Nvidia Nemotron nano 12B v2 (US GovCloud)

Amazon Bedrock chat Deprecates in 146d (Mar 30, 2027)
Heads-up: Amazon Bedrock has scheduled Nvidia Nemotron nano 12B v2 (US GovCloud) for deprecation on Mar 30, 2027. Plan a migration. Use Agent Command Center's model fallback routing to swap models without code changes.

Nvidia Nemotron nano 12B v2 (US GovCloud) is an Amazon Bedrock chat model.It supports a 128,000-token context windowwith up to 8,192 output tokens.Input is priced at $0.240/M tokens and output at $0.720/M tokens. Capabilities include function calling, vision. Route Nvidia Nemotron nano 12B v2 (US GovCloud) 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 Nvidia Nemotron nano 12B v2 (US GovCloud) spend

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

~3K in / ~400 out · 5K req/day
3,000
0128,000
400
08,192
5,000
01,000,000
Per request
$0.001008
in $0.000720 · out $0.000288
Per day
$5.04
5,000 requests
Per month
$153
152,188 requests

Estimate uses $0.2400/M input · $0.7200/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 $0.240/M
Output $0.720/M
Blended (3:1 in:out) $0.360/M
Cross-model comparable price

Context & limits

Context window
128,000 tokens
Max input
128,000 tokens
Max output
8,192 tokens
Modalities
vision, text

Capabilities

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

Strengths & caveats — when to pick Nvidia Nemotron nano 12B v2 (US GovCloud)

Data-driven from Nvidia Nemotron nano 12B v2 (US GovCloud)'s percentile within chat peers.

Where it's strong

  • +agentic workflows that depend on reliable tool calls
  • +document, chart, and screenshot understanding

Watch out for

  • !scheduled deprecation on Mar 30, 2027 (146 days)

Benchmarks pending

We haven't logged public benchmark scores for Nvidia Nemotron nano 12B v2 (US GovCloud) yet. Have one to contribute? Submit a source — citations help us prioritise.

Try it

Call Nvidia Nemotron nano 12B v2 (US GovCloud) 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.
# Nvidia Nemotron nano 12B v2 (US GovCloud) 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/us-gov-nvidia-nemotron-nano-12b-v2",
    messages=[{"role": "user", "content": "Hello, Nvidia Nemotron nano 12B v2 (US GovCloud)!"}],
)

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/us-gov-nvidia-nemotron-nano-12b-v2",
    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

Nvidia Nemotron nano 12B v2 (US GovCloud) 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 Nvidia Nemotron nano 12B v2 (US GovCloud) cost?

Input is priced at $0.240 per 1M tokens and output at $0.720 per 1M tokens (Amazon Bedrock, last verified Oct 22, 2026).

What is the context window of Nvidia Nemotron nano 12B v2 (US GovCloud)?

Nvidia Nemotron nano 12B v2 (US GovCloud) supports a 128,000-token context window with up to 8,192 output tokens.

Does Nvidia Nemotron nano 12B v2 (US GovCloud) support function calling?

Yes — Nvidia Nemotron nano 12B v2 (US GovCloud) supports function (tool) calling.

Is Nvidia Nemotron nano 12B v2 (US GovCloud) good for production?

Nvidia Nemotron nano 12B v2 (US GovCloud) is well-suited for agentic workflows that depend on reliable tool calls and document, chart, and screenshot understanding. Consider alternatives if you need scheduled deprecation on Mar 30, 2027 (146 days).

How can I route to Nvidia Nemotron nano 12B v2 (US GovCloud) 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 Nvidia Nemotron nano 12B v2 (US GovCloud)

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