Nvidia Nvidia Nemotron nano 9B v2
Together AI chat Deprecated 252d agomodel fallback routing to swap models without code changes.
Nvidia Nvidia Nemotron nano 9B v2 is a Together AI chat model.It supports a 131,072-token context window.Input is priced at $0.0600/M tokens and output at $0.250/M tokens. Route Nvidia Nvidia Nemotron nano 9B v2 via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
Estimate Nvidia Nvidia Nemotron nano 9B v2 spend
Pick a workload, fine-tune the sliders, and see the monthly bill.
Estimate uses $0.0600/M input · $0.2500/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 | $0.0600/M | |
| Output | $0.250/M | |
| Blended (3:1 in:out) | $0.108/M Cross-model comparable price |
Context & limits
- Context window
- 131,072 tokens
- Max input
- 131,072 tokens
- Max output
- —
- Modalities
- 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 Nvidia Nvidia Nemotron nano 9B v2
Data-driven from Nvidia Nvidia Nemotron nano 9B v2's percentile within chat peers.
Where it's strong
Watch out for
- !high cost — input + output rates are in the top 91% of priced chat peers; consider a cheaper sibling for high-volume workloads
- !agentic workflows — no advertised function-calling; use a tool-capable model and route via Agent Command Center for fallback
- !strict structured output — no JSON-schema enforcement, expect retry loops
- !already deprecated — provider stopped accepting new traffic 252 days ago
Benchmarks pending
We haven't logged public benchmark scores for Nvidia Nvidia Nemotron nano 9B v2 yet. Have one to contribute? Submit a source — citations help us prioritise.
Call Nvidia Nvidia Nemotron nano 9B v2 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.# Nvidia Nvidia Nemotron nano 9B v2 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-nano-9b-v2",
messages=[{"role": "user", "content": "Hello, Nvidia Nvidia Nemotron nano 9B v2!"}],
)
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-nano-9b-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"))AGENTCC_API_KEY with a key fromapp.futureagi.com.Gateway docs ↗Same model on other providers
nvidia-nvidia-nemotron-nano-9b-v2 is also available via 1 other route. Pricing, regions, and capabilities can differ — compare before routing production traffic.
| Provider | Input / 1M | Output / 1M | Verified |
|---|---|---|---|
| DeepInfra | $0.0400/M | $0.160/M | Oct 22, 2026 |
Explore similar models
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FAQ
How much does Nvidia Nvidia Nemotron nano 9B v2 cost?
Input is priced at $0.0600 per 1M tokens and output at $0.250 per 1M tokens (Together AI, last verified Oct 22, 2026).
What is the context window of Nvidia Nvidia Nemotron nano 9B v2?
Nvidia Nvidia Nemotron nano 9B v2 supports a 131,072-token context window.
Does Nvidia Nvidia Nemotron nano 9B v2 support function calling?
Nvidia Nvidia Nemotron nano 9B v2 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 Nvidia Nvidia Nemotron nano 9B v2 good for production?
Nvidia Nvidia Nemotron nano 9B v2 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 Nvidia Nvidia Nemotron nano 9B v2 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 nano 9B v2
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.