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.
Cambia e Confronta Qualsiasi Modello
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Live comparison: no in-depth editorial review has been published for this pair yet.
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%
Llama 5
Modello a Pesi ApertiLlama 5 di Meta è un modello di intelligenza artificiale con finestra di contesto da 5M token e tariffazione API di $0/1M token in input e $0/1M in output, ideale per coding, ragionamento e automazione aziendale.
$0.00 / 1M
$0.00 / 1M
5M tokens
74%
Differenze Chiave a Colpo d’Occhio
- 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 |
Chi vince in ogni categoria?
At-a-glance decision frameworkPunteggi 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
- 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
Llama 5 · Strengths & Key Advantages
- Architettura Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) avanzata di Meta con supporto fino a 5M token di contesto
- Ottimo rapporto qualità-prezzo ($0/1M input, $0/1M 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 | 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 |
Specifiche Tecniche
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: Risposte Rapide (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.
Verdetto
Choose GPT OSS Safeguard 120B for automazione enterprise, sviluppo software, agenti ia autonomi e pipeline di produzione openai, or choose Llama 5 for automazione enterprise, sviluppo software, agenti ia autonomi e pipeline di produzione meta.
Domande Frequenti (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.
Related reading
- Aion-3.0 vs Aion-Mini: Benchmarks, Pricing & Speed ComparisonConfronta
- Aion-3.0 vs Gemini 3.6 Flash: Benchmarks, Pricing & Speed ComparisonConfronta
- Aion-Mini vs Laguna XS 2.1: Benchmarks, Pricing & Speed ComparisonConfronta
- Aya Expanse 32B vs Llama 3.2 90B Vision: Benchmarks, Pricing & Speed ComparisonConfronta
- Aya Expanse 8B vs Command R+: Benchmarks, Pricing & Speed ComparisonConfronta
- Aya Expanse 8B vs Qwen 2.5 7B Instruct: Benchmarks, Pricing & Speed ComparisonConfronta
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: OpenAI & Meta Official Documentation