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Nemotron 3.5 Content Safety vs GPT OSS Safeguard 120B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Nemotron 3.5 Content Safety vs GPT OSS Safeguard 120B: Benchmarks, Pricing & Speed Comparison

Nemotron 3.5 Content Safety von NVIDIA und GPT OSS Safeguard 120B von OpenAI gehören zu den führenden KI-Modellen. Nemotron 3.5 Content Safety bietet ein Kontextfenster von 128K Tokens bei API-Preisen von $0.08/$0.16 pro 1M Tokens, während GPT OSS Safeguard 120B 131.1K Tokens Kontext zu $0.15/$0.60 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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NVIDIA

Nemotron 3.5 Content Safety

Open-Weights-Basismodell

Nemotron 3.5 Content Safety von NVIDIA ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 128K Kontextfenster, 160 Token/s Durchsatz, einem Intelligenz-Index von 39/100 und $0.08/$0.16 p

Intelligence Index39 / 100
Input Token:

$0.08 / 1M

Output Token:

$0.16 / 1M

Context Window:

128K tokens

SWE-bench Verified:

N/A

OpenAI

GPT OSS Safeguard 120B

Open-Weights-Basismodell

GPT OSS Safeguard 120B von OpenAI ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 131K Kontextfenster, 90 Token/s Durchsatz, einem Intelligenz-Index von 52/100 und $0.15/$0.6 pro 1M T

Intelligence Index52 / 100
Input Token:

$0.15 / 1M

Output Token:

$0.60 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

41%

Wichtigste Unterschiede auf einen Blick

  • Nemotron 3.5 Content Safety is 2.7x cheaper for API usage (input + output tokens combined).
  • GPT OSS Safeguard 120B has a larger context window (131K vs 128K tokens).
  • Nemotron 3.5 Content Safety is faster at 160 tok/s vs 90 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureNemotron 3.5 Content SafetyGPT OSS Safeguard 120B
Intelligence Index
Composite reasoning & knowledge score (0-100)
3952
Provider
NVIDIAOpenAI
Input Price
Cost per million input tokens
$0.08 / 1M$0.15 / 1M
Output Price
Cost per million output tokens
$0.16 / 1M$0.60 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.05 / 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
160 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
$0.02 / 1M$0.01 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
Nemotron 3.5 Content Safety
2.7x cheaper monthly runtime
Speed
Nemotron 3.5 Content Safety
+78% faster generation
Context
GPT OSS Safeguard 120B
131.1K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Nemotron 3.5 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.

1200ptsvs1322pts
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.

160 tok/s vs90 tok/s

Latenz & Antwortzeit

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 3.5 Content Safety0.22s
GPT OSS Safeguard 120B0.52s
Est. End-to-End Response (500 tokens)TTFT + generation time
Nemotron 3.5 Content Safety3.35s
GPT OSS Safeguard 120B6.08s

Interaktiver Token-Kosten-Simulator

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 3.5 Content Safety Monthly API Bill
$0.29 / month
$0.08/1M in · $0.16/1M out
GPT OSS Safeguard 120B Monthly API Bill
$0.79 / month
$0.15/1M in · $0.6/1M out
Nemotron 3.5 Content Safety saves approximately $0.49 / month (63%) under this workload.2.7x Savings

Nemotron 3.5 Content Safety · Strengths & Key Advantages

  • 128K Kontextfenster und bis zu 2K maximale Output-Token (Arena Elo 1200)
  • Hohe Generierungsgeschwindigkeit von 160 Token/Sekunde bei nur 220 ms Erstlatenz (TTFT)
  • Offene Modellgewichte (12B) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama
Considerations & Trade-offs
  • Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
  • Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

GPT OSS Safeguard 120B · Strengths & Key Advantages

  • 131K Kontextfenster und bis zu 33K maximale Output-Token (SWE-bench Verified 41% · GPQA Diamond 65%)
  • Hohe Generierungsgeschwindigkeit von 90 Token/Sekunde bei nur 520 ms Erstlatenz (TTFT)
  • Offene Modellgewichte (117B (5.1B MoE)) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama
Considerations & Trade-offs
  • Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
  • Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

Token Use & Task Economics

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

MetricNemotron 3.5 Content SafetyGPT OSS Safeguard 120B
Cost per Task
Estimated USD to finish one task
$0.02$0.13
Output Tokens / Task
Tokens generated per task
2K40K
Reasoning Tokens / Task
Thinking tokens burned per task
1K20K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureNemotron 3.5 Content SafetyGPT OSS Safeguard 120B
Official API Model ID (SDK)nemotron-3-5-content-safetygpt-oss-safeguard-120b
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI Responses API & Chat Completions v1
Release Date2026-062025-10
Knowledge CutoffApril 2026June 2024
ArchitectureMultimodal Safety & Compliance ClassifierSparse MoE safety-classifier model
Parameters12B117B (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 ($0.08/1M in) across full 128K windowFlat rate ($0.15/1M in) across full 131K window
Prompt Cache Policy & TTLCached input at $0.02/1M 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) · Vision (~258–765 tokens per image tile)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 7 GB VRAM (4-bit Quantized) · 26 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 Modalitiestext, visiontext
Output Modalitiestexttext

Nemotron 3.5 Content Safety vs GPT OSS Safeguard 120B: Schnelle Antworten (FAQ)

Which is more intelligent, Nemotron 3.5 Content Safety or GPT OSS Safeguard 120B?

GPT OSS Safeguard 120B is more intelligent. It scores 52 compared with 39 on the composite Intelligence Index.

Which model is faster?

Nemotron 3.5 Content Safety is faster, generating 160 tokens per second compared with 90 tokens per second.

Which model is cheaper to run?

Nemotron 3.5 Content Safety is cheaper at $0.05 per 1M tokens blended, compared with $0.10 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Nemotron 3.5 Content Safety has lower latency, with a time to first token of 0.22s 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.

Fazit

Sowohl Nemotron 3.5 Content Safety (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch GPT OSS Safeguard 120B (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: GPT OSS Safeguard 120B führt bei komplexem Reasoning und Coding, Nemotron 3.5 Content Safety liefert den höheren Token-Durchsatz und Nemotron 3.5 Content Safety ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Nemotron 3.5 Content Safety oder GPT OSS Safeguard 120B?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt GPT OSS Safeguard 120B vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Nemotron 3.5 Content Safety und GPT OSS Safeguard 120B?+

Nemotron 3.5 Content Safety kostet $0.08 / $0.16 pro 1M Input-/Output-Tokens, GPT OSS Safeguard 120B liegt bei $0.15 / $0.60. Beim gewichteten Gesamtpreis ist Nemotron 3.5 Content Safety günstiger.

Welches Modell hat das größere Kontextfenster und die höhere Geschwindigkeit?+

Nemotron 3.5 Content Safety bietet 128K Tokens Kontext gegenüber 131.1K Tokens bei GPT OSS Safeguard 120B. Bei der Generierungsgeschwindigkeit liegt Nemotron 3.5 Content Safety vorn.

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.

Offizielle Quellen: Offizielle API-Dokumentation von NVIDIA und OpenAI, Systemkarten und unabhängige Benchmark-Auswertungen (2026).