Nemotron 3 Content Safety vs GPT OSS Safeguard 120B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Nemotron 3 Content Safety de NVIDIA y GPT OSS Safeguard 120B de OpenAI compiten directamente en el ecosistema actual de IA. Nemotron 3 Content Safety ofrece una ventana de contexto de 128K tokens a $0.05/$0.10 por millón de tokens (entrada/salida), mientras que GPT OSS Safeguard 120B ofrece 131.1K tokens de contexto a $0.15/$0.60 por millón de tokens. Analizamos sus benchmarks, latencia y costos reales.
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Nemotron 3 Content Safety
Modelo Base de Pesos AbiertosNemotron 3 Content Safety de NVIDIA es un modelo de IA diseñado para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, con ventana de contexto de 128K, velocidad de 190 tokens/s, índice de inteligencia d
$0.05 / 1M
$0.10 / 1M
128K tokens
N/A
GPT OSS Safeguard 120B
Modelo Base de Pesos AbiertosGPT OSS Safeguard 120B de OpenAI es un modelo de IA diseñado para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, con ventana de contexto de 131K, velocidad de 90 tokens/s, índice de inteligencia de 52
$0.15 / 1M
$0.60 / 1M
131.1K tokens
41%
Diferencias Clave de un Vistazo
- 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.
| Característica | Nemotron 3 Content Safety | GPT OSS Safeguard 120B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 37 | 52 |
Provider | NVIDIA | OpenAI |
Input Price Cost per million input tokens | $0.05 / 1M | $0.15 / 1M |
Output Price Cost per million output tokens | $0.10 / 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 text | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 2K | 33K |
Throughput Speed Tokens generated per second | 190 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 |
¿Quién gana en cada área?
At-a-glance decision frameworkEvaluations ongoing
Puntuaciones de Benchmarks Verificadas
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.
Latencia y Tiempo de Respuesta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interactivo de Costos de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Nemotron 3 Content Safety · Strengths & Key Advantages
- Ventana de contexto de 128K con hasta 2K tokens de salida máxima (Arena Elo 1175)
- Rendimiento ágil de 190 tokens/segundo con latencia inicial (TTFT) de solo 160 ms
- Pesos abiertos (8B) listos para autoalojamiento local o en nube privada mediante vLLM y Ollama
Considerations & Trade-offs
- Requiere infraestructura GPU dedicada con suficiente memoria VRAM para inferencia local sin cuantización
- En demostraciones científicas extremas, los modelos de razonamiento pesado de gama alta ofrecen mayor profundidad
GPT OSS Safeguard 120B · Strengths & Key Advantages
- Ventana de contexto de 131K con hasta 33K tokens de salida máxima (SWE-bench Verified 41% · GPQA Diamond 65%)
- Rendimiento ágil de 90 tokens/segundo con latencia inicial (TTFT) de solo 520 ms
- Pesos abiertos (117B (5.1B MoE)) listos para autoalojamiento local o en nube privada mediante vLLM y Ollama
Considerations & Trade-offs
- Requiere infraestructura GPU dedicada con suficiente memoria VRAM para inferencia local sin cuantización
- En demostraciones científicas extremas, los modelos de razonamiento pesado de gama alta ofrecen mayor profundidad
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Nemotron 3 Content Safety | GPT OSS Safeguard 120B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.01 | $0.13 |
Output Tokens / Task Tokens generated per task | 2K | 40K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 20K |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | Nemotron 3 Content Safety | GPT OSS Safeguard 120B |
|---|---|---|
| Official API Model ID (SDK) | nemotron-3-content-safety | gpt-oss-safeguard-120b |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-04 | 2025-10 |
| Knowledge Cutoff | February 2026 | June 2024 |
| Architecture | Enterprise Safety Classifier Transformer | Sparse MoE safety-classifier model |
| Parameters | 8B | 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) · 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 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 3 Content Safety vs GPT OSS Safeguard 120B: Respuestas Rápidas (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.
Veredicto
Tanto Nemotron 3 Content Safety (Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.) como GPT OSS Safeguard 120B (Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.) destacan en producción: GPT OSS Safeguard 120B lidera en inteligencia general y programación, Nemotron 3 Content Safety ofrece mayor velocidad de generación de tokens y Nemotron 3 Content Safety resulta más rentable para cargas de trabajo API de alto volumen.
Preguntas frecuentes
¿Cuál es mejor para programación y razonamiento, Nemotron 3 Content Safety o GPT OSS Safeguard 120B?+
En las pruebas de inteligencia compuesta y resolución de código, GPT OSS Safeguard 120B obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.
¿Cuánto cuesta la API de Nemotron 3 Content Safety frente a GPT OSS Safeguard 120B?+
Nemotron 3 Content Safety cuesta $0.05 / $0.10 por millón de tokens de entrada/salida, mientras que GPT OSS Safeguard 120B cuesta $0.15 / $0.60. En costo combinado, Nemotron 3 Content Safety es la opción más económica.
¿Qué modelo tiene mayor ventana de contexto y menor latencia?+
Nemotron 3 Content Safety cuenta con 128K tokens de contexto frente a 131.1K tokens de GPT OSS Safeguard 120B. En velocidad de inferencia, Nemotron 3 Content Safety ofrece un rendimiento superior.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
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- Nemotron 3 Content Safety vs. Nemotron Content Safety Reasoning 4B: Comparativa de Benchmarks, Precios API y Velocidad (2026)Comparar
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Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.
Fuentes: Documentación oficial de API de NVIDIA y OpenAI, tarjetas de sistema y benchmarks independientes (2026).