Nemotron 3 Super
nvidia/nemotron-3-super-120b-a12b提供： NVIDIA · ファミリー: nemotron · リリース 2026-03-11 · 知識カットオフ: 2026-02
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
機能一覧
Model fit scores
0–100 · higher is betterThese scores reward declared capabilities, context size, price and provider availability — they are not benchmark results. Use them as a directional signal alongside your own evaluation.
Coding66
- Tool calling40/40
- Structured output0/20
- Reasoning10/10
- Context window (100K → 1M)8/20
- Provider availability8/10
Agents73
- Tool calling35/35
- Structured output0/25
- Reasoning15/15
- Output token limit15/15
- Provider availability8/10
JSON / structured output48
- Structured output / JSON mode0/50
- Tool calling20/20
- Temperature control10/10
- Price-friendly for high-volume18/20
Cost efficiency62
- Headline price (log-scaled)62/95
- Has prompt-cache pricing0/5
Long context61
- Context window (100K → 2M)51/90
- Has published price for full window10/10
Production-readiness98
- Number of independent providers40/40
- Has published per-token price20/20
- Context window ≥ 8K15/15
- No data inconsistencies across providers8/10
- Official model (not derivative)15/15
Cost Efficiency Index
Open full calculator →Estimated cost using the recommended provider's headline rate. Each scenario fixes average input/output tokens — the assumptions are shown in the third column.
| Scenario | Cost | Assumption |
|---|---|---|
RAG answer per 1,000 RAG answers | $1.40 < $0.01 per request | 5K input tokens (query + 4 retrieved chunks of ~1K each) and a 500-token answer. Typical SaaS knowledge-base bot. |
Support ticket triage per 10,000 tickets | $2.80 < $0.01 per request | 1K input tokens (ticket body + system prompt) and a 100-token JSON classification reply. High-volume customer support. |
Data extraction per 1,000 documents | $0.80 < $0.01 per request | 2K input tokens (a single document page) and a 500-token JSON extraction. ETL / invoice / form pipelines. |
Code review per 1,000 PRs | $2.40 < $0.01 per request | 8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads. |
Agent step per 1,000 steps | $2.88 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
料金詳細
推奨料金 (提供元): nvidia · nvidia/nemotron-3-super-120b-a12b
最安プロバイダー: openrouter · $0.100 入力 + $0.500 出力
8 か所で利用可能
| プロバイダー | プロバイダーモデルID | 入力 / 1M | 出力 / 1M | コンテキスト | リリース日 |
|---|---|---|---|---|---|
| Nvidia nvidia | nvidia/nemotron-3-super-120b-a12b | $0.200 | $0.800 | 262K | 2026-03-11 |
| OpenRouter openrouter | nvidia/nemotron-3-super-120b-a12b | $0.100 | $0.500 | 262K | 2026-03-11 |
| Vercel AI Gateway vercel | nvidia/nemotron-3-super-120b-a12b | $0.150 | $0.650 | 256K | 2026-03-18 |
| Perplexity Agent perplexity-agent | nvidia/nemotron-3-super-120b-a12b | $0.250 | $2.50 | 1M | 2026-03-11 |
| Weights & Biases wandb | nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8 | $0.200 | $0.800 | 262K | 2026-03-11 |
| Kilo Gateway kilo | nvidia/nemotron-3-super-120b-a12b | $0.100 | $0.500 | 262K | 2026-03-11 |
| Nebius Token Factory nebius | nvidia/nemotron-3-super-120b-a12b | $0.300 | $0.900 | 256K | 2026-03-11 |
| Cortecs cortecs | nemotron-3-super-120b-a12b | $0.266 | $0.799 | 262K | 2026-03-11 |
プロバイダー間でデータに差異
- context_window varies: 1000000, 256000, 262144
プロバイダーごとに本モデルの値が異なります。上部の「主要数値」は代表的プロバイダーを使用しています。詳細は表をご確認ください。
Frequently asked questions
How much does Nemotron 3 Super cost?
Nemotron 3 Super costs $0.200 per 1M input tokens and $0.800 per 1M output tokens, sourced from nvidia. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.
What is the context window of Nemotron 3 Super?
Nemotron 3 Super has a context window of 262K tokens, with a max output of 262K tokens per reply. This is the total combined size of prompt + completion.
Does Nemotron 3 Super support tool calling?
Yes. Nemotron 3 Super supports tool calling (function calling). This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.
Does Nemotron 3 Super support structured output / JSON mode?
Support for structured output / JSON-schema-constrained decoding is not reported for Nemotron 3 Super in our data source. Verify with NVIDIA's official documentation before relying on it in production.
Can Nemotron 3 Super accept image input?
No. Nemotron 3 Super only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is Nemotron 3 Super open-weight?
Yes. Nemotron 3 Super's weights are publicly available, so you can self-host or fine-tune. Note that open weights ≠ open source — the training data and code are typically not released.
What are the best alternatives to Nemotron 3 Super?
If Nemotron 3 Super doesn't fit, consider nemotron-3-nano-30b-a3b, nvidia-nemotron-nano-9b-v2, Llama 3.3 Nemotron Super 49B v1.5. Each one targets the same use case — see the Related links below for direct head-to-head pages.
Where does this data come from?
All numbers come from the public models.dev API and are normalised into a single canonical model record. We re-pull daily and write any changes (price, context, capability) to the /changelog page.
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- Llama 3.3 Nemotron Super 49B v1.5$0.05 in / $0.25 out
- Llama 3.3 Nemotron Super 49B v1$0.15 in / $0.15 out
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Capability lists this model is in
最終更新：
Data is sourced from models.dev and normalized for comparison. Prices and capabilities may change. Always verify critical production decisions with the provider's official documentation.