llama-3.1-nemotron-safety-guard-8b-v3
nvidia/llama-3-1-nemotron-safety-guard-8b-v3提供： NVIDIA · ファミリー: llama · リリース 2025-10-28 · 知識カットオフ: 2023-12
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.
Coding4
- Tool calling0/40
- Structured output0/20
- Reasoning0/10
- Context window (100K → 1M)2/20
- Provider availability2/10
Agents2
- Tool calling0/35
- Structured output0/25
- Reasoning0/15
- Output token limit0/15
- Provider availability2/10
JSON / structured output20
- Structured output / JSON mode0/50
- Tool calling0/20
- Temperature control0/10
- Price-friendly for high-volume20/20
Cost efficiency95
- Headline price (log-scaled)95/95
- Has prompt-cache pricing0/5
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 | $0.06 < $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 | $0.11 < $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 | $0.03 < $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 | $0.09 < $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 | $0.13 < $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): vultr · nvidia/Llama-3.1-Nemotron-Safety-Guard-8B-v3
最安プロバイダー: nvidia · Unknown 入力 + Unknown 出力
2 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/llama-3_1-nemotron-safety-guard-8b-v3 | Unknown | Unknown | 128K | 2025-10-28 |
| Vultr vultr | nvidia/Llama-3.1-Nemotron-Safety-Guard-8B-v3 | $0.010 | $0.010 | 8K | 2025-10-28 |
プロバイダー間でデータに差異
- context_window varies: 128000, 8192
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 llama-3.1-nemotron-safety-guard-8b-v3 cost?
llama-3.1-nemotron-safety-guard-8b-v3 costs $0.010 per 1M input tokens and $0.010 per 1M output tokens, sourced from vultr. 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-safety-guard-8b-v3?
llama-3.1-nemotron-safety-guard-8b-v3 has a context window of 128K tokens, with a max output of 4K tokens per reply. This is the total combined size of prompt + completion.
Does llama-3.1-nemotron-safety-guard-8b-v3 support tool calling?
No. llama-3.1-nemotron-safety-guard-8b-v3 does not support tool calling (function calling). If your workflow requires it, look at the /capabilities/tool-calling list for alternatives.
Does llama-3.1-nemotron-safety-guard-8b-v3 support structured output / JSON mode?
Support for structured output / JSON-schema-constrained decoding is not reported for llama-3.1-nemotron-safety-guard-8b-v3 in our data source. Verify with NVIDIA's official documentation before relying on it in production.
Can llama-3.1-nemotron-safety-guard-8b-v3 accept image input?
No. llama-3.1-nemotron-safety-guard-8b-v3 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-safety-guard-8b-v3 open-weight?
Yes. llama-3.1-nemotron-safety-guard-8b-v3'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-safety-guard-8b-v3?
If llama-3.1-nemotron-safety-guard-8b-v3 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
- Nemotron 3 Nano Omni$0.13 in / $0.38 out
Capability lists this model is in
最終更新：
Pricing and capabilities are refreshed daily and reconciled against each provider's official documentation. Always verify critical production decisions with the provider directly.