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 dari NVIDIA dan GPT OSS Safeguard 120B dari OpenAI adalah dua model AI unggulan. Nemotron 3.5 Content Safety menawarkan jendela konteks 128K token dengan harga $0.08/$0.16 per 1 juta token, sedangkan GPT OSS Safeguard 120B menyediakan konteks 131.1K token seharga $0.15/$0.60 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.
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Nemotron 3.5 Content Safety
Model Dasar Bobot TerbukaNemotron 3.5 Content Safety dari NVIDIA adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 128K, kecepatan 160 token/detik, indeks kecerdasan 39/100, ser
$0.08 / 1M
$0.16 / 1M
128K tokens
N/A
GPT OSS Safeguard 120B
Model Dasar Bobot TerbukaGPT OSS Safeguard 120B dari OpenAI adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 131K, kecepatan 90 token/detik, indeks kecerdasan 52/100, serta tar
$0.15 / 1M
$0.60 / 1M
131.1K tokens
41%
Perbedaan Utama Sekilas
- 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 |
Siapa Unggul di Mana?
At-a-glance decision frameworkEvaluations ongoing
Skor Benchmark Terverifikasi
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.
Latensi & Waktu Respons
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulator Biaya Token Interaktif
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Nemotron 3.5 Content Safety · Strengths & Key Advantages
- Jendela konteks 128K dengan kapasitas output maksimum hingga 2K token (Arena Elo 1200)
- Kecepatan generasi tinggi 160 token/detik dengan latensi token pertama (TTFT) hanya 220 ms
- Bobot terbuka (12B) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama
Considerations & Trade-offs
- Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
- Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi
GPT OSS Safeguard 120B · Strengths & Key Advantages
- Jendela konteks 131K dengan kapasitas output maksimum hingga 33K token (SWE-bench Verified 41% · GPQA Diamond 65%)
- Kecepatan generasi tinggi 90 token/detik dengan latensi token pertama (TTFT) hanya 520 ms
- Bobot terbuka (117B (5.1B MoE)) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama
Considerations & Trade-offs
- Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
- Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi
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 |
Spesifikasi Teknis
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: Jawaban Cepat (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.
Kesimpulan
Baik Nemotron 3.5 Content Safety (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun GPT OSS Safeguard 120B (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: GPT OSS Safeguard 120B unggul dalam penalaran dan pemrograman, Nemotron 3.5 Content Safety memberikan kecepatan generasi token lebih tinggi, dan Nemotron 3.5 Content Safety lebih hemat biaya untuk penggunaan API skala besar.
Pertanyaan Umum (FAQ)
Mana yang lebih baik untuk coding dan penalaran, Nemotron 3.5 Content Safety atau GPT OSS Safeguard 120B?+
Dalam indeks kecerdasan gabungan dan benchmark coding, GPT OSS Safeguard 120B memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.
Berapa perbandingan harga API Nemotron 3.5 Content Safety vs GPT OSS Safeguard 120B?+
Nemotron 3.5 Content Safety mengenakan biaya $0.08 / $0.16 per 1M token input/output, sedangkan GPT OSS Safeguard 120B sebesar $0.15 / $0.60. Secara keseluruhan, Nemotron 3.5 Content Safety lebih hemat biaya.
Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+
Nemotron 3.5 Content Safety mendukung konteks 128K token dibandingkan 131.1K token pada GPT OSS Safeguard 120B, sementara Nemotron 3.5 Content Safety unggul dalam kecepatan output token per detik.
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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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.
Sumber resmi: Dokumentasi API resmi NVIDIA & OpenAI serta evaluasi benchmark independen (2026).