Head-to-Head Model Comparison

GPT-4.1 nano vs GPT-4o mini

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale

Cambia e Confronta Qualsiasi Modello

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

GPT-4.1 nanoProfile

GPT-4o miniProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

OpenAI

GPT-4.1 nano

Modello Leggero ed Economico

GPT-4.1 nano di OpenAI è un modello di IA progettato per la massima efficienza dei costi per token e l’inferenza rapida in produzione, con finestra di contesto da 1.0M, velocità di 220 token/s, indice di intelligenza di 26/100 e tariffa di

Intelligence Index26 / 100

Input Token:

$0.10 / 1M

Output Token:

$0.40 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

24%

OpenAI

GPT-4o mini

Modello Leggero ed Economico

GPT-4o mini di OpenAI è un modello di IA progettato per la massima efficienza dei costi per token e l’inferenza rapida in produzione, con finestra di contesto da 128K, velocità di 140 token/s, indice di intelligenza di 30/100 e tariffa di $

Intelligence Index30 / 100

Input Token:

$0.15 / 1M

Output Token:

$0.60 / 1M

Context Window:

128K tokens

SWE-bench Verified:

24.5%

Differenze Chiave a Colpo d’