "content": "Give me three crisp launch checklist items."}
    ]
  }'

from openai import OpenAI

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

resp = client.chat.completions.create(
    model="cohere.embed-multilingual-v3",
    messages=[
        {"role": "system", "content": "You are a precise product assistant."},
        {"role": "user", "content": "Give me three crisp launch checklist items."},
    ],
)
print(resp.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.LLMAPI_KEY,
  baseURL: "https://api.llmapi.ai/v1",
});

const resp = await client.chat.completions.create({
  model: "cohere.embed-multilingual-v3",
  messages: [
    { role: "user", content: "Give me three crisp launch checklist items." },
  ],
});

console.log(resp.choices[0].message.content);

stream = client.chat.completions.create(
    model="cohere.embed-multilingual-v3",
    messages=[{"role": "user", "content": "Write a haiku about latency."}],
    stream=True,
)

for chunk in stream:
    delta = chunk.choices[0].delta.content
    if delta:
        print(delta, end="", flush=True)

{
  "model": "cohere.embed-multilingual-v3",
  "messages": [
    {"role": "system", "content": "You are a precise product assistant."},
    {"role": "user", "content": "Give me three crisp launch checklist items."}
  ]
}

ENDPOINTS

Supported endpoints & features

Base URL https://api.llmapi.ai/v1 · model ID cohere.embed-multilingual-v3 · OpenAI-compatible.

POST /v1/chat/completionsChat Completions — supported

Feature	Status	How to set it
Function calling (tools)	Not supported	`tools, tool_choice`
Parallel tool calls	Not supported	`parallel_tool_calls`
JSON mode	Not supported	`response_format: {"type": "json_object"}`
Structured outputs	Not supported	`response_format: {"type": "json_schema", …}`
Streaming	Not supported	`stream: true`
Vision (image input)	Not supported	`image_url content parts`
Web search	Not supported	`web_search`

ERRORS

Errors & fallback routing

Code	What it means	What to do
`401`	Missing or invalid API key.	Send `Authorization: Bearer YOUR_LLMAPI_KEY` and check the key is active.
`429`	Rate limited, or the upstream provider is throttling the request.	Back off and retry with jitter; the gateway also retries the request on another provider where one is available.
`5xx`	Upstream provider error or timeout.	Retry; fallback routing sends the retry to the next healthy provider for this model.

When a provider fails or throttles, the request is routed to the next provider serving this model — the ones listed in the providers table above.

Cohere Embed Multilingual v3 (Bedrock) in production

Best workloadsLLM.API features

What the model is good at.

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.

What you get when you call Cohere Embed Multilingual v3 (Bedrock) through LLM.API.

Unified AI Routing
Reach Cohere Embed Multilingual v3 (Bedrock) and sibling models through one OpenAI-compatible endpoint.
One endpoint, every model.
Cost Control
Compare provider price points and keep spend visible as you scale Cohere Embed Multilingual v3 (Bedrock).
More performance, less spend.
Reliability Layer
Practical: Retry and route across configured providers when a single upstream blips.
Never go dark on users.
Observability
Trace prompts, tokens, and errors for Cohere Embed Multilingual v3 (Bedrock) alongside the rest of your stack.
See every token, everywhere.
Drop-in SDKs
Keep using familiar OpenAI client patterns with base URL https://api.llmapi.ai/v1.
Think tasks, not models.
Model Breadth
Production: Swap Cohere Embed Multilingual v3 (Bedrock) for chat, media, or embedding alternatives without rewriting auth.
Scale from 1 to millions.

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

LLM API rating for Cohere Embed Multilingual v3 (Bedrock)

★ 3.1/ 5.0LLM API rating
50% production adoption · 30% category benchmark · 20% tooling & valueEstimated from published specs

3.7Production adoptionEstablished provider, moderate volume

2.8Benchmark qualityNo public Arena rank — scored on declared reasoning support

2.2Developer ecosystemContext window, tool and JSON support, price against the category median

How is this rating calculated?

Every model gets a separate 1.0–5.0 score for each category it can be used in, so a model can rate high for coding and lower for vision. The score combines three parts:

Production adoption — 50%. How widely the model is actually used in production: provider traffic, LiteLLM and gateway usage, open-weight downloads and its LMArena rank. Proven, widely used models score highest.
Category benchmark — 30%. The public benchmark that matters for that category — the Artificial Analysis Intelligence Index and LMArena Elo for reasoning, LMArena coding Elo for coding, image and document support plus vision Elo for vision, tool and JSON support for function calling, measured speed for voice.
Tooling & value — 20%. Context window, support for tools, structured outputs and streaming, and price compared with the category median.

Scores of 4.0 and above are shown in green, 3.0–3.9 in yellow, below 3.0 in red. When a model has no public data for a part, it gets a neutral value instead of an invented number.

A “Verified benchmark” mark (✓) means an independent lab — LMArena or Artificial Analysis — has actually tested that model, and the source figure is shown. Models without that mark are labelled “Estimated from published specs”: the score comes from the published context window, tool and schema support, price and adoption only, because no independent lab has tested them yet.

One exception: a brand-new top-tier model that scores very high on the independent benchmarks but has no usage history yet is rated on the benchmark (60%) and tooling & value (40%) alone, so a missing adoption figure can't hold it back. All inputs come from public sources and are refreshed weekly.

Adoption and benchmark inputs come from public sources — LMArena, Artificial Analysis Intelligence Index and published model specs. Updated weekly.

COMMUNITY

What developers say about Cohere models

LMArena—Speed—Sources: LMArena, Artificial Analysis

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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Cohere Embed Multilingual v3 (Bedrock) API: Endpoint & Pricing

What is Cohere Embed Multilingual v3 (Bedrock)?

Can you self-host Cohere Embed Multilingual v3 (Bedrock)?

Providers

Try this model

Code snippet

Supported endpoints & features

Errors & fallback routing

Cohere Embed Multilingual v3 (Bedrock) in production

Batch throughput

Semantic vectorization

Multilingual text

Cross-document matching

Low-latency lookups

Unified AI Routing

Cost Control

Reliability Layer

Observability

Drop-in SDKs

Model Breadth