Nemotron 3 Super 120B A12B vs GPT-OSS 120B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Nemotron 3 Super 120B A12B di NVIDIA e GPT-OSS 120B di OpenAI sono due modelli di IA di riferimento. Nemotron 3 Super 120B A12B offre una finestra di contesto di 262.1K token a $0.30/$0.80 per milione di token, mentre GPT-OSS 120B mette a disposizione 131.1K token di contesto a $0.15/$0.60 per milione di token. Di seguito analizziamo benchmark, latenza e costi.
Cambia e Confronta Qualsiasi Modello
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Nemotron 3 Super 120B A12B
IA Agentica e Strumenti AutonomiNemotron 3 Super 120B A12B di NVIDIA è un modello di IA progettato per i workflow agentici autonomi, il tool calling e l’automazione multi-step, con finestra di contesto da 262K, velocità di 85 token/s, indice di intelligenza di 53/100 e ta
$0.30 / 1M
$0.80 / 1M
262.1K tokens
51%
GPT-OSS 120B
Modello Base a Pesi ApertiGPT-OSS 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 95 token/s, indice di intelligenza di 56/100 e t
$0.15 / 1M
$0.60 / 1M
131.1K tokens
45.5%
Differenze Chiave a Colpo d’Occhio
- GPT-OSS 120B is 1.6x cheaper for API usage (input + output tokens combined).
- Nemotron 3 Super 120B A12B has a larger context window (262K vs 131K tokens).
- GPT-OSS 120B is faster at 95 tok/s vs 85 tok/s.
- Nemotron 3 Super 120B A12B leads on SWE-bench coding (51.0% vs 45.5%).
| Feature | Nemotron 3 Super 120B A12B | GPT-OSS 120B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 53 | 56 |
Provider | NVIDIA | OpenAI |
Input Price Cost per million input tokens | $0.30 / 1M | $0.15 / 1M |
Output Price Cost per million output tokens | $0.80 / 1M | $0.60 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.18 / 1M | $0.10 / 1M |
Context Window Maximum input + output tokens | 262.1K tokens~393 A4 pages of text | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 33K | 33K |
Throughput Speed Tokens generated per second | 85 tok/s | 95 tok/s |
SWE-bench Verified Real-world software engineering | 51% | 45.5% |
GPQA Diamond Graduate-level reasoning | 64.5% | 68.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.06 / 1M | $0.01 / 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.
Nemotron 3 Super 120B A12B · Strengths & Key Advantages
- Finestra di contesto da 262K con capacità massima di output fino a 33K token (SWE-bench Verified 51% · GPQA Diamond 64.5%)
- Throughput elevato di 85 token/secondo con latenza al primo token (TTFT) di soli 460 ms
- Pesi aperti (120B (12B 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
GPT-OSS 120B · Strengths & Key Advantages
- Finestra di contesto da 131K con capacità massima di output fino a 33K token (SWE-bench Verified 45.5% · GPQA Diamond 68.5%)
- Throughput elevato di 95 token/secondo con latenza al primo token (TTFT) di soli 480 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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Nemotron 3 Super 120B A12B | GPT-OSS 120B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.38 | $0.14 |
Output Tokens / Task Tokens generated per task | 38K | 45K |
Reasoning Tokens / Task Thinking tokens burned per task | 18K | 22K |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | Nemotron 3 Super 120B A12B | GPT-OSS 120B |
|---|---|---|
| Official API Model ID (SDK) | nemotron-3-super-120b-a12b | gpt-oss-120b |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-03 | 2025-08 |
| Knowledge Cutoff | January 2026 | June 2024 |
| Architecture | High-Throughput Sparse MoE (120B Total / 12B Active) | Sparse MoE transformer with reasoning |
| Parameters | 120B (12B active MoE) | 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 ($0.3/1M in) across full 262K window | Flat rate ($0.15/1M in) across full 131K window |
| Prompt Cache Policy & TTL | Cached input at $0.06/1M tokens | Automatic prefix caching at $0.015/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| 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 | 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) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 74 GB VRAM (4-bit Quantized) · 258 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 | text |
| Output Modalities | text | text |
Nemotron 3 Super 120B A12B vs GPT-OSS 120B: Risposte Rapide (FAQ)
Which is more intelligent, Nemotron 3 Super 120B A12B or GPT-OSS 120B?
GPT-OSS 120B is more intelligent. It scores 56 compared with 53 on the composite Intelligence Index.
Which model is faster?
GPT-OSS 120B is faster, generating 95 tokens per second compared with 85 tokens per second.
Which model is cheaper to run?
GPT-OSS 120B is cheaper at $0.10 per 1M tokens blended, compared with $0.18 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Nemotron 3 Super 120B A12B has lower latency, with a time to first token of 0.46s compared with 0.48s.
Which model has a larger context window?
Nemotron 3 Super 120B A12B has a larger context window, supporting 262.1K tokens compared with 131.1K tokens.
Verdetto
Sia Nemotron 3 Super 120B A12B (Ideale per i workflow agentici autonomi, il tool calling e l’automazione multi-step, sistemi RAG aziendali e integrazioni API scalabili.) sia GPT-OSS 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.) eccellono in produzione: GPT-OSS 120B guida nei benchmark di ragionamento e programmazione, GPT-OSS 120B garantisce una generazione più rapida e GPT-OSS 120B offre il miglior rapporto qualità-prezzo per carichi API elevati.
Domande Frequenti (FAQ)
Quale modello è migliore per coding e ragionamento, Nemotron 3 Super 120B A12B o GPT-OSS 120B?+
Nell’indice di intelligenza composito e nei benchmark di programmazione prevale GPT-OSS 120B. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.
Qual è la differenza di prezzo API tra Nemotron 3 Super 120B A12B e GPT-OSS 120B?+
Nemotron 3 Super 120B A12B costa $0.30 / $0.80 per milione di token di input/output, mentre GPT-OSS 120B costa $0.15 / $0.60. Nel costo combinato, GPT-OSS 120B risulta più economico.
Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+
Nemotron 3 Super 120B A12B supporta 262.1K token di contesto contro 131.1K token di GPT-OSS 120B, mentre GPT-OSS 120B 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 NVIDIA e OpenAI, schede di sistema e benchmark indipendenti (2026).