Nemotron Nano 9B v2 vs GPT-OSS 20B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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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.
원하는 AI 모델 선택 및 실시간 비교
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Nemotron Nano 9B v2
오픈 웨이트 모델NVIDIA의 Nemotron Nano 9B v2 모델은 131K 토큰 컨텍스트 윈도우와 입력 $0.08/1M 토큰, 출력 $0.2/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.08 / 1M
$0.20 / 1M
131.1K tokens
32.5%
GPT-OSS 20B
오픈 웨이트 모델OpenAI의 GPT-OSS 20B 모델은 131K 토큰 컨텍스트 윈도우와 입력 $0.1/1M 토큰, 출력 $0.5/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.10 / 1M
$0.50 / 1M
131.1K tokens
32.5%
한눈에 보는 핵심 차이점
- 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 |
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.
Nemotron Nano 9B v2 · Strengths & Key Advantages
- NVIDIA의 첨단 Efficient Dense Transformer 아키텍처와 최대 131K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0.08/1M, 출력 $0.2/1M)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
GPT-OSS 20B · Strengths & Key Advantages
- OpenAI의 첨단 Sparse MoE transformer with reasoning 아키텍처와 최대 131K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0.1/1M, 출력 $0.5/1M)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 | 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 |
기술 사양 (Specifications)
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: 핵심 요약 문답 (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.
종합 평가
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.
자주 묻는 질문 (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 12B v2 VL vs GPT-OSS 20B: Benchmarks, Pricing & Speed Comparison비교
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- Nemotron Nano 9B v2 vs Nemotron Research Reasoning: Benchmarks, Pricing & Speed Comparison비교
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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: GetInfoUs Independent AI Benchmarks & Official API Specs