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Nemotron Nano 9B v2 vs GPT-OSS 20B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien
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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 von NVIDIA und GPT-OSS 20B von OpenAI gehören zu den führenden KI-Modellen. Nemotron Nano 9B v2 bietet ein Kontextfenster von 131.1K Tokens bei API-Preisen von $0.08/$0.20 pro 1M Tokens, während GPT-OSS 20B 131.1K Tokens Kontext zu $0.10/$0.50 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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NVIDIA

Nemotron Nano 9B v2

Kompaktes Effizienz-Modell

Nemotron Nano 9B v2 von NVIDIA ist ein KI-Modell für maximale Kosteneffizienz pro Token und ressourcenschonende Produktions-Workloads mit 131K Kontextfenster, 140 Token/s Durchsatz, einem Intelligenz-Index von 42/100 und $0.08/$0.2 pro 1M 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

Open-Weights-Reasoning-Modell

GPT-OSS 20B von OpenAI ist ein KI-Modell für Open-Weights-Reasoning mit transparenter Gedankenketten-Verarbeitung (Chain-of-Thought) mit 131K Kontextfenster, 160 Token/s Durchsatz, einem Intelligenz-Index von 46/100 und $0.1/$0.5 pro 1M Tok

Intelligence Index46 / 100
Input Token:

$0.10 / 1M

Output Token:

$0.50 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

32.5%

Wichtigste Unterschiede auf einen Blick

  • 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

Wer gewinnt in welcher Disziplin?

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

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Latenz & Antwortzeit

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

Interaktiver Token-Kosten-Simulator

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

  • 131K Kontextfenster und bis zu 8K maximale Output-Token (SWE-bench Verified 32.5% · GPQA Diamond 48%)
  • Hohe Generierungsgeschwindigkeit von 140 Token/Sekunde bei nur 280 ms Erstlatenz (TTFT)
  • Offene Modellgewichte (9B) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama
Considerations & Trade-offs
  • Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
  • Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

GPT-OSS 20B · Strengths & Key Advantages

  • 131K Kontextfenster und bis zu 33K maximale Output-Token (SWE-bench Verified 32.5% · GPQA Diamond 55%)
  • Hohe Generierungsgeschwindigkeit von 160 Token/Sekunde bei nur 260 ms Erstlatenz (TTFT)
  • Offene Modellgewichte (21B (3.6B MoE)) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama
Considerations & Trade-offs
  • Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
  • Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

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

Technische Spezifikationen

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: Schnelle Antworten (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.

Fazit

Sowohl Nemotron Nano 9B v2 (Optimal für maximale Kosteneffizienz pro Token und ressourcenschonende Produktions-Workloads, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch GPT-OSS 20B (Optimal für Open-Weights-Reasoning mit transparenter Gedankenketten-Verarbeitung (Chain-of-Thought), Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: GPT-OSS 20B führt bei komplexem Reasoning und Coding, GPT-OSS 20B liefert den höheren Token-Durchsatz und Nemotron Nano 9B v2 ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Nemotron Nano 9B v2 oder GPT-OSS 20B?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt GPT-OSS 20B vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Nemotron Nano 9B v2 und GPT-OSS 20B?+

Nemotron Nano 9B v2 kostet $0.08 / $0.20 pro 1M Input-/Output-Tokens, GPT-OSS 20B liegt bei $0.10 / $0.50. Beim gewichteten Gesamtpreis ist Nemotron Nano 9B v2 günstiger.

Welches Modell hat das größere Kontextfenster und die höhere Geschwindigkeit?+

Nemotron Nano 9B v2 bietet 131.1K Tokens Kontext gegenüber 131.1K Tokens bei GPT-OSS 20B. Bei der Generierungsgeschwindigkeit liegt GPT-OSS 20B vorn.

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.

Offizielle Quellen: Offizielle API-Dokumentation von NVIDIA und OpenAI, Systemkarten und unabhängige Benchmark-Auswertungen (2026).