GPT OSS Safeguard 120B vs Llama 5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare GPT OSS Safeguard 120B by OpenAI against Llama 5 by Meta across real-world coding, reasoning, token economics, and latency.
원하는 AI 모델 선택 및 실시간 비교
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GPT OSS Safeguard 120B
오픈 웨이트 모델OpenAI의 GPT OSS Safeguard 120B 모델은 131K 토큰 컨텍스트 윈도우와 입력 $0.15/1M 토큰, 출력 $0.6/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.15 / 1M
$0.60 / 1M
131.1K tokens
41%
Llama 5
오픈 웨이트 모델Meta의 Llama 5 모델은 5M 토큰 컨텍스트 윈도우와 입력 $0/1M 토큰, 출력 $0/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.00 / 1M
$0.00 / 1M
5M tokens
74%
한눈에 보는 핵심 차이점
- Llama 5 is 1.0x cheaper for API usage (input + output tokens combined).
- Llama 5 has a larger context window (5M vs 131K tokens).
- GPT OSS Safeguard 120B is faster at 90 tok/s vs 65 tok/s.
- Llama 5 leads on SWE-bench coding (74.0% vs 41.0%).
| Feature | GPT OSS Safeguard 120B | Llama 5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 52 | 94 |
Provider | OpenAI | Meta |
Input Price Cost per million input tokens | $0.15 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.60 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.10 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 131.1K tokens~197 A4 pages of text | 5M tokens~7,500 A4 pages of text |
Max Output Tokens Per single request | 33K | 128K |
Throughput Speed Tokens generated per second | 90 tok/s | 65 tok/s |
SWE-bench Verified Real-world software engineering | 41% | 74% |
GPQA Diamond Graduate-level reasoning | 65% | 84% |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 1M | $0.00 / 1M |
부문별 우위 모델
At-a-glance decision framework검증된 벤치마크 점수 비교
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.
지연 시간(Latency) 및 응답 속도
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
실시간 API 토큰 비용 시뮬레이터
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT OSS Safeguard 120B · Strengths & Key Advantages
- OpenAI의 첨단 Sparse MoE safety-classifier model 아키텍처와 최대 131K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0.15/1M, 출력 $0.6/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Llama 5 · Strengths & Key Advantages
- Meta의 첨단 Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) 아키텍처와 최대 5M 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0/1M, 출력 $0/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT OSS Safeguard 120B | Llama 5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.13 | $0.00 |
Output Tokens / Task Tokens generated per task | 40K | 40K |
Reasoning Tokens / Task Thinking tokens burned per task | 20K | 20K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT OSS Safeguard 120B | Llama 5 |
|---|---|---|
| Official API Model ID (SDK) | gpt-oss-safeguard-120b | meta-llama/Llama-5 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-10 | 2026-04 |
| Knowledge Cutoff | June 2024 | January 2026 |
| Architecture | Sparse MoE safety-classifier model | Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) |
| Parameters | 117B (5.1B active MoE) | 600B+ total (~40-80B active) |
| 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 | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.015/1M (no extra cache-write fee) | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| 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) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) |
| 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) · 372 GB VRAM (4-bit Quantized) · 1290 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, vision, video, audio |
| Output Modalities | text | text |
GPT OSS Safeguard 120B vs Llama 5: 핵심 요약 문답 (FAQ)
Which is more intelligent, GPT OSS Safeguard 120B or Llama 5?
Llama 5 is more intelligent. It scores 94 compared with 52 on the composite Intelligence Index.
Which model is faster?
GPT OSS Safeguard 120B is faster, generating 90 tokens per second compared with 65 tokens per second.
Which model is cheaper to run?
Llama 5 is cheaper at $0.00 per 1M tokens blended, compared with $0.10 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT OSS Safeguard 120B has lower latency, with a time to first token of 0.52s compared with 0.85s.
Which model has a larger context window?
Llama 5 has a larger context window, supporting 5M tokens compared with 131.1K tokens.
종합 평가
Choose GPT OSS Safeguard 120B for 기업 업무 자동화, 소프트웨어 개발, 자율형 ai 에이전트 및 openai 기반 프로덕션 파이프라인, or choose Llama 5 for 기업 업무 자동화, 소프트웨어 개발, 자율형 ai 에이전트 및 meta 기반 프로덕션 파이프라인.
자주 묻는 질문 (FAQ)
Which is cheaper: GPT OSS Safeguard 120B or Llama 5?+
GPT OSS Safeguard 120B costs $0.15/1M input and $0.6/1M output tokens, while Llama 5 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: GPT OSS Safeguard 120B or Llama 5?+
GPT OSS Safeguard 120B supports 131K tokens whereas Llama 5 supports 5M tokens.
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.
공식 출처: OpenAI & Meta Official Documentation