Llama 3.1 Nemotron Ultra 253B
nvidia/llama-3-1-nemotron-ultra-253b-v1出品方： NVIDIA · 系列: nemotron · 发布 2025-04-07 · 知识截止: 2024-12
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
能力清单
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
Agents62
- Tool calling35/35
- Structured output0/25
- Reasoning15/15
- Output token limit10/15
- Provider availability2/10
JSON / structured output45
- Structured output / JSON mode0/50
- Tool calling20/20
- Temperature control10/10
- Price-friendly for high-volume15/20
Cost efficiency58
- Headline price (log-scaled)53/95
- Has prompt-cache pricing5/5
Long context45
- Context window (100K → 2M)35/90
- Has published price for full window10/10
Production-readiness68
- Number of independent providers10/40
- 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 | $3.90 < $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 | $7.80 < $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 | $2.10 < $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 | $6.60 < $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 | $8.28 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
定价详情
推荐定价来自 nebius · nvidia/Llama-3_1-Nemotron-Ultra-253B-v1
最便宜的渠道： nvidia · Unknown 输入 + Unknown 输出
在 2 家渠道可用
| 服务商 | 服务商模型 ID | 输入 / 1M | 输出 / 1M | 上下文 | 发布日期 |
|---|---|---|---|---|---|
| Nvidia nvidia | nvidia/llama-3.1-nemotron-ultra-253b-v1 | Unknown | Unknown | 128K | 2025-04-07 |
| Nebius Token Factory nebius | nvidia/Llama-3_1-Nemotron-Ultra-253B-v1 | $0.600 | $1.80 | 128K | 2025-01-15 |
各渠道数据存在不一致
- release_date varies (span 82d): 2025-01-15, 2025-04-07
各服务商对此模型的报告值存在差异。上方「核心数据」使用代表性服务商的值；逐项请以下表为准。
Frequently asked questions
How much does Llama 3.1 Nemotron Ultra 253B cost?
Llama 3.1 Nemotron Ultra 253B costs $0.600 per 1M input tokens and $1.80 per 1M output tokens, sourced from nebius. 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 3.1 Nemotron Ultra 253B?
Llama 3.1 Nemotron Ultra 253B has a context window of 128K tokens, with a max output of 16K tokens per reply. This is the total combined size of prompt + completion.
Does Llama 3.1 Nemotron Ultra 253B support tool calling?
Yes. Llama 3.1 Nemotron Ultra 253B 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 3.1 Nemotron Ultra 253B support structured output / JSON mode?
Support for structured output / JSON-schema-constrained decoding is not reported for Llama 3.1 Nemotron Ultra 253B in our data source. Verify with NVIDIA's official documentation before relying on it in production.
Can Llama 3.1 Nemotron Ultra 253B accept image input?
No. Llama 3.1 Nemotron Ultra 253B only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is Llama 3.1 Nemotron Ultra 253B open-weight?
Yes. Llama 3.1 Nemotron Ultra 253B'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 3.1 Nemotron Ultra 253B?
If Llama 3.1 Nemotron Ultra 253B doesn't fit, consider Nemotron 3 Super, nemotron-3-nano-30b-a3b, Nemotron 3 Ultra 550B A55B. 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 are normalised into a single canonical model record and reconciled with each provider's official documentation. We re-pull daily and write any changes (price, context, capability) to the /changelog page.
Explore more
More NVIDIA models
- Nemotron 3 Super$0.20 in / $0.80 out
- nemotron-3-nano-30b-a3b$0.05 in / $0.20 out
- Nemotron 3 Ultra 550B A55B$0.50 in / $2.50 out
- 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
Capability lists this model is in
最近更新：
Pricing and capabilities are refreshed daily and reconciled against each provider's official documentation. Always verify critical production decisions with the provider directly.