Nemotron Nano 9B v2 vs GPT-OSS 20B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Nemotron Nano 9B v2 by NVIDIA and GPT-OSS 20B 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, Nemotron Nano 9B v2 holds a notable pricing advantage for token ingestion. On output generation, Nemotron Nano 9B v2 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 131K tokens. In latency-sensitive production environments, GPT-OSS 20B 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 Nano 9B v2
نموذج مفتوح الأوزاننموذج Nemotron Nano 9B v2 المقدم من NVIDIA يوفر نافذة سياق تصل إلى 131K رمز، بتكلفة $0.08 لكل مليون رمز إدخال و$0.2 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$0.08 / 1M
$0.20 / 1M
131.1K tokens
32.5%
GPT-OSS 20B
نموذج مفتوح الأوزاننموذج GPT-OSS 20B المقدم من OpenAI يوفر نافذة سياق تصل إلى 131K رمز، بتكلفة $0.1 لكل مليون رمز إدخال و$0.5 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$0.10 / 1M
$0.50 / 1M
131.1K tokens
32.5%
أهم الفروقات في لمحة
- Nemotron Nano 9B v2 is 1.9x cheaper for API usage (input + output tokens combined).
- GPT-OSS 20B is faster at 160 tok/s vs 140 tok/s.
- GPT-OSS 20B leads on SWE-bench coding (32.5% vs 32.5%).
| Feature | Nemotron Nano 9B v2 | GPT-OSS 20B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 42 | 46 |
Provider | NVIDIA | OpenAI |
Input Price Cost per million input tokens | $0.08 / 1M | $0.10 / 1M |
Output Price Cost per million output tokens | $0.20 / 1M | $0.50 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.05 / 1M | $0.08 / 1M |
Context Window Maximum input + output tokens | 131.1K tokens~197 A4 pages of text | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 8K | 33K |
Throughput Speed Tokens generated per second | 140 tok/s | 160 tok/s |
SWE-bench Verified Real-world software engineering | 32.5% | 32.5% |
GPQA Diamond Graduate-level reasoning | 48% | 55% |
Cached Input Price Prompt caching discount per 1M tokens | $0.02 / 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 Nano 9B v2 · Strengths & Key Advantages
- بنية Efficient Dense Transformer متطورة من NVIDIA مع سعة سياق تبلغ 131K رمز
- كفاءة عالية في التكلفة ($0.08/1M إدخال و$0.2/1M 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 20B · Strengths & Key Advantages
- بنية Sparse MoE transformer with reasoning متطورة من OpenAI مع سعة سياق تبلغ 131K رمز
- كفاءة عالية في التكلفة ($0.1/1M إدخال و$0.5/1M 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 Nano 9B v2 | GPT-OSS 20B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.06 | $0.09 |
Output Tokens / Task Tokens generated per task | 22K | 40K |
Reasoning Tokens / Task Thinking tokens burned per task | 8K | 18K |
المواصفات التقنية
Architecture, release timeline, capabilities, and modalities.
| Feature | Nemotron Nano 9B v2 | GPT-OSS 20B |
|---|---|---|
| Official API Model ID (SDK) | nemotron-nano-9b-v2 | gpt-oss-20b |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-08 | 2025-08 |
| Knowledge Cutoff | June 2025 | June 2024 |
| Architecture | Efficient Dense Transformer | Sparse MoE transformer with reasoning |
| Parameters | 9B | 21B (3.6B 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 131K window | Flat rate ($0.1/1M in) across full 131K window |
| Prompt Cache Policy & TTL | Cached input at $0.02/1M tokens | Automatic prefix caching at $0.01/1M (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) · 6 GB VRAM (4-bit Quantized) · 19 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 13 GB VRAM (4-bit Quantized) · 45 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 Nano 9B v2 vs GPT-OSS 20B: إجابات سريعة (FAQ)
Which is more intelligent, Nemotron Nano 9B v2 or GPT-OSS 20B?
GPT-OSS 20B is more intelligent. It scores 46 compared with 42 on the composite Intelligence Index.
Which model is faster?
GPT-OSS 20B is faster, generating 160 tokens per second compared with 140 tokens per second.
Which model is cheaper to run?
Nemotron Nano 9B v2 is cheaper at $0.05 per 1M tokens blended, compared with $0.08 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-OSS 20B has lower latency, with a time to first token of 0.26s compared with 0.28s.
الحكم النهائي
Verdict & Model Selection Recommendation: Choose Nemotron Nano 9B v2 if your priority is Robotics control, local PC copilots, and real-time interactive voice agents. Its performance in demanding tasks justifies its operational token cost. Choose GPT-OSS 20B if your workflow prioritizes Self-hosted agents, on-premise deployments and privacy-sensitive work. 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 Nano 9B v2 compare to GPT-OSS 20B in API token pricing and cost per token?+
Nemotron Nano 9B v2 costs $0.08 per 1M input tokens and $0.20 per 1M output tokens, whereas GPT-OSS 20B costs $0.10 input and $0.50 output. Overall, Nemotron Nano 9B v2 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 Nano 9B v2 or GPT-OSS 20B?+
Both Nemotron Nano 9B v2 and GPT-OSS 20B offer an identical context window of 131K tokens (~98k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: Nemotron Nano 9B v2 or GPT-OSS 20B?+
Both models achieve an identical 32.5% score on SWE-bench Verified, demonstrating matched software engineering capabilities.
What is the difference in reasoning capability and extended thinking between Nemotron Nano 9B v2 and GPT-OSS 20B?+
Nemotron Nano 9B v2 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, GPT-OSS 20B provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Nemotron Nano 9B v2 and GPT-OSS 20B?+
Nemotron Nano 9B v2 generates approximately 140 tokens per second, while GPT-OSS 20B delivers 160 tokens per second. In latency-sensitive production environments, GPT-OSS 20B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Nemotron Nano 9B v2 and GPT-OSS 20B to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Nemotron Nano 9B v2 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GPT-OSS 20B. 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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- Nemotron Nano 9B v2 vs Nemotron Research Reasoning: Benchmarks, Pricing & Speed Comparisonمقارنة
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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