Head-to-Head Model Comparison

Nemotron Nano 12B v2 VL vs GPT-OSS 20B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado por el equipo de investigación de GetInfoUsÚltima revisión: October 2026Política editorial

Cambia y Compara Cualquier Modelo

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

Nemotron Nano 12B v2 VLProfile

GPT-OSS 20BProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

NVIDIA

Nemotron Nano 12B v2 VL

IA Multimodal Nativa

Nemotron Nano 12B v2 VL de NVIDIA es un modelo de IA diseñado para comprensión multimodal nativa de texto e imagen y análisis visual de documentos, con ventana de contexto de 128K, velocidad de 110 tokens/s, índice de inteligencia de 43/100

Intelligence Index43 / 100

Input Token:

$0.12 / 1M

Output Token:

$0.30 / 1M

Context Window:

128K tokens

SWE-bench Verified:

31%

OpenAI

GPT-OSS 20B

Razonamiento de Pesos Abiertos

GPT-OSS 20B de OpenAI es un modelo de IA diseñado para razonamiento de cadena de pensamiento con pesos abiertos y alta eficiencia algorítmica, con ventana de contexto de 131K, velocidad de 160 tokens/s, índice de inteligencia de 46/100 y ta

Intelligence Index46 / 100

Input Token:

$0.10 / 1M

Output Token:

$0.50 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

32.5%

Diferencias Clave de un Vistazo

Nemotron Nano 12B v2 VL is 1.2x cheaper for API usage (input + output tokens combined).
GPT-OSS 20B has a larger context window (131K vs 128K tokens).
GPT-OSS 20B is faster at 160 tok/s vs 110 tok/s.
GPT-OSS 20B leads on SWE-bench coding (32.5% vs 31.0%).

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Característica	Nemotron Nano 12B v2 VL	GPT-OSS 20B
Intelligence Index Composite reasoning & knowledge score (0-100)	43	46
Provider	NVIDIA	OpenAI
Input Price Cost per million input tokens	$0.12 / 1M	$0.10 / 1M
Output Price Cost per million output tokens	$0.30 / 1M	$0.50 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.07 / 1M	$0.08 / 1M
Context Window Maximum input + output tokens	128K tokens~192 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	110 tok/s	160 tok/s
SWE-bench Verified Real-world software engineering	31%	32.5%
GPQA Diamond Graduate-level reasoning	47%	55%
Cached Input Price Prompt caching discount per 1M tokens	$0.03 / 1M	$0.01 / 1M

¿Quién gana en cada área?

At-a-glance decision framework

Código

GPT-OSS 20B

+1.5% SWE-bench lead

Costo

Nemotron Nano 12B v2 VL

1.2x cheaper monthly runtime

Velocidad

GPT-OSS 20B

+45% faster generation

Contexto

GPT-OSS 20B

131.1K tokens window

Puntuaciones de Benchmarks Verificadas

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

Nemotron Nano 12B v2 VLGPT-OSS 20B

SWE-bench Verified (Autonomous Coding)

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

31%vs32.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

47%vs55%

MMLU-Pro (Multi-task Language Understanding)

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

70%vs72.5%

LMSYS Chatbot Arena Elo

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

1250ptsvs1288pts

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.

27%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.

52%vs72%

Long-Context Reasoning

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

66%vs63%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

110 tok/s vs160 tok/s

Latencia y Tiempo de Respuesta

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 12B v2 VL0.35s

GPT-OSS 20B0.26s

Est. End-to-End Response (500 tokens)TTFT + generation time

Nemotron Nano 12B v2 VL4.90s

GPT-OSS 20B3.38s

Simulador Interactivo de Costos de Tokens

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

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

Nemotron Nano 12B v2 VL Monthly API Bill

$0.49 / month

$0.12/1M in · $0.3/1M out

GPT-OSS 20B Monthly API Bill

$0.61 / month

$0.1/1M in · $0.5/1M out

Nemotron Nano 12B v2 VL saves approximately $0.12 / month (20%) under this workload.1.2x Savings

Nemotron Nano 12B v2 VL · Strengths & Key Advantages

Ventana de contexto de 128K con hasta 8K tokens de salida máxima (SWE-bench Verified 31% · GPQA Diamond 47%)
Rendimiento ágil de 110 tokens/segundo con latencia inicial (TTFT) de solo 350 ms
Pesos abiertos (12B) listos para autoalojamiento local o en nube privada mediante vLLM y Ollama

Considerations & Trade-offs

Requiere infraestructura GPU dedicada con suficiente memoria VRAM para inferencia local sin cuantización
En demostraciones científicas extremas, los modelos de razonamiento pesado de gama alta ofrecen mayor profundidad

