Head-to-Head Model Comparison

Nemotron 3 Nano Omni 30B A3B Reasoning vs Mistral Small 4

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 3 Nano Omni 30B A3B ReasoningProfile

Mistral Small 4Profile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

NVIDIA

Nemotron 3 Nano Omni 30B A3B Reasoning

IA Multimodal Nativa

Nemotron 3 Nano Omni 30B A3B Reasoning 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 256K, velocidad de 105 tokens/s, índice de intelig

Intelligence Index48 / 100

Input Token:

$0.20 / 1M

Output Token:

$0.60 / 1M

Context Window:

256K tokens

SWE-bench Verified:

42%

Mistral AI

Mistral Small 4

Inferencia Ultrarrápida

Mistral Small 4 de Mistral AI es un modelo de IA diseñado para interacción en tiempo real, baja latencia y enrutamiento de alto volumen, con ventana de contexto de 256K, velocidad de 120 tokens/s, índice de inteligencia de 48/100 y tarifa d

Intelligence Index48 / 100

Input Token:

$0.10 / 1M

Output Token:

$0.30 / 1M

Context Window:

256K tokens

SWE-bench Verified:

44.5%

Diferencias Clave de un Vistazo

Mistral Small 4 is 2.0x cheaper for API usage (input + output tokens combined).
Mistral Small 4 is faster at 120 tok/s vs 105 tok/s.
Mistral Small 4 leads on SWE-bench coding (44.5% vs 42.0%).

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

Característica	Nemotron 3 Nano Omni 30B A3B Reasoning	Mistral Small 4
Intelligence Index Composite reasoning & knowledge score (0-100)	48	48
Provider	NVIDIA	Mistral AI
Input Price Cost per million input tokens	$0.20 / 1M	$0.10 / 1M
Output Price Cost per million output tokens	$0.60 / 1M	$0.30 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.14 / 1M	$0.07 / 1M
Context Window Maximum input + output tokens	256K tokens~384 A4 pages of text	256K tokens~384 A4 pages of text
Max Output Tokens Per single request	16K	16K
Throughput Speed Tokens generated per second	105 tok/s	120 tok/s
SWE-bench Verified Real-world software engineering	42%	44.5%
GPQA Diamond Graduate-level reasoning	56.5%	56%
Cached Input Price Prompt caching discount per 1M tokens	$0.05 / 1M	$0.03 / 1M

¿Quién gana en cada área?

At-a-glance decision framework

Código

Mistral Small 4

+2.5% SWE-bench lead

Costo

Mistral Small 4

2.0x cheaper monthly runtime

Velocidad

Mistral Small 4

+14% faster generation

Contexto

Tied

256K tokens window

Puntuaciones de Benchmarks Verificadas

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

Nemotron 3 Nano Omni 30B A3B ReasoningMistral Small 4

SWE-bench Verified (Autonomous Coding)

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

42%vs44.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

56.5%vs56%

MMLU-Pro (Multi-task Language Understanding)

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

77.2%vs78%

LMSYS Chatbot Arena Elo

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

1310ptsvs1315pts

Terminal-Bench (Agentic Coding & Terminal Use)

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

36.5%vs38%

AIME (Advanced Math Competition)

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

66%vs66%

Long-Context Reasoning

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

78%vs80%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

105 tok/s vs120 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 3 Nano Omni 30B A3B Reasoning0.38s

Mistral Small 40.34s

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

Nemotron 3 Nano Omni 30B A3B Reasoning5.14s

Mistral Small 44.51s

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 3 Nano Omni 30B A3B Reasoning Monthly API Bill

$0.92 / month

$0.2/1M in · $0.6/1M out

Mistral Small 4 Monthly API Bill

$0.47 / month

$0.1/1M in · $0.3/1M out

Mistral Small 4 saves approximately $0.45 / month (49%) under this workload.2.0x Savings

Nemotron 3 Nano Omni 30B A3B Reasoning · Strengths & Key Advantages

Ventana de contexto de 256K con hasta 16K tokens de salida máxima (SWE-bench Verified 42% · GPQA Diamond 56.5%)
Rendimiento ágil de 105 tokens/segundo con latencia inicial (TTFT) de solo 380 ms
Pesos abiertos (30B (3B 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

