Llama 3.3 Nemotron Super 49B v1
nvidia/llama-3-3-nemotron-super-49b-v1Por NVIDIA · família: nemotron · lançado 2025-08-08 · data de conhecimento: 2023-12
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
Capacidades
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.
Coding54
- Tool calling40/40
- Structured output0/20
- Reasoning10/10
- Context window (100K → 1M)2/20
- Provider availability2/10
Agents67
- Tool calling35/35
- Structured output0/25
- Reasoning15/15
- Output token limit15/15
- Provider availability2/10
JSON / structured output49
- Structured output / JSON mode0/50
- Tool calling20/20
- Temperature control10/10
- Price-friendly for high-volume19/20
Cost efficiency75
- Headline price (log-scaled)75/95
- Has prompt-cache pricing0/5
Long context46
- Context window (100K → 2M)36/90
- Has published price for full window10/10
Production-readiness66
- Number of independent providers10/40
- Has published per-token price20/20
- Context window ≥ 8K15/15
- No data inconsistencies across providers6/10
- 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 | $0.82 < $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 | $1.65 < $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 | $0.38 < $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 | $1.35 < $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 | $1.89 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
Detalhes de preço
Preço recomendado de nano-gpt · nvidia/Llama-3.3-Nemotron-Super-49B-v1
Provedor mais barato: nvidia · Unknown entrada + Unknown saída
Disponível em 2 provedores
| Provedor | ID do modelo do provedor | Entrada / 1M | Saída / 1M | Contexto | Lançado |
|---|---|---|---|---|---|
| Nvidia nvidia | nvidia/llama-3_3-nemotron-super-49b-v1 | Unknown | Unknown | 131K | 2025-04-07 |
| NanoGPT nano-gpt | nvidia/Llama-3.3-Nemotron-Super-49B-v1 | $0.150 | $0.150 | 128K | 2025-08-08 |
Inconsistências de dados entre provedores
- context_window varies: 128000, 131072
- release_date varies (span 123d): 2025-04-07, 2025-08-08
Os provedores reportam valores diferentes para este modelo. Os dados rápidos acima usam um provedor representativo; consulte a tabela para detalhes por provedor.
Frequently asked questions
How much does Llama 3.3 Nemotron Super 49B v1 cost?
Llama 3.3 Nemotron Super 49B v1 costs $0.150 per 1M input tokens and $0.150 per 1M output tokens, sourced from nano-gpt. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.
What is the context window of Llama 3.3 Nemotron Super 49B v1?
Llama 3.3 Nemotron Super 49B v1 has a context window of 131K tokens, with a max output of 131K tokens per reply. This is the total combined size of prompt + completion.
Does Llama 3.3 Nemotron Super 49B v1 support tool calling?
Yes. Llama 3.3 Nemotron Super 49B v1 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 Llama 3.3 Nemotron Super 49B v1 support structured output / JSON mode?
Support for structured output / JSON-schema-constrained decoding is not reported for Llama 3.3 Nemotron Super 49B v1 in our data source. Verify with NVIDIA's official documentation before relying on it in production.
Can Llama 3.3 Nemotron Super 49B v1 accept image input?
No. Llama 3.3 Nemotron Super 49B v1 only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is Llama 3.3 Nemotron Super 49B v1 open-weight?
Yes. Llama 3.3 Nemotron Super 49B v1'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 Llama 3.3 Nemotron Super 49B v1?
If Llama 3.3 Nemotron Super 49B v1 doesn't fit, consider Nemotron 3 Super, nemotron-3-nano-30b-a3b, nvidia-nemotron-nano-9b-v2. 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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- nvidia-nemotron-nano-9b-v2$0.04 in / $0.16 out
- Llama 3.3 Nemotron Super 49B v1.5$0.05 in / $0.25 out
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Capability lists this model is in
Última atualização:
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.