GPT-OSS 20B · Strengths & Key Advantages

Ventana de contexto de 131K con hasta 33K tokens de salida máxima (SWE-bench Verified 32.5% · GPQA Diamond 55%)
Rendimiento ágil de 160 tokens/segundo con latencia inicial (TTFT) de solo 260 ms
Pesos abiertos (21B (3.6B MoE)) listos para autoalojamiento local o en nube privada mediante vLLM y Ollama

Considerations & Trade-offs

Requiere infraestructura GPU dedicada con suficiente memoria VRAM para inferencia local sin cuantización
En demostraciones científicas extremas, los modelos de razonamiento pesado de gama alta ofrecen mayor profundidad

Token Use & Task Economics

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

Metric	Nemotron Nano 12B v2 VL	GPT-OSS 20B
Cost per Task Estimated USD to finish one task	$0.09	$0.09
Output Tokens / Task Tokens generated per task	24K	40K
Reasoning Tokens / Task Thinking tokens burned per task	8K	18K

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

Característica	Nemotron Nano 12B v2 VL	GPT-OSS 20B
Official API Model ID (SDK)	nemotron-nano-12b-v2-vl	gpt-oss-20b
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI Responses API & Chat Completions v1
Release Date	2025-10	2025-08
Knowledge Cutoff	August 2025	June 2024
Architecture	Compact Vision-Language Transformer	Sparse MoE transformer with reasoning
Parameters	12B	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 ($0.12/1M in) across full 128K window	Flat rate ($0.1/1M in) across full 131K window
Prompt Cache Policy & TTL	Cached input at $0.03/1M 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) · Vision (~258–765 tokens per image tile)	Text BPE (~4 chars/token)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 7 GB VRAM (4-bit Quantized) · 26 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, vision	text
Output Modalities	text	text

Nemotron Nano 12B v2 VL vs GPT-OSS 20B: Respuestas Rápidas (FAQ)

Which is more intelligent, Nemotron Nano 12B v2 VL or GPT-OSS 20B?

GPT-OSS 20B is more intelligent. It scores 46 compared with 43 on the composite Intelligence Index.

Which model is faster?

GPT-OSS 20B is faster, generating 160 tokens per second compared with 110 tokens per second.

Which model has lower latency?

GPT-OSS 20B has lower latency, with a time to first token of 0.26s compared with 0.35s.

Which model has a larger context window?

GPT-OSS 20B has a larger context window, supporting 131.1K tokens compared with 128K tokens.

Veredicto

Tanto Nemotron Nano 12B v2 VL (Ideal para comprensión multimodal nativa de texto e imagen y análisis visual de documentos, sistemas RAG empresariales y automatización mediante API.) como GPT-OSS 20B (Ideal para razonamiento de cadena de pensamiento con pesos abiertos y alta eficiencia algorítmica, sistemas RAG empresariales y automatización mediante API.) destacan en producción: GPT-OSS 20B lidera en inteligencia general y programación, GPT-OSS 20B ofrece mayor velocidad de generación de tokens y Nemotron Nano 12B v2 VL resulta más rentable para cargas de trabajo API de alto volumen.

Preguntas frecuentes

¿Cuál es mejor para programación y razonamiento, Nemotron Nano 12B v2 VL o GPT-OSS 20B?+

En las pruebas de inteligencia compuesta y resolución de código, GPT-OSS 20B obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.

¿Cuánto cuesta la API de Nemotron Nano 12B v2 VL frente a GPT-OSS 20B?+

Nemotron Nano 12B v2 VL cuesta $0.12 / $0.30 por millón de tokens de entrada/salida, mientras que GPT-OSS 20B cuesta $0.10 / $0.50. En costo combinado, Nemotron Nano 12B v2 VL es la opción más económica.

¿Qué modelo tiene mayor ventana de contexto y menor latencia?+

Nemotron Nano 12B v2 VL cuenta con 128K tokens de contexto frente a 131.1K tokens de GPT-OSS 20B. En velocidad de inferencia, GPT-OSS 20B ofrece un rendimiento superior.

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 & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Nemotron Nano 12B v2 VL vs GPT-OSS 20B

Cambia y Compara Cualquier Modelo

Nemotron Nano 12B v2 VL

GPT-OSS 20B

¿Quién gana en cada área?

Puntuaciones de Benchmarks Verificadas

Latencia y Tiempo de Respuesta

Simulador Interactivo de Costos de Tokens

Nemotron Nano 12B v2 VL · Strengths & Key Advantages

Considerations & Trade-offs

GPT-OSS 20B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificaciones Técnicas

Nemotron Nano 12B v2 VL vs GPT-OSS 20B: Respuestas Rápidas (FAQ)

Veredicto

Preguntas frecuentes

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