Mistral Small 4 · Strengths & Key Advantages

Ventana de contexto de 256K con hasta 16K tokens de salida máxima (SWE-bench Verified 44.5% · GPQA Diamond 56%)
Rendimiento ágil de 120 tokens/segundo con latencia inicial (TTFT) de solo 340 ms
Pesos abiertos (24B) 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 3 Nano Omni 30B A3B Reasoning	Mistral Small 4
Cost per Task Estimated USD to finish one task	$0.22	$0.12
Output Tokens / Task Tokens generated per task	28K	28K
Reasoning Tokens / Task Thinking tokens burned per task	12K	12K

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

Característica	Nemotron 3 Nano Omni 30B A3B Reasoning	Mistral Small 4
Official API Model ID (SDK)	nemotron-3-nano-omni-30b-a3b	mistral-small-4-latest
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2026-04	2026-03
Knowledge Cutoff	February 2026	January 2026
Architecture	Omnimodal Sparse MoE (30B Total / 3B Active)	Next-Gen Compact Multimodal Transformer
Parameters	30B (3B active MoE)	24B
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 ($0.2/1M in) across full 256K window	Flat rate ($0.1/1M in) across full 256K window
Prompt Cache Policy & TTL	Cached input at $0.05/1M tokens	Cached input at $0.03/1M tokens
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	Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
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) · Audio (~25–32 tokens/sec)	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 19 GB VRAM (4-bit Quantized) · 65 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 15 GB VRAM (4-bit Quantized) · 52 GB VRAM (Native BF16)
Safety & Frontier Classification	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
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, audio	text, vision
Output Modalities	text	text

Nemotron 3 Nano Omni 30B A3B Reasoning vs Mistral Small 4: Respuestas Rápidas (FAQ)

Which model is faster?

Mistral Small 4 is faster, generating 120 tokens per second compared with 105 tokens per second.

Which model is cheaper to run?

Mistral Small 4 is cheaper at $0.07 per 1M tokens blended, compared with $0.14 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Mistral Small 4 has lower latency, with a time to first token of 0.34s compared with 0.38s.

Veredicto

Tanto Nemotron 3 Nano Omni 30B A3B Reasoning (Ideal para comprensión multimodal nativa de texto e imagen y análisis visual de documentos, sistemas RAG empresariales y automatización mediante API.) como Mistral Small 4 (Ideal para interacción en tiempo real, baja latencia y enrutamiento de alto volumen, sistemas RAG empresariales y automatización mediante API.) destacan en producción: Nemotron 3 Nano Omni 30B A3B Reasoning lidera en inteligencia general y programación, Mistral Small 4 ofrece mayor velocidad de generación de tokens y Mistral Small 4 resulta más rentable para cargas de trabajo API de alto volumen.

Preguntas frecuentes

¿Cuál es mejor para programación y razonamiento, Nemotron 3 Nano Omni 30B A3B Reasoning o Mistral Small 4?+

En las pruebas de inteligencia compuesta y resolución de código, Nemotron 3 Nano Omni 30B A3B Reasoning obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.

¿Cuánto cuesta la API de Nemotron 3 Nano Omni 30B A3B Reasoning frente a Mistral Small 4?+

Nemotron 3 Nano Omni 30B A3B Reasoning cuesta $0.20 / $0.60 por millón de tokens de entrada/salida, mientras que Mistral Small 4 cuesta $0.10 / $0.30. En costo combinado, Mistral Small 4 es la opción más económica.

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

Nemotron 3 Nano Omni 30B A3B Reasoning cuenta con 256K tokens de contexto frente a 256K tokens de Mistral Small 4. En velocidad de inferencia, Mistral Small 4 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 3 Nano Omni 30B A3B Reasoning vs Mistral Small 4

Cambia y Compara Cualquier Modelo

Nemotron 3 Nano Omni 30B A3B Reasoning

Mistral Small 4

¿Quién gana en cada área?

Puntuaciones de Benchmarks Verificadas

Latencia y Tiempo de Respuesta

Simulador Interactivo de Costos de Tokens

Nemotron 3 Nano Omni 30B A3B Reasoning · Strengths & Key Advantages

Considerations & Trade-offs

Mistral Small 4 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificaciones Técnicas

Nemotron 3 Nano Omni 30B A3B Reasoning vs Mistral Small 4: Respuestas Rápidas (FAQ)

Veredicto

Preguntas frecuentes

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