GetInfoUs
Head-to-Head Model Comparison

Nemotron 3 Content Safety vs GPT OSS Safeguard 120B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs 연구팀 검토 완료최종 검토일: October 2026편집 정책
공유
Related topics (12)
AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Nemotron 3 Content Safety vs GPT OSS Safeguard 120B: Benchmarks, Pricing & Speed Comparison

NVIDIA의 Nemotron 3 Content Safety와 OpenAI의 GPT OSS Safeguard 120B는 현재 가장 주목받는 AI 모델입니다. Nemotron 3 Content Safety는 128K 토큰 컨텍스트 윈도우와 100만 토큰당 $0.05/$0.10(입력/출력)의 요금을 제공하며, GPT OSS Safeguard 120B는 131.1K 토큰 컨텍스트와 $0.15/$0.60의 요금을 제공합니다. 아래에서 벤치마크, 지연 시간 및 비용을 상세히 비교해 보세요.

1:1 모델 맞대결 비교 (VS)

원하는 AI 모델 선택 및 실시간 비교

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

Profile
Profile
Multi-Model Arena Stack:
5-Model Matrix Workbench
Popular Matchups:
NVIDIA

Nemotron 3 Content Safety

오픈 웨이트 파운데이션 모델

NVIDIA의 Nemotron 3 Content Safety 모델은 프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보에 최적화된 AI 모델로, 128K 컨텍스트 윈도우, 초당 190 토큰의 생성 속도, 종합 지능 지수 37/100점(1M 토큰당 입력 $0.05 / 출력 $0.1)을 제공합니다.

Intelligence Index37 / 100
Input Token:

$0.05 / 1M

Output Token:

$0.10 / 1M

Context Window:

128K tokens

SWE-bench Verified:

N/A

OpenAI

GPT OSS Safeguard 120B

오픈 웨이트 파운데이션 모델

OpenAI의 GPT OSS Safeguard 120B 모델은 프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보에 최적화된 AI 모델로, 131K 컨텍스트 윈도우, 초당 90 토큰의 생성 속도, 종합 지능 지수 52/100점(1M 토큰당 입력 $0.15 / 출력 $0.6)을 제공합니다.

Intelligence Index52 / 100
Input Token:

$0.15 / 1M

Output Token:

$0.60 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

41%

한눈에 보는 핵심 차이점

  • Nemotron 3 Content Safety is 4.4x cheaper for API usage (input + output tokens combined).
  • GPT OSS Safeguard 120B has a larger context window (131K vs 128K tokens).
  • Nemotron 3 Content Safety is faster at 190 tok/s vs 90 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureNemotron 3 Content SafetyGPT OSS Safeguard 120B
Intelligence Index
Composite reasoning & knowledge score (0-100)
3752
Provider
NVIDIAOpenAI
Input Price
Cost per million input tokens
$0.05 / 1M$0.15 / 1M
Output Price
Cost per million output tokens
$0.10 / 1M$0.60 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.03 / 1M$0.10 / 1M
Context Window
Maximum input + output tokens
128K tokens~192 A4 pages of text131.1K tokens~197 A4 pages of text
Max Output Tokens
Per single request
2K33K
Throughput Speed
Tokens generated per second
190 tok/s90 tok/s
SWE-bench Verified
Real-world software engineering
N/A41%
GPQA Diamond
Graduate-level reasoning
N/A65%
Cached Input Price
Prompt caching discount per 1M tokens
$0.01 / 1M$0.01 / 1M

부문별 우위 모델

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
Nemotron 3 Content Safety
4.4x cheaper monthly runtime
Speed
Nemotron 3 Content Safety
+111% faster generation
Context
GPT OSS Safeguard 120B
131.1K tokens window

검증된 벤치마크 점수 비교

Official evaluation metrics verified by independent third-party research labs.

Nemotron 3 Content SafetyGPT OSS Safeguard 120B
SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

N/Avs41%
GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

N/Avs65%
MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

N/Avs79%
LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1175ptsvs1322pts
Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

N/Avs9.2%
Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

N/Avs28%
SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

N/Avs31%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs55%
AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

N/Avs82%
Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

N/Avs69.5%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

190 tok/s vs90 tok/s

지연 시간(Latency) 및 응답 속도

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better
Nemotron 3 Content Safety0.16s
GPT OSS Safeguard 120B0.52s
Est. End-to-End Response (500 tokens)TTFT + generation time
Nemotron 3 Content Safety2.79s
GPT OSS Safeguard 120B6.08s

실시간 API 토큰 비용 시뮬레이터

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Nemotron 3 Content Safety Monthly API Bill
$0.18 / month
$0.05/1M in · $0.1/1M out
GPT OSS Safeguard 120B Monthly API Bill
$0.79 / month
$0.15/1M in · $0.6/1M out
Nemotron 3 Content Safety saves approximately $0.61 / month (77%) under this workload.4.4x Savings

Nemotron 3 Content Safety · Strengths & Key Advantages

  • 128K 컨텍스트 윈도우 및 최대 2K 출력 토큰 생성 지원 (Arena Elo 1175)
  • 초당 190 토큰의 빠른 처리 속도와 160ms에 불과한 첫 토큰 지연 시간(TTFT)
  • 오픈 웨이트 아키텍처 (8B)를 통해 vLLM, SGLang, Ollama 기반 자체 GPU 서버 구축 및 파인튜닝 가능
Considerations & Trade-offs
  • 비양자화 전체 정밀도로 로컬 서버에서 구동하려면 충분한 GPU VRAM 용량이 필요합니다
  • 초고난도 학술 연구 및 수학 증명 과제에서는 상위 대형 추론 특화 모델이 더 깊은 분석을 제공할 수 있습니다

