GPT OSS Safeguard 120B vs Llama 3.1 Nemotron Safety Guard 8B v3
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
GPT OSS Safeguard 120B di OpenAI e Llama 3.1 Nemotron Safety Guard 8B v3 di NVIDIA sono due modelli di IA di riferimento. GPT OSS Safeguard 120B offre una finestra di contesto di 131.1K token a $0.15/$0.60 per milione di token, mentre Llama 3.1 Nemotron Safety Guard 8B v3 mette a disposizione 128K token di contesto a $0.05/$0.10 per milione di token. Di seguito analizziamo benchmark, latenza e costi.
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GPT OSS Safeguard 120B
Modello Base a Pesi ApertiGPT OSS Safeguard 120B di OpenAI è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 131K, velocità di 90 token/s, indice di intelligenza di
$0.15 / 1M
$0.60 / 1M
131.1K tokens
41%
Llama 3.1 Nemotron Safety Guard 8B v3
Modello Base a Pesi ApertiLlama 3.1 Nemotron Safety Guard 8B v3 di NVIDIA è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 128K, velocità di 180 token/s, indice di
$0.05 / 1M
$0.10 / 1M
128K tokens
N/A
Differenze Chiave a Colpo d’Occhio
- Llama 3.1 Nemotron Safety Guard 8B v3 is 4.4x cheaper for API usage (input + output tokens combined).
- GPT OSS Safeguard 120B has a larger context window (131K vs 128K tokens).
- Llama 3.1 Nemotron Safety Guard 8B v3 is faster at 180 tok/s vs 90 tok/s.
| Feature | GPT OSS Safeguard 120B | Llama 3.1 Nemotron Safety Guard 8B v3 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 52 | 36 |
Provider | OpenAI | NVIDIA |
Input Price Cost per million input tokens | $0.15 / 1M | $0.05 / 1M |
Output Price Cost per million output tokens | $0.60 / 1M | $0.10 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.10 / 1M | $0.03 / 1M |
Context Window Maximum input + output tokens | 131.1K tokens~197 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 33K | 2K |
Throughput Speed Tokens generated per second | 90 tok/s | 180 tok/s |
SWE-bench Verified Real-world software engineering | 41% | N/A |
GPQA Diamond Graduate-level reasoning | 65% | N/A |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 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.
GPT OSS Safeguard 120B · Strengths & Key Advantages
- Finestra di contesto da 131K con capacità massima di output fino a 33K token (SWE-bench Verified 41% · GPQA Diamond 65%)
- Throughput elevato di 90 token/secondo con latenza al primo token (TTFT) di soli 520 ms
- Pesi aperti (117B (5.1B MoE)) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama
Considerations & Trade-offs
- Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
- Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità
Llama 3.1 Nemotron Safety Guard 8B v3 · Strengths & Key Advantages
- Finestra di contesto da 128K con capacità massima di output fino a 2K token (Arena Elo 1160)
- Throughput elevato di 180 token/secondo con latenza al primo token (TTFT) di soli 180 ms
- Pesi aperti (8B) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama
Considerations & Trade-offs
- Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
- Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT OSS Safeguard 120B | Llama 3.1 Nemotron Safety Guard 8B v3 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.13 | $0.01 |
Output Tokens / Task Tokens generated per task | 40K | 2K |
Reasoning Tokens / Task Thinking tokens burned per task | 20K | 0K |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT OSS Safeguard 120B | Llama 3.1 Nemotron Safety Guard 8B v3 |
|---|---|---|
| Official API Model ID (SDK) | gpt-oss-safeguard-120b | llama-3-1-nemotron-safety-guard-8b-v3 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-10 | 2025-10 |
| Knowledge Cutoff | June 2024 | August 2025 |
| Architecture | Sparse MoE safety-classifier model | Specialized Guardrail Classifier Transformer |
| Parameters | 117B (5.1B active MoE) | 8B |
| 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 ($0.15/1M in) across full 131K window | Flat rate ($0.05/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.015/1M (no extra cache-write fee) | Cached input at $0.01/1M tokens |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | Text-only completion (No GUI action loop) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Prompt-based JSON extraction only |
| Web Search Grounding & Sandbox | Native Web Search tool + Hosted Code Interpreter & File Search RAG | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| 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) · 73 GB VRAM (4-bit Quantized) · 252 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 5 GB VRAM (4-bit Quantized) · 17 GB VRAM (Native BF16) |
| Safety & Frontier Classification | OpenAI Preparedness Framework Audited & System Card Published | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| 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 |
GPT OSS Safeguard 120B vs Llama 3.1 Nemotron Safety Guard 8B v3: Risposte Rapide (FAQ)
Which is more intelligent, GPT OSS Safeguard 120B or Llama 3.1 Nemotron Safety Guard 8B v3?
GPT OSS Safeguard 120B is more intelligent. It scores 52 compared with 36 on the composite Intelligence Index.
Which model is faster?
Llama 3.1 Nemotron Safety Guard 8B v3 is faster, generating 180 tokens per second compared with 90 tokens per second.
Which model is cheaper to run?
Llama 3.1 Nemotron Safety Guard 8B v3 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?
Llama 3.1 Nemotron Safety Guard 8B v3 has lower latency, with a time to first token of 0.18s 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
Sia GPT OSS Safeguard 120B (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) sia Llama 3.1 Nemotron Safety Guard 8B v3 (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: GPT OSS Safeguard 120B guida nei benchmark di ragionamento e programmazione, Llama 3.1 Nemotron Safety Guard 8B v3 garantisce una generazione più rapida e Llama 3.1 Nemotron Safety Guard 8B v3 offre il miglior rapporto qualità-prezzo per carichi API elevati.
Domande Frequenti (FAQ)
Quale modello è migliore per coding e ragionamento, GPT OSS Safeguard 120B o Llama 3.1 Nemotron Safety Guard 8B v3?+
Nell’indice di intelligenza composito e nei benchmark di programmazione prevale GPT OSS Safeguard 120B. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.
Qual è la differenza di prezzo API tra GPT OSS Safeguard 120B e Llama 3.1 Nemotron Safety Guard 8B v3?+
GPT OSS Safeguard 120B costa $0.15 / $0.60 per milione di token di input/output, mentre Llama 3.1 Nemotron Safety Guard 8B v3 costa $0.05 / $0.10. Nel costo combinato, Llama 3.1 Nemotron Safety Guard 8B v3 risulta più economico.
Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+
GPT OSS Safeguard 120B supporta 131.1K token di contesto contro 128K token di Llama 3.1 Nemotron Safety Guard 8B v3, mentre Llama 3.1 Nemotron Safety Guard 8B v3 offre una velocità di generazione superiore.
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: Documentazione API ufficiale di OpenAI e NVIDIA, schede di sistema e benchmark indipendenti (2026).