Twelvelabs Marengo Embed 2.7 v1.0

Amazon Bedrock embedding

Twelvelabs Marengo Embed 2.7 v1.0 is an Amazon Bedrock embedding model.It supports a 77-token context window.Input is priced at $70.00/M tokens Route Twelvelabs Marengo Embed 2.7 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: May 12, 2026 View source ↗
Cost calculator

Estimate Twelvelabs Marengo Embed 2.7 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
$0.2100
in $0.2100 · out $0.000000
Per day
$1,050
5,000 requests
Per month
$31,959
152,188 requests

Estimate uses $70.00/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 May 12, 2026.

Input $70.00/M
Output
Per image $0.000100
Per minute (audio) $0.008400

Limits

Context window
77 tokens
Max input
77 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

Where it's strong

  • +pricing — cheaper than 99% of priced embedding models on Future AGI

Watch out for

  • !limited context — 77-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 2.7 v1.0 yet. Have one to contribute? Submit a source — citations help us prioritise.

Try it

Call Twelvelabs Marengo Embed 2.7 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 2.7 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/eu-twelvelabs-marengo-embed-2-7-v1-0",
    messages=[{"role": "user", "content": "Hello, Twelvelabs Marengo Embed 2.7 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/eu-twelvelabs-marengo-embed-2-7-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 ↗

Compare with similar models

Twelvelabs Marengo Embed 2.7 v1.0 doesn't have a public Arena ELO score yet, so we group by provider only — quality-tier comparisons need a benchmark.

FAQ

How much does Twelvelabs Marengo Embed 2.7 v1.0 cost?

Input is priced at $70.00 per 1M tokens and output at $0.000000 per 1M tokens (Amazon Bedrock, last verified May 12, 2026).

What is the context window of Twelvelabs Marengo Embed 2.7 v1.0?

Twelvelabs Marengo Embed 2.7 v1.0 supports a 77-token context window.

Does Twelvelabs Marengo Embed 2.7 v1.0 support function calling?

Twelvelabs Marengo Embed 2.7 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 2.7 v1.0 good for production?

Twelvelabs Marengo Embed 2.7 v1.0 is well-suited for pricing — cheaper than 99% of priced embedding models on Future AGI. Consider alternatives if you need limited context — 77-token window is in the bottom quartile; not ideal for long documents or large RAG.

How can I route to Twelvelabs Marengo Embed 2.7 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 2.7 v1.0

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