Nemotron 3 Nano 30B A3B (Free)
nemotron-3-nano-30b-a3b:freeNVIDIA Nemotron 3 Nano 30B A3B is a 30B-parameter MoE model optimized for highly efficient, accurate agent-style AI systems. It’s fully open — with weights, data, and training recipes available — so developers can customize and deploy it privately on their own infrastructure. (Free trial endpoints log usage and aren’t intended for sensitive or production workloads.)
- Context
- 256K tokens
- Endpoint
Pricing
Quick Start
Select an endpoint and copy a working example for this model.
from openai import OpenAI client = OpenAI( api_key="YOUR_API_KEY", base_url="https://api.apertis.ai/v1") response = client.chat.completions.create( model="nemotron-3-nano-30b-a3b:free", messages=[ {"role": "user", "content": "Hello!"} ], max_tokens=1024, temperature=0.7) print(response.choices[0].message.content) # Optional: Enable context compression to reduce token usage# response = client.chat.completions.create(# model="nemotron-3-nano-30b-a3b:free",# messages=[{"role": "user", "content": "Hello!"}],# extra_body={"compression": {"enabled": True, "model": "gpt-4.1-mini"}}# )Supported Parameters
API docsmodelmessagesmax_tokenstemperaturetop_pstreamtoolsreasoning_effortstream_optionsthinkingextra_bodyCursor IDE Model IDs
Use these namespaced identifiers in Cursor IDE to avoid conflicts with built-in models.
Compare with Other Models
See how this model compares to others from the same provider.
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- Context
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- Input
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- Output
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Nemotron 3.5 Lightning (Free)
NVIDIA Nemotron 3.5 Lightning is an open Mixture-of-Experts (MoE) model with 30B total parameters and 3B active per token, optimized for high-throughput agentic workloads and efficient inference. Its lightweight active compute and open design make it well suited for specialized agents, domain-specific customization, and scalable production deployments where speed, cost efficiency, and adaptability are key.
- Context
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- Input
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- Context
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- Input
- $0/M
- Output
- $0/M
Llama 3.3 Nemotron Super 49B V1.5
Llama-3.3-Nemotron-Super-49B-v1.5 is a 49B reasoning and chat model derived from Llama-3.3-70B-Instruct, tuned for agent workflows like RAG and tool calling with a 128K context window. It combines supervised training with multiple RL stages to improve alignment, step-by-step reasoning, and tool use, while a NAS “Puzzle” architecture reduces memory and boosts throughput so it can run on a single H100/H200. It delivers strong results across math and coding benchmarks, supports toggleable reasoning modes, and is designed for efficient, reliable agent systems and long-context retrieval where accuracy and cost balance matter.
- Context
- 131.1K
- Input
- $0.05/M
- Output
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