Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B

W&B Inference chat

Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B is a W&B Inference chat model.It supports a 1,049,000-token context window.Input is priced at $0.150/M tokens and output at $0.750/M tokens. Capabilities include function calling, vision, reasoning, prompt caching. Route Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B 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 Nvidia Nemotron 3.5 Super VL 120B A12B spend

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

~3K in / ~400 out · 5K req/day
3,000
01,049,000
400
016,000
5,000
01,000,000
cached @ $0.0200/M
Per request
$0.000750
in $0.000450 · out $0.000300
Per day
$3.75
5,000 requests
Per month
$114
152,188 requests

Estimate uses $0.1500/M input · $0.7500/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 $0.150/M
Output $0.750/M
Blended (3:1 in:out) $0.300/M
Cross-model comparable price
Cached input $0.0200/M

Context & limits

Context window
1,049,000 tokens
Max input
1,049,000 tokens
Max output
—
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 ✓ supported
  • Reasoning ✓ supported

Strengths & caveats — when to pick Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B

Data-driven from Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B's percentile within chat peers.

Where it's strong

  • +long-context tasks — context window in the top 2% of peers

Watch out for

  • No major caveats flagged from public spec.

Benchmarks pending

We haven't logged public benchmark scores for Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B yet. Have one to contribute? Submit a source — citations help us prioritise.

Try it

Call Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B 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 Nvidia Nemotron 3.5 Super VL 120B A12B 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="wandb-inference/nvidia-nvidia-nemotron-3-5-super-vl-120b-a12b",
    messages=[{"role": "user", "content": "Hello, Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B!"}],
)

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="wandb-inference/nvidia-nvidia-nemotron-3-5-super-vl-120b-a12b",
    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 Nvidia Nemotron 3.5 Super VL 120B A12B 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 Nvidia Nemotron 3.5 Super VL 120B A12B cost?

Input is priced at $0.150 per 1M tokens and output at $0.750 per 1M tokens (W&B Inference, last verified Oct 22, 2026).

What is the context window of Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B?

Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B supports a 1,049,000-token context window.

Does Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B support function calling?

Yes — Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B supports function (tool) calling.

Is Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B good for production?

Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B is well-suited for long-context tasks — context window in the top 2% of peers.

How can I route to Nvidia Nvidia Nemotron 3.5 Super VL 120B A12B 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 Nvidia Nemotron 3.5 Super VL 120B A12B

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