Twelvelabs Marengo Embed 2.7 v1.0 (EU)
Amazon Bedrock embedding Deprecates in 26d (Nov 30, 2026)model fallback routing to swap models without code changes.
Twelvelabs Marengo Embed 2.7 v1.0 (EU) is an Amazon Bedrock embedding model.It supports a 77-token context window. Route Twelvelabs Marengo Embed 2.7 v1.0 (EU) via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
Estimate Twelvelabs Marengo Embed 2.7 v1.0 (EU) spend
Pick a workload, fine-tune the sliders, and see the monthly bill.
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
- 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
Strengths & caveats — when to pick Twelvelabs Marengo Embed 2.7 v1.0 (EU)
Data-driven from Twelvelabs Marengo Embed 2.7 v1.0 (EU)'s percentile within embedding peers.
Where it's strong
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)
- !scheduled deprecation on Nov 30, 2026 (26 days)
Benchmarks pending
We haven't logged public benchmark scores for Twelvelabs Marengo Embed 2.7 v1.0 (EU) yet. Have one to contribute? Submit a source — citations help us prioritise.
Call Twelvelabs Marengo Embed 2.7 v1.0 (EU) 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.
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 (EU) 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 (EU)!"}],
)
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"))AGENTCC_API_KEY with a key fromapp.futureagi.com.Gateway docs ↗Explore similar models
Twelvelabs Marengo Embed 2.7 v1.0 (EU) 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.
- Cohere Embed v4.0Amazon Bedrock · $0.120/M in · $0.000000/M out · 128,000 ctxView model →
- Cohere Embed v4.0 (Global)Amazon Bedrock · $0.120/M in · $0.000000/M out · 128,000 ctxView model →
- Amazon Titan Embed G1 Text 02Amazon Bedrock · $0.1000/M in · $0.000000/M out · 8,192 ctxView model →
- Amazon Titan Embed Text v1Amazon Bedrock · $0.1000/M in · $0.000000/M out · 8,192 ctxView model →
FAQ
How much does Twelvelabs Marengo Embed 2.7 v1.0 (EU) 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 2.7 v1.0 (EU)?
Twelvelabs Marengo Embed 2.7 v1.0 (EU) supports a 77-token context window.
Does Twelvelabs Marengo Embed 2.7 v1.0 (EU) support function calling?
Twelvelabs Marengo Embed 2.7 v1.0 (EU) 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 (EU) good for production?
Twelvelabs Marengo Embed 2.7 v1.0 (EU) 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 2.7 v1.0 (EU) 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 (EU)
Official sources, independent benchmarks, and pricing aggregators — no random search-engine guesses.
Third-party evals — verify the marketing.
Cross-check our number against the rest of the ecosystem.