GPT OSS Safeguard 120B · Strengths & Key Advantages

  • 131K 컨텍스트 윈도우 및 최대 33K 출력 토큰 생성 지원 (SWE-bench Verified 41% · GPQA Diamond 65%)
  • 초당 90 토큰의 빠른 처리 속도와 520ms에 불과한 첫 토큰 지연 시간(TTFT)
  • 오픈 웨이트 아키텍처 (117B (5.1B MoE))를 통해 vLLM, SGLang, Ollama 기반 자체 GPU 서버 구축 및 파인튜닝 가능
Considerations & Trade-offs
  • 비양자화 전체 정밀도로 로컬 서버에서 구동하려면 충분한 GPU VRAM 용량이 필요합니다
  • 초고난도 학술 연구 및 수학 증명 과제에서는 상위 대형 추론 특화 모델이 더 깊은 분석을 제공할 수 있습니다

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

MetricNemotron 3 Content SafetyGPT OSS Safeguard 120B
Cost per Task
Estimated USD to finish one task
$0.01$0.13
Output Tokens / Task
Tokens generated per task
2K40K
Reasoning Tokens / Task
Thinking tokens burned per task
0K20K

기술 사양 (Specifications)

Architecture, release timeline, capabilities, and modalities.

FeatureNemotron 3 Content SafetyGPT OSS Safeguard 120B
Official API Model ID (SDK)nemotron-3-content-safetygpt-oss-safeguard-120b
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI Responses API & Chat Completions v1
Release Date2026-042025-10
Knowledge CutoffFebruary 2026June 2024
ArchitectureEnterprise Safety Classifier TransformerSparse MoE safety-classifier model
Parameters8B117B (5.1B active MoE)
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed CloudUnlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)Flat rate ($0.05/1M in) across full 128K windowFlat rate ($0.15/1M in) across full 131K window
Prompt Cache Policy & TTLCached input at $0.01/1M tokensAutomatic prefix caching at $0.015/1M (no extra cache-write fee)
Computer Use / GUI AgentText-only completion (No GUI action loop)API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
MCP & Parallel Tool CallingPrompt-based JSON extraction onlyNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsNative Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning SupportFull Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
Multimodal Token BillingText BPE (~4 chars/token)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 5 GB VRAM (4-bit Quantized) · 17 GB VRAM (Native BF16)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 73 GB VRAM (4-bit Quantized) · 252 GB VRAM (Native BF16)
Safety & Frontier ClassificationOpen-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)OpenAI Preparedness Framework Audited & System Card Published
Enterprise Compliance (ZDR/HIPAA/SOC2)100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestexttext
Output Modalitiestexttext

Nemotron 3 Content Safety vs GPT OSS Safeguard 120B: 핵심 요약 문답 (FAQ)

Which is more intelligent, Nemotron 3 Content Safety or GPT OSS Safeguard 120B?

GPT OSS Safeguard 120B is more intelligent. It scores 52 compared with 37 on the composite Intelligence Index.

Which model is faster?

Nemotron 3 Content Safety is faster, generating 190 tokens per second compared with 90 tokens per second.

Which model is cheaper to run?

Nemotron 3 Content Safety is cheaper at $0.03 per 1M tokens blended, compared with $0.10 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Nemotron 3 Content Safety has lower latency, with a time to first token of 0.16s compared with 0.52s.

Which model has a larger context window?

GPT OSS Safeguard 120B has a larger context window, supporting 131.1K tokens compared with 128K tokens.

종합 평가

Nemotron 3 Content Safety(프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보, 엔터프라이즈 RAG 지식베이스 및 대규모 프로덕션 API 연동에 최적.)와 GPT OSS Safeguard 120B(프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보, 엔터프라이즈 RAG 지식베이스 및 대규모 프로덕션 API 연동에 최적.) 모두 뛰어난 성능을 자랑합니다. 고난도 추론과 코딩 벤치마크에서는 GPT OSS Safeguard 120B가 앞서며, 토큰 생성 속도에서는 Nemotron 3 Content Safety가 빠르고, 대규모 API 운영 비용 면에서는 Nemotron 3 Content Safety가 더 경제적입니다.

자주 묻는 질문 (FAQ)

코딩과 복잡한 추론에는 Nemotron 3 Content Safety와 GPT OSS Safeguard 120B 중 어느 모델이 더 나은가요?+

종합 지능 지수 및 코딩 벤치마크에서는 GPT OSS Safeguard 120B가 우위를 보입니다. 상단의 SWE-bench 및 GPQA 점수를 함께 확인해 보세요.

Nemotron 3 Content Safety와 GPT OSS Safeguard 120B의 API 요금 차이는 얼마인가요?+

Nemotron 3 Content Safety는 100만 토큰당 입력 $0.05 / 출력 $0.10이며, GPT OSS Safeguard 120B는 입력 $0.15 / 출력 $0.60입니다. 혼합 비용 기준으로는 Nemotron 3 Content Safety가 더 저렴합니다.

어느 모델이 더 큰 컨텍스트 윈도우와 빠른 속도를 제공하나요?+

컨텍스트 길이는 Nemotron 3 Content Safety가 128K 토큰, GPT OSS Safeguard 120B가 131.1K 토큰를 지원하며, 출력 속도 면에서는 Nemotron 3 Content Safety가 더 빠릅니다.

AI Terminology & Guide
Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.

공식 출처: NVIDIA 및 OpenAI 공식 API 문서, 시스템 카드 및 독립 벤치마크 평가 (2026).