Nemotron Content Safety Reasoning 4B vs GPT OSS Safeguard 120B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Nemotron Content Safety Reasoning 4B von NVIDIA und GPT OSS Safeguard 120B von OpenAI gehören zu den führenden KI-Modellen. Nemotron Content Safety Reasoning 4B bietet ein Kontextfenster von 128K Tokens bei API-Preisen von $0.05/$0.15 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.
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Nemotron Content Safety Reasoning 4B
Open-Weights-Reasoning-ModellNemotron Content Safety Reasoning 4B von NVIDIA ist ein KI-Modell für Open-Weights-Reasoning mit transparenter Gedankenketten-Verarbeitung (Chain-of-Thought) mit 128K Kontextfenster, 150 Token/s Durchsatz, einem Intelligenz-Index von 38/100
$0.05 / 1M
$0.15 / 1M
128K tokens
N/A
GPT OSS Safeguard 120B
Open-Weights-BasismodellGPT 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
$0.15 / 1M
$0.60 / 1M
131.1K tokens
41%
Wichtigste Unterschiede auf einen Blick
- Nemotron Content Safety Reasoning 4B is 3.5x cheaper for API usage (input + output tokens combined).
- GPT OSS Safeguard 120B has a larger context window (131K vs 128K tokens).
- Nemotron Content Safety Reasoning 4B is faster at 150 tok/s vs 90 tok/s.
| Feature | Nemotron Content Safety Reasoning 4B | GPT OSS Safeguard 120B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 38 | 52 |
Provider | NVIDIA | OpenAI |
Input Price Cost per million input tokens | $0.05 / 1M | $0.15 / 1M |
Output Price Cost per million output tokens | $0.15 / 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 text | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 4K | 33K |
Throughput Speed Tokens generated per second | 150 tok/s | 90 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 41% |
GPQA Diamond Graduate-level reasoning | N/A | 65% |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 1M | $0.01 / 1M |
Wer gewinnt in welcher Disziplin?
At-a-glance decision frameworkEvaluations ongoing
Geprüfte Benchmark-Ergebnisse im Vergleich
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Latenz & Antwortzeit
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Interaktiver Token-Kosten-Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Nemotron Content Safety Reasoning 4B · Strengths & Key Advantages
- 128K Kontextfenster und bis zu 4K maximale Output-Token (Arena Elo 1190)
- Hohe Generierungsgeschwindigkeit von 150 Token/Sekunde bei nur 240 ms Erstlatenz (TTFT)
- Offene Modellgewichte (4B) 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.
| Metric | Nemotron Content Safety Reasoning 4B | GPT OSS Safeguard 120B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.02 | $0.13 |
Output Tokens / Task Tokens generated per task | 4K | 40K |
Reasoning Tokens / Task Thinking tokens burned per task | 2K | 20K |
Technische Spezifikationen
Architecture, release timeline, capabilities, and modalities.
| Feature | Nemotron Content Safety Reasoning 4B | GPT OSS Safeguard 120B |
|---|---|---|
| Official API Model ID (SDK) | nemotron-content-safety-reasoning-4b | gpt-oss-safeguard-120b |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-01 | 2025-10 |
| Knowledge Cutoff | November 2025 | June 2024 |
| Architecture | Reasoning-Augmented Safety Classifier | Sparse MoE safety-classifier model |
| Parameters | 4B | 117B (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 Cloud | Unlimited (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 window | Flat rate ($0.15/1M in) across full 131K window |
| Prompt Cache Policy & TTL | Cached input at $0.01/1M tokens | Automatic prefix caching at $0.015/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | Text-only completion (No GUI action loop) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| MCP & Parallel Tool Calling | Prompt-based JSON extraction only | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Full 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 Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 2 GB VRAM (4-bit Quantized) · 9 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 Classification | Open-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 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text | text |
| Output Modalities | text | text |
Nemotron Content Safety Reasoning 4B vs GPT OSS Safeguard 120B: Schnelle Antworten (FAQ)
Which is more intelligent, Nemotron Content Safety Reasoning 4B or GPT OSS Safeguard 120B?
GPT OSS Safeguard 120B is more intelligent. It scores 52 compared with 38 on the composite Intelligence Index.
Which model is faster?
Nemotron Content Safety Reasoning 4B is faster, generating 150 tokens per second compared with 90 tokens per second.
Which model is cheaper to run?
Nemotron Content Safety Reasoning 4B 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 Content Safety Reasoning 4B has lower latency, with a time to first token of 0.24s 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 Content Safety Reasoning 4B (Optimal für Open-Weights-Reasoning mit transparenter Gedankenketten-Verarbeitung (Chain-of-Thought), 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 Content Safety Reasoning 4B liefert den höheren Token-Durchsatz und Nemotron Content Safety Reasoning 4B ist die kosteneffizienteste Wahl für große API-Workloads.
Häufig gestellte Fragen (FAQ)
Welches Modell ist besser für Coding und Reasoning – Nemotron Content Safety Reasoning 4B 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 Content Safety Reasoning 4B und GPT OSS Safeguard 120B?+
Nemotron Content Safety Reasoning 4B kostet $0.05 / $0.15 pro 1M Input-/Output-Tokens, GPT OSS Safeguard 120B liegt bei $0.15 / $0.60. Beim gewichteten Gesamtpreis ist Nemotron Content Safety Reasoning 4B günstiger.
Welches Modell hat das größere Kontextfenster und die höhere Geschwindigkeit?+
Nemotron Content Safety Reasoning 4B bietet 128K Tokens Kontext gegenüber 131.1K Tokens bei GPT OSS Safeguard 120B. Bei der Generierungsgeschwindigkeit liegt Nemotron Content Safety Reasoning 4B vorn.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
- GPT OSS Safeguard 120B vs. Llama 3.1 Nemotron Safety Guard 8B v3: Benchmarks, API-Preise & Geschwindigkeit im Vergleich (2026)Vergleich
- Nemotron 3.5 Content Safety vs. GPT OSS Safeguard 120B: Benchmarks, API-Preise & Geschwindigkeit im Vergleich (2026)Vergleich
- Nemotron 3 Content Safety vs. GPT OSS Safeguard 120B: Benchmarks, API-Preise & Geschwindigkeit im Vergleich (2026)Vergleich
- Nemotron 3 Content Safety vs. Nemotron Content Safety Reasoning 4B: Benchmarks, API-Preise & Geschwindigkeit im Vergleich (2026)Vergleich
- Nemotron Content Safety Reasoning 4B vs. Llama 3.1 Nemotron Safety Guard 8B v3: Benchmarks, API-Preise & Geschwindigkeit im Vergleich (2026)Vergleich
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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).