nvidia/Nemotron-3-Nano-30B-A3B:flexDeepInfra Inc.
- api
- chat
- hosting
- US
- model lab
- NVIDIA
- weights
- open
- added
- December 2025
- model id
- deepinfra/nvidia/Nemotron-3-Nano-30B-A3B:flex
capabilities 2/8
2 providers serve nemotron-3-nano-30b-a3b. This page is one of them. Compare all endpoints
- input
- $0.04
- output
- $0.16
- cache write
- -
- cache read
- -
worked cost
| volume | cost |
|---|---|
| 100K in + 10K out | $0.0056 |
| 1M in + 100K out | $0.0560 |
| 10M in + 1M out | $0.56 |
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
no measured traffic for this endpoint yet. pricing above is the provider's own
no published scores for this exact variant. region deployments and highspeed tiers usually share the base model's results
- retention
- none
- trains on prompts
- no
- hosted in
- US
Terms are the provider's, not Requesty's: routing a request here puts it under them. DeepInfra Privacy Policy
Need every request to stay in one jurisdiction? EU hosting routes only through EU-hosted providers.
deepinfra/nvidia/Nemotron-3-Nano-30B-A3B:flexthis id pins the deepinfra inc. deployment, with no routing and no failover. the managed id on the canonical page routes across all 2 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="deepinfra/nvidia/Nemotron-3-Nano-30B-A3B:flex", 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 Nano is an open small reasoning model optimized for fast, cost-efficient inference in agentic and production workloads. Built with a hybrid Mixture-of-Experts (MoE) and Mamba-Transformer architecture, it delivers strong multi-step reasoning, high token throughput, stable latency with predictable cost, and efficient deployment for agent-based systems. Designed for real-world AI systems where reasoning can generate significantly more tokens per prompt, Nemotron Nano reduces compu
questions 5
How much does nvidia/Nemotron-3-Nano-30B-A3B:flex cost?
What is the context window of nvidia/Nemotron-3-Nano-30B-A3B:flex?
What can nvidia/Nemotron-3-Nano-30B-A3B:flex do?
How do I use nvidia/Nemotron-3-Nano-30B-A3B:flex with the OpenAI SDK?
Can I run nvidia/Nemotron-3-Nano-30B-A3B:flex through Requesty?
more from deepinfra inc. 8
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| deepseek-v4-flash-0731:flex | 1.0M | $0.07 |
| glm-5.2 | 262K | $0.75 |
| glm-5.2:flex | 1M | $0.60 |
call nvidia/nemotron-3-nano-30b-a3b:flex 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