Nemotron 3.5 Content Safety vs GPT OSS Safeguard 120B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Nemotron 3.5 Content Safety de NVIDIA y GPT OSS Safeguard 120B de OpenAI compiten directamente en el ecosistema actual de IA. Nemotron 3.5 Content Safety ofrece una ventana de contexto de 128K tokens a $0.08/$0.16 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 3.5 Content Safety
Modelo Base de Pesos AbiertosNemotron 3.5 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 160 tokens/s, índice de inteligencia
$0.08 / 1M
$0.16 / 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 3.5 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.
| Característica | Nemotron 3.5 Content Safety | GPT OSS Safeguard 120B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 39 | 52 |
Provider | NVIDIA | OpenAI |
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 text | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 2K | 33K |
Throughput Speed Tokens generated per second | 160 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 | $0.02 / 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 3.5 Content Safety · Strengths & Key Advantages
- Ventana de contexto de 128K con hasta 2K tokens de salida máxima (Arena Elo 1200)
- Rendimiento ágil de 160 tokens/segundo con latencia inicial (TTFT) de solo 220 ms
- Pesos abiertos (12B) 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 3.5 Content Safety | GPT OSS Safeguard 120B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.02 | $0.13 |
Output Tokens / Task Tokens generated per task | 2K | 40K |
Reasoning Tokens / Task Thinking tokens burned per task | 1K | 20K |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | Nemotron 3.5 Content Safety | GPT OSS Safeguard 120B |
|---|---|---|
| Official API Model ID (SDK) | nemotron-3-5-content-safety | gpt-oss-safeguard-120b |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-06 | 2025-10 |
| Knowledge Cutoff | April 2026 | June 2024 |
| Architecture | Multimodal Safety & Compliance Classifier | Sparse MoE safety-classifier model |
| Parameters | 12B | 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 ($0.08/1M in) across full 128K window | Flat rate ($0.15/1M in) across full 131K window |
| Prompt Cache Policy & TTL | Cached input at $0.02/1M 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) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / 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 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, vision | text |
| Output Modalities | text | text |
Nemotron 3.5 Content Safety vs GPT OSS Safeguard 120B: Respuestas Rápidas (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.
Veredicto
Tanto Nemotron 3.5 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 3.5 Content Safety ofrece mayor velocidad de generación de tokens y Nemotron 3.5 Content Safety resulta más rentable para cargas de trabajo API de alto volumen.
Preguntas frecuentes
¿Cuál es mejor para programación y razonamiento, Nemotron 3.5 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 3.5 Content Safety frente a GPT OSS Safeguard 120B?+
Nemotron 3.5 Content Safety cuesta $0.08 / $0.16 por millón de tokens de entrada/salida, mientras que GPT OSS Safeguard 120B cuesta $0.15 / $0.60. En costo combinado, Nemotron 3.5 Content Safety es la opción más económica.
¿Qué modelo tiene mayor ventana de contexto y menor latencia?+
Nemotron 3.5 Content Safety cuenta con 128K tokens de contexto frente a 131.1K tokens de GPT OSS Safeguard 120B. En velocidad de inferencia, Nemotron 3.5 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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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).