Nemotron 3.5 Content Safety vs GPT OSS Safeguard 120B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Nemotron 3.5 Content Safety by NVIDIA and GPT OSS Safeguard 120B by OpenAI represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Nemotron 3.5 Content Safety holds a notable pricing advantage for token ingestion. On output generation, Nemotron 3.5 Content Safety is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, GPT OSS Safeguard 120B provides an expanded memory horizon with 131K tokens. In latency-sensitive production environments, Nemotron 3.5 Content Safety leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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Nemotron 3.5 Content Safety
Modello a Pesi ApertiNemotron 3.5 Content Safety di NVIDIA è un modello di intelligenza artificiale con finestra di contesto da 128K token e tariffazione API di $0.08/1M token in input e $0.16/1M in output, ideale per coding, ragionamento e automazione aziendal
$0.08 / 1M
$0.16 / 1M
128K tokens
N/A
GPT OSS Safeguard 120B
Modello a Pesi ApertiGPT OSS Safeguard 120B di OpenAI è un modello di intelligenza artificiale con finestra di contesto da 131K token e tariffazione API di $0.15/1M token in input e $0.6/1M in output, ideale per coding, ragionamento e automazione aziendale.
$0.15 / 1M
$0.60 / 1M
131.1K tokens
41%
Differenze Chiave a Colpo d’Occhio
- 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.
| Feature | 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 |
Chi vince in ogni categoria?
At-a-glance decision frameworkEvaluations ongoing
Punteggi Benchmark Verificati
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.
Latenza e Tempo di Risposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulatore Interattivo dei Costi Token
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Nemotron 3.5 Content Safety · Strengths & Key Advantages
- Architettura Multimodal Safety & Compliance Classifier avanzata di NVIDIA con supporto fino a 128K token di contesto
- Ottimo rapporto qualità-prezzo ($0.08/1M input, $0.16/1M output) e bassa latenza
- Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Considerations & Trade-offs
- L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
- Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato
GPT OSS Safeguard 120B · Strengths & Key Advantages
- Architettura Sparse MoE safety-classifier model avanzata di OpenAI con supporto fino a 131K token di contesto
- Ottimo rapporto qualità-prezzo ($0.15/1M input, $0.6/1M output) e bassa latenza
- Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Considerations & Trade-offs
- L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
- Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato
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 |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | 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: Risposte Rapide (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.
Verdetto
Verdict & Model Selection Recommendation: Choose Nemotron 3.5 Content Safety if your priority is Live streaming moderation, social media content review, and multimodal safety guardrails. Its performance in demanding tasks justifies its operational token cost. Choose GPT OSS Safeguard 120B if your workflow prioritizes Content moderation, policy screening and safety routing layers. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Domande Frequenti (FAQ)
How does Nemotron 3.5 Content Safety compare to GPT OSS Safeguard 120B in API token pricing and cost per token?+
Nemotron 3.5 Content Safety costs $0.08 per 1M input tokens and $0.16 per 1M output tokens, whereas GPT OSS Safeguard 120B costs $0.15 input and $0.60 output. Overall, Nemotron 3.5 Content Safety is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Nemotron 3.5 Content Safety or GPT OSS Safeguard 120B?+
Nemotron 3.5 Content Safety supports a context window of 128K tokens (~96k words), while GPT OSS Safeguard 120B supports 131K tokens (~98k words). Therefore, GPT OSS Safeguard 120B can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Nemotron 3.5 Content Safety or GPT OSS Safeguard 120B?+
Nemotron 3.5 Content Safety scores at the frontier of coding benchmarks, while GPT OSS Safeguard 120B scores 41%. For complex autonomous debugging and agentic engineering, GPT OSS Safeguard 120B provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Nemotron 3.5 Content Safety and GPT OSS Safeguard 120B?+
Nemotron 3.5 Content Safety features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, GPT OSS Safeguard 120B provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Nemotron 3.5 Content Safety and GPT OSS Safeguard 120B?+
Nemotron 3.5 Content Safety generates approximately 160 tokens per second, while GPT OSS Safeguard 120B delivers 90 tokens per second. In latency-sensitive production environments, Nemotron 3.5 Content Safety provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Nemotron 3.5 Content Safety and GPT OSS Safeguard 120B to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Nemotron 3.5 Content Safety to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GPT OSS Safeguard 120B. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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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.
Fonti ufficiali: GetInfoUs Independent AI Benchmarks & Official API Specs