Nemotron 3 Super 120B A12B vs GPT-OSS 120B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Nemotron 3 Super 120B A12B by NVIDIA and GPT-OSS 120B by OpenAI represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, GPT-OSS 120B holds a notable pricing advantage for token ingestion. On output generation, GPT-OSS 120B is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Nemotron 3 Super 120B A12B provides an expanded memory horizon with 262K tokens. In latency-sensitive production environments, GPT-OSS 120B leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
بدّل وقارن بين أي نموذجين
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Nemotron 3 Super 120B A12B
نموذج مفتوح الأوزاننموذج Nemotron 3 Super 120B A12B المقدم من NVIDIA يوفر نافذة سياق تصل إلى 262K رمز، بتكلفة $0.3 لكل مليون رمز إدخال و$0.8 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$0.30 / 1M
$0.80 / 1M
262.1K tokens
51%
GPT-OSS 120B
نموذج مفتوح الأوزاننموذج GPT-OSS 120B المقدم من OpenAI يوفر نافذة سياق تصل إلى 131K رمز، بتكلفة $0.15 لكل مليون رمز إدخال و$0.6 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$0.15 / 1M
$0.60 / 1M
131.1K tokens
45.5%
أهم الفروقات في لمحة
- 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 |
من يتفوق في كل مجال؟
At-a-glance decision frameworkنتائج الاختبارات المعيارية الموثقة
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.
زمن الاستجابة والسرعة
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
محاكي تكلفة الرموز التفاعلي
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Nemotron 3 Super 120B A12B · Strengths & Key Advantages
- بنية High-Throughput Sparse MoE (120B Total / 12B Active) متطورة من NVIDIA مع سعة سياق تبلغ 262K رمز
- كفاءة عالية في التكلفة ($0.3/1M إدخال و$0.8/1M إخراج) وسرعة استجابة ممتازة
- أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
- يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
- قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة
GPT-OSS 120B · Strengths & Key Advantages
- بنية Sparse MoE transformer with reasoning متطورة من OpenAI مع سعة سياق تبلغ 131K رمز
- كفاءة عالية في التكلفة ($0.15/1M إدخال و$0.6/1M إخراج) وسرعة استجابة ممتازة
- أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
- يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
- قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة
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 |
المواصفات التقنية
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: إجابات سريعة (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.
الحكم النهائي
Verdict & Model Selection Recommendation: Choose Nemotron 3 Super 120B A12B if your priority is Enterprise multi-agent architectures, massive data extraction, and autonomous coding. Its performance in demanding tasks justifies its operational token cost. Choose GPT-OSS 120B if your workflow prioritizes Self-hosted agents that need near-frontier quality on private data. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
الأسئلة الشائعة (FAQ)
How does Nemotron 3 Super 120B A12B compare to GPT-OSS 120B in API token pricing and cost per token?+
Nemotron 3 Super 120B A12B costs $0.30 per 1M input tokens and $0.80 per 1M output tokens, whereas GPT-OSS 120B costs $0.15 input and $0.60 output. Overall, GPT-OSS 120B is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Nemotron 3 Super 120B A12B or GPT-OSS 120B?+
Nemotron 3 Super 120B A12B supports a context window of 262K tokens (~197k words), while GPT-OSS 120B supports 131K tokens (~98k words). Therefore, Nemotron 3 Super 120B A12B can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Nemotron 3 Super 120B A12B or GPT-OSS 120B?+
Nemotron 3 Super 120B A12B scores 51% on SWE-bench Verified while GPT-OSS 120B scores 45.5%. For autonomous software engineering and multi-file debugging, Nemotron 3 Super 120B A12B holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Nemotron 3 Super 120B A12B and GPT-OSS 120B?+
Nemotron 3 Super 120B A12B features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, GPT-OSS 120B provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Nemotron 3 Super 120B A12B and GPT-OSS 120B?+
Nemotron 3 Super 120B A12B generates approximately 85 tokens per second, while GPT-OSS 120B delivers 95 tokens per second. In latency-sensitive production environments, GPT-OSS 120B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Nemotron 3 Super 120B A12B and GPT-OSS 120B to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to GPT-OSS 120B to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Nemotron 3 Super 120B A12B. This tiered routing pattern cuts overall API bills by up to 70%.
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.
المصادر الرسمية: GetInfoUs Independent AI Benchmarks & Official API Specs