Nvidia Models
Usage
1B
13.9K
17 of 17
- nemotron-3-ultra-550b-a55b-free
- nemotron-3.5-lightning-free
- nemotron-3.5-super-vl-free
- nemotron-3-super-120b-a12b-free
- nvidia-nemotron-3-super-120b-a12b
- nemotron-3-nano-omni-30b-a3b-reasoning-free
- nemotron-3.5-content-safety-free
- nvidia-nemotron-nano-9b-v2
- 5 more models
- nemotron-3-ultra-550b-a55b-free
- nemotron-3.5-lightning-free
- nemotron-3.5-super-vl-free
- nemotron-3-nano-omni-30b-a3b-reasoning-free
- nemotron-3-super-120b-a12b-free
- nemotron-3.5-content-safety-free
- nvidia-nemotron-3-super-120b-a12b
- nvidia-llama-3.3-nemotron-super-49b-v1.5
- 9 more models
Which models that traffic went to
- Nemotron 3 Ultra 550B A55B (free)49.3%509M
- Nemotron 3.5 Lightning (free)18.2%187M
- Nemotron 3.5 Super VL (free)17.9%185M
- Nemotron 3 Super 120B A12B (free)6.5%66.6M
- Nvidia Nemotron 3 Super 120B A12B4.3%43.8M
- Nemotron 3 Nano Omni 30B A3B (reasoning) (free)3.7%38.5M
- Nemotron 3.5 Content Safety (free)0.1%1.2M
- Nvidia Nemotron Nano 9B V2<0.1%202K
- 5 more models<0.1%107K
- Nemotron 3 Ultra 550B A55B (free)41.4%5.8K
- Nemotron 3.5 Lightning (free)18.2%2.5K
- Nemotron 3.5 Super VL (free)14.6%2K
- Nemotron 3 Nano Omni 30B A3B (reasoning) (free)9.0%1.3K
- Nemotron 3 Super 120B A12B (free)8.7%1.2K
- Nemotron 3.5 Content Safety (free)3.3%458
- Nvidia Nemotron 3 Super 120B A12B1.8%244
- Nvidia Llama 3.3 Nemotron Super 49B V1.50.6%77
- 9 more models2.5%344
All 17 Nvidia Models
Open in model list| Modalities | ||||||
|---|---|---|---|---|---|---|
| nemotron-3-nano-30b-a3b-free | Takes text, returns text. | 1.05M | FreeFree/M | — | — | — |
| nemotron-3-super-120b-a12b-free | Takes text, returns text. | 1.05M | FreeFree/M | — | — | — |
| nemotron-3-ultra-550b-a55b-free | Takes text, returns text. | 1.05M | FreeFree/M | — | — | — |
| nemotron-3.5-lightning-free | Takes text, returns text. | 1.05M | FreeFree/M | — | — | — |
| nvidia-nemotron-3-super-120b-a12b | Takes text, returns text. | 1M | $0.11$0.55/M | $0.0275/M | — | — |
| nemotron-3-nano-omni-30b-a3b-reasoning-free | Takes text, vision, audio, video, returns text. | 262K | FreeFree/M | — | — | — |
| nemotron-3.5-content-safety-free | Takes text, vision, returns text. | 131K | FreeFree/M | — | — | — |
| nemotron-nano-12b-v2-vl-free | Takes text, vision, video, returns text. | 131K | FreeFree/M | — | — | — |
| nemotron-nano-9b-v2-free | Takes text, returns text. | 131K | FreeFree/M | — | — | — |
| nemotron-3.5-super-vl-free | Takes text, vision, returns text. | — | FreeFree/M | — | — | — |
| nvidia-nemotron-nano-9b-v2 | Takes , returns text. | — | $0.044$0.176/M | — | — | — |
| deepinfra-nvidia-nemotron-3-nano-30b-a3b2 | Takes , returns text. | — | $0.066$0.264/M | — | 10 tok/s | 0.30 s |
| nvidia-nemotron-3-nano-30b-a3b | Takes , returns text. | — | $0.066$0.264/M | — | 10 tok/s | 0.30 s |
| nvidia-llama-3.3-nemotron-super-49b-v1.5 | Takes , returns text. | — | $0.11$0.44/M | — | — | — |
| nvidia-nemotron-nano-12b-v2-vl | Takes , returns text. | — | $0.22$0.66/M | — | — | — |
| nvidia/Llama-3_1-Nemotron-Ultra-253B-v1 | Takes , returns text. | — | $0.5$0.5/M | — | — | — |
| nvidia-llama-3.1-nemotron-70b-instruct | Takes , returns text. | — | $1.32$1.32/M | — | — | — |
Common Comparisons
deepinfra-nvidia-nemotron-3-nano-30b-a3b2 vs nemotron-3-nano-omni-30b-a3b-reasoning-freedeepinfra-nvidia-nemotron-3-nano-30b-a3b2 vs nemotron-3-ultra-550b-a55b-freedeepinfra-nvidia-nemotron-3-nano-30b-a3b2 vs nemotron-3.5-content-safety-freedeepinfra-nvidia-nemotron-3-nano-30b-a3b2 vs qwen3.8-max-previewdeepinfra-nvidia-nemotron-3-nano-30b-a3b2 vs kimi-k3Nvidia on AIHubMix
Which Nvidia model should I start with?
nemotron-3-nano-omni-30b-a3b-reasoning-free is free on input — the cheapest entry here that declares tool calling, and it carries a 262K context. Move up to nvidia-llama-3.1-nemotron-70b-instruct when answer quality matters more than cost, or to nemotron-3-nano-30b-a3b-free for long-form reasoning.
Which of these models reason before answering?
9 of the 17 models here declare a reasoning phase — they work through the problem before producing an answer, which helps on multi-step problems at the cost of extra output tokens. Use the Reasoning filter above the table to see them. The catalog does not record anything further about how they differ, so this page does not sort them into families.
Why are there several entries for the same model?
Because each row is a route you can call, not a model release. Some IDs name an upstream (azure-, alicloud-, cc-), some are the open-weight repository form (nvidia/…), and some differ only in capitalisation, kept so older integrations keep working.
The catalog does not carry a field saying which of those a given row is, so this page does not sort them into buckets it would have to invent. Every row shows that route’s own price, context and speed — compare those directly, and open a model to see the upstreams that serve it.
How is cached input billed?
The Cache read column is the rate for input tokens served from the prompt cache — for example nvidia-nemotron-3-super-120b-a12b bills cache hits at 25% of the input rate. Cache write is the surcharge for putting a prompt into the cache in the first place, and only a few upstreams bill it separately. A dash in either column means the catalog carries no cache rate for that model, so plan on paying the full input rate.
Do I need a separate Nvidia account?
No. One AIHubMix key covers every model on this page, and switching between them is a change to the model string — billing, rate limits, and logs stay in one place.
Start calling Nvidia in one line
One key, one endpoint, 945 models across 46 model authors.
