Twelvelabs Marengo Embed 3.0 v1.0

Amazon Bedrock embedding

Twelvelabs Marengo Embed 3.0 v1.0 is an Amazon Bedrock embedding model.It supports a 500-token context window. Route Twelvelabs Marengo Embed 3.0 v1.0 via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.

Pricing source: litellm Last verified: Oct 22, 2026 View source ↗
Cost calculator

Estimate Twelvelabs Marengo Embed 3.0 v1.0 spend

Pick a workload, fine-tune the sliders, and see the monthly bill.

~3K in / ~400 out · 5K req/day
3,000
08,000
400
016,000
5,000
01,000,000
Per request
$0.000000
in $0.000000 · out $0.000000
Per day
$0.000000
5,000 requests
Per month
$0.000000
152,188 requests

Estimate uses $0.000000/M input · $0.000000/M output. Provider pricing changes. Production costs vary with retries, streaming overhead, and tool-call rounds.
Want this for free? Cache + route via Agent Command Center — first 100K requests and 100K cache hits free every month.

Pricing

Per-token rates, expressed in USD per 1M tokens. Verified Oct 22, 2026.

Input —
Output —
Per image $0.000100
Per minute (audio) $0.008400

Context & limits

Context window
500 tokens
Max input
500 tokens
Max output
—
Modalities
embedding, text

Capabilities

  • Function calling — not advertised
  • Parallel tool calls — not advertised
  • Vision input — not advertised
  • Audio input — not advertised
  • Audio output — not advertised
  • PDF input — not advertised
  • Streaming ✓ supported
  • Structured output — not advertised
  • Prompt caching — not advertised
  • Reasoning — not advertised

Strengths & caveats — when to pick Twelvelabs Marengo Embed 3.0 v1.0

Data-driven from Twelvelabs Marengo Embed 3.0 v1.0's percentile within embedding peers.

Where it's strong

Watch out for

  • !limited context — 500-token window is in the bottom quartile; not ideal for long documents or large RAG
  • !small context (under 16K tokens)

Benchmarks pending

We haven't logged public benchmark scores for Twelvelabs Marengo Embed 3.0 v1.0 yet. Have one to contribute? Submit a source — citations help us prioritise.

Try it

Call Twelvelabs Marengo Embed 3.0 v1.0 via Agent Command Center

One OpenAI-compatible endpoint. Routing, fallback, semantic caching, guardrails, and cost tracking come along for the ride. First 100K requests + 100K cache hits free every month.

SDK
Native Future AGI client (agentcc / @agentcc/client). Per-call metadata — provider, cost, latency, cache hit, request id — is returned on x-agentcc-* response headers, so any HTTP client can read it.
# Twelvelabs Marengo Embed 3.0 v1.0 via the Agent Command Center Python SDK
# pip install agentcc
import os
from agentcc import AgentCC

client = AgentCC(
    api_key=os.environ["AGENTCC_API_KEY"],   # from app.futureagi.com → Settings → API Keys
    base_url="https://gateway.futureagi.com/v1",
)

resp = client.chat.completions.create(
    model="bedrock/twelvelabs-marengo-embed-3-0-v1-0",
    messages=[{"role": "user", "content": "Hello, Twelvelabs Marengo Embed 3.0 v1.0!"}],
)

print(resp.choices[0].message.content)
print(f"Tokens: {resp.usage.total_tokens}")

# Per-call gateway metadata is returned on x-agentcc-* response headers.
# When you need it programmatically, use .with_raw_response to get them:
raw = client.chat.completions.with_raw_response.create(
    model="bedrock/twelvelabs-marengo-embed-3-0-v1-0",
    messages=[{"role": "user", "content": "Same call, but I want the headers."}],
)
print("Provider:", raw.headers.get("x-agentcc-provider"))
print("Latency:", raw.headers.get("x-agentcc-latency-ms"), "ms")
print("Cost:   ", raw.headers.get("x-agentcc-cost"), "USD")
print("Cache:  ", raw.headers.get("x-agentcc-cache"))
Set AGENTCC_API_KEY with a key fromapp.futureagi.com.Gateway docs ↗

Explore similar models

Twelvelabs Marengo Embed 3.0 v1.0 doesn't have a public Arena ELO score yet, so we group by provider only — quality-tier comparisons need a benchmark. Side-by-side links appear where a comparison page exists; otherwise, open the model profile.

FAQ

How much does Twelvelabs Marengo Embed 3.0 v1.0 cost?

Input is priced at $0.000000 per 1M tokens and output at $0.000000 per 1M tokens (Amazon Bedrock, last verified Oct 22, 2026).

What is the context window of Twelvelabs Marengo Embed 3.0 v1.0?

Twelvelabs Marengo Embed 3.0 v1.0 supports a 500-token context window.

Does Twelvelabs Marengo Embed 3.0 v1.0 support function calling?

Twelvelabs Marengo Embed 3.0 v1.0 does not currently advertise function-calling support. For agentic workloads, prefer a tool-calling-capable model and route via Agent Command Center for fallback.

Is Twelvelabs Marengo Embed 3.0 v1.0 good for production?

Twelvelabs Marengo Embed 3.0 v1.0 is best evaluated against your own production traces. Pipe traffic through Agent Command Center to compare it head-to-head against alternatives in shadow mode.

How can I route to Twelvelabs Marengo Embed 3.0 v1.0 with fallback?

Use Agent Command Center: a single OpenAI-compatible endpoint that supports cost-optimized routing, latency-aware retries, model fallback, and shadow traffic. Configure once, swap models without app changes.

Useful links for Twelvelabs Marengo Embed 3.0 v1.0

Official sources, independent benchmarks, and pricing aggregators — no random search-engine guesses.