nemotron-3-super-120b-a12bNVIDIA
- api
- chat
- hosting
- US
- model lab
- NVIDIA
- weights
- open
- added
- March 2026
- model id
- nvidia/nemotron-3-super-120b-a12b
capabilities 2/8
- input
- free
- output
- free
- cache write
- -
- cache read
- -
worked cost
| volume | cost |
|---|---|
| 100K in + 10K out | free |
| 1M in + 100K out | free |
| 10M in + 1M out | free |
Requesty charges what the upstream provider charges, with no markup and no per-request fee. Prompt caching and smart routing cut the effective cost further on repeated context. Gateway pricing
- median ttft
- 1.84s
- median tok/s
- 72.0
- cache hit
- 80.5%
- answered
- 96.70%
Weekly aggregates from production traffic routed through Requesty to this endpoint. A metric with no qualifying sample is left out rather than filled in. Compare against the other providers
no published scores for this exact variant. region deployments and highspeed tiers usually share the base model's results
- retention
- yes
- trains on prompts
- yes
- hosted in
- US
Terms are the provider's, not Requesty's: routing a request here puts it under them. NVIDIA Privacy Policy
Need every request to stay in one jurisdiction? EU hosting routes only through EU-hosted providers.
nvidia/nemotron-3-super-120b-a12bthis id pins the nvidia deployment, with no routing and no failover. the managed id on the canonical page routes across all 1 providers instead
from openai import OpenAI client = OpenAI( api_key="YOUR_REQUESTY_API_KEY", base_url="https://router.requesty.ai/v1",) response = client.chat.completions.create( model="nvidia/nemotron-3-super-120b-a12b", messages=[ {"role": "user", "content": "Explain quantum computing in one paragraph."}, ],) print(response.choices[0].message.content)
Change the base url to https://router.requesty.ai/v1, use your Requesty key, set the model to the id above. Existing OpenAI SDK code needs no other edit, and the same key reaches every other model in the catalog. Browse all models
NVIDIA Nemotron 3 Super is a 120B-parameter open hybrid Mamba-Transformer MoE model activating just 12B parameters, with multi-token prediction for high throughput. 1M token context for long-horizon agent coherence, cross-document reasoning, and multi-step planning. Strong on AIME 2025, TerminalBench, and SWE-Bench Verified. Part of the NVIDIA Nemotron family.
questions 5
How much does nemotron-3-super-120b-a12b cost?
What is the context window of nemotron-3-super-120b-a12b?
What can nemotron-3-super-120b-a12b do?
How do I use nemotron-3-super-120b-a12b with the OpenAI SDK?
Can I run nemotron-3-super-120b-a12b through Requesty?
more from nvidia 6
| endpoint | ctx | in /M |
|---|---|---|
| nemotron-3.5-lightning-30b-a3b | 1.0M | free |
| muse-glimmer-30b | 131K | free |
| nemotron-3.5-content-safety | 131K | free |
| nemotron-3-nano-omni-30b-a3b-reasoning | 131K | free |
| nemotron-3-ultra-550b-a55b | 1.0M | free |
| nemotron-3-nano-30b-a3b | 262K | free |
call nemotron-3-super-120b-a12b through one endpoint
One key for this endpoint and 600+ other models. No markup on provider prices, automatic failover when a provider degrades, and prompt caching built in. Methodology