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Nemotron Nano 9B v2 vs GPT-OSS 20B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Nemotron Nano 9B v2 vs GPT-OSS 20B: Benchmarks, Pricing & Speed Comparison

Nemotron Nano 9B v2 dari NVIDIA dan GPT-OSS 20B dari OpenAI adalah dua model AI unggulan. Nemotron Nano 9B v2 menawarkan jendela konteks 131.1K token dengan harga $0.08/$0.20 per 1 juta token, sedangkan GPT-OSS 20B menyediakan konteks 131.1K token seharga $0.10/$0.50 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.

Perbandingan Langsung (VS)

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NVIDIA

Nemotron Nano 9B v2

Model Ringan & Hemat Biaya

Nemotron Nano 9B v2 dari NVIDIA adalah model AI yang dirancang untuk efisiensi biaya API maksimal per token dan throughput produksi yang ringan, menghadirkan jendela konteks 131K, kecepatan 140 token/detik, indeks kecerdasan 42/100, serta t

Intelligence Index42 / 100
Input Token:

$0.08 / 1M

Output Token:

$0.20 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

32.5%

OpenAI

GPT-OSS 20B

Penalaran Bobot Terbuka

GPT-OSS 20B dari OpenAI adalah model AI yang dirancang untuk penalaran rantai pemikiran (Chain-of-Thought) berbobot terbuka yang sangat efisien, menghadirkan jendela konteks 131K, kecepatan 160 token/detik, indeks kecerdasan 46/100, serta t

Intelligence Index46 / 100
Input Token:

$0.10 / 1M

Output Token:

$0.50 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

32.5%

Perbedaan Utama Sekilas

  • 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%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureNemotron Nano 9B v2GPT-OSS 20B
Intelligence Index
Composite reasoning & knowledge score (0-100)
4246
Provider
NVIDIAOpenAI
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 text131.1K tokens~197 A4 pages of text
Max Output Tokens
Per single request
8K33K
Throughput Speed
Tokens generated per second
140 tok/s160 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

Siapa Unggul di Mana?

At-a-glance decision framework
Coding
GPT-OSS 20B
+0.0% SWE-bench lead
Cost
Nemotron Nano 9B v2
1.9x cheaper monthly runtime
Speed
GPT-OSS 20B
+14% faster generation
Context
Tied
131.1K tokens window

Skor Benchmark Terverifikasi

Official evaluation metrics verified by independent third-party research labs.

Nemotron Nano 9B v2GPT-OSS 20B
SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

32.5%vs32.5%
GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

48%vs55%
MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

71%vs72.5%
LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1245ptsvs1288pts
Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

N/Avs7.2%
Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

28%vs22%
SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

N/Avs24.5%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs46%
AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

54%vs72%
Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

68%vs63%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

140 tok/s vs160 tok/s

Latensi & Waktu Respons

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better
Nemotron Nano 9B v20.28s
GPT-OSS 20B0.26s
Est. End-to-End Response (500 tokens)TTFT + generation time
Nemotron Nano 9B v23.85s
GPT-OSS 20B3.38s

Simulator Biaya Token Interaktif

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Nemotron Nano 9B v2 Monthly API Bill
$0.33 / month
$0.08/1M in · $0.2/1M out
GPT-OSS 20B Monthly API Bill
$0.61 / month
$0.1/1M in · $0.5/1M out
Nemotron Nano 9B v2 saves approximately $0.28 / month (46%) under this workload.1.9x Savings

Nemotron Nano 9B v2 · Strengths & Key Advantages

  • Jendela konteks 131K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 32.5% · GPQA Diamond 48%)
  • Kecepatan generasi tinggi 140 token/detik dengan latensi token pertama (TTFT) hanya 280 ms
  • Bobot terbuka (9B) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama
Considerations & Trade-offs
  • Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
  • Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

GPT-OSS 20B · Strengths & Key Advantages

  • Jendela konteks 131K dengan kapasitas output maksimum hingga 33K token (SWE-bench Verified 32.5% · GPQA Diamond 55%)
  • Kecepatan generasi tinggi 160 token/detik dengan latensi token pertama (TTFT) hanya 260 ms
  • Bobot terbuka (21B (3.6B MoE)) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama
Considerations & Trade-offs
  • Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
  • Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

MetricNemotron Nano 9B v2GPT-OSS 20B
Cost per Task
Estimated USD to finish one task
$0.06$0.09
Output Tokens / Task
Tokens generated per task
22K40K
Reasoning Tokens / Task
Thinking tokens burned per task
8K18K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

FeatureNemotron Nano 9B v2GPT-OSS 20B
Official API Model ID (SDK)nemotron-nano-9b-v2gpt-oss-20b
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI Responses API & Chat Completions v1
Release Date2025-082025-08
Knowledge CutoffJune 2025June 2024
ArchitectureEfficient Dense TransformerSparse MoE transformer with reasoning
Parameters9B21B (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 CloudUnlimited (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 windowFlat rate ($0.1/1M in) across full 131K window
Prompt Cache Policy & TTLCached input at $0.02/1M tokensAutomatic prefix caching at $0.01/1M (no extra cache-write fee)
Computer Use / GUI AgentAPI / 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 CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsNative Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning SupportFull 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 BillingText BPE (~4 chars/token)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMBF16 / 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 ClassificationOpen-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 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestexttext
Output Modalitiestexttext

Nemotron Nano 9B v2 vs GPT-OSS 20B: Jawaban Cepat (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.

Kesimpulan

Baik Nemotron Nano 9B v2 (Sangat cocok untuk efisiensi biaya API maksimal per token dan throughput produksi yang ringan, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun GPT-OSS 20B (Sangat cocok untuk penalaran rantai pemikiran (Chain-of-Thought) berbobot terbuka yang sangat efisien, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: GPT-OSS 20B unggul dalam penalaran dan pemrograman, GPT-OSS 20B memberikan kecepatan generasi token lebih tinggi, dan Nemotron Nano 9B v2 lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Nemotron Nano 9B v2 atau GPT-OSS 20B?+

Dalam indeks kecerdasan gabungan dan benchmark coding, GPT-OSS 20B memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Nemotron Nano 9B v2 vs GPT-OSS 20B?+

Nemotron Nano 9B v2 mengenakan biaya $0.08 / $0.20 per 1M token input/output, sedangkan GPT-OSS 20B sebesar $0.10 / $0.50. Secara keseluruhan, Nemotron Nano 9B v2 lebih hemat biaya.

Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+

Nemotron Nano 9B v2 mendukung konteks 131.1K token dibandingkan 131.1K token pada GPT-OSS 20B, sementara GPT-OSS 20B unggul dalam kecepatan output token per detik.

AI Terminology & Guide
Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

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.

Sumber resmi: Dokumentasi API resmi NVIDIA & OpenAI serta evaluasi benchmark independen (2026).