Nvidia Nvidia Nemotron 3.5 Super VL

Together AI chat

Nvidia Nvidia Nemotron 3.5 Super VL is a Together AI chat model.It supports a 262,144-token context window.Input is priced at $0.150/M tokens and output at $0.500/M tokens. Capabilities include function calling, vision, reasoning, prompt caching. Route Nvidia Nvidia Nemotron 3.5 Super VL 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 spend

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

~3K in / ~400 out · 5K req/day
3,000
0262,144
400
016,000
5,000
01,000,000
cached @ $0.0200/M
Per request
$0.000650
in $0.000450 · out $0.000200
Per day
$3.25
5,000 requests
Per month
$98.92
152,188 requests

Estimate uses $0.1500/M input · $0.5000/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 $0.500/M
Blended (3:1 in:out) $0.238/M
Cross-model comparable price
Cached input $0.0200/M

Context & limits

Context window
262,144 tokens
Max input
262,144 tokens
Max output
—
Modalities
vision, text

Capabilities

  • Function calling ✓ supported
  • Parallel tool calls ✓ supported
  • 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

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

Where it's strong

  • +parallel tool calls — only 18% of chat models on Future AGI advertise this

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 yet. Have one to contribute? Submit a source — citations help us prioritise.

Try it

Call Nvidia Nvidia Nemotron 3.5 Super VL 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 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="together-ai/nvidia-nvidia-nemotron-3-5-super-vl",
    messages=[{"role": "user", "content": "Hello, Nvidia Nvidia Nemotron 3.5 Super VL!"}],
)

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="together-ai/nvidia-nvidia-nemotron-3-5-super-vl",
    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 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 cost?

Input is priced at $0.150 per 1M tokens and output at $0.500 per 1M tokens (Together AI, last verified Oct 22, 2026).

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

Nvidia Nvidia Nemotron 3.5 Super VL supports a 262,144-token context window.

Does Nvidia Nvidia Nemotron 3.5 Super VL support function calling?

Yes — Nvidia Nvidia Nemotron 3.5 Super VL supports function (tool) calling, including parallel tool calls.

Is Nvidia Nvidia Nemotron 3.5 Super VL good for production?

Nvidia Nvidia Nemotron 3.5 Super VL is well-suited for parallel tool calls — only 18% of chat models on Future AGI advertise this.

How can I route to Nvidia Nvidia Nemotron 3.5 Super VL 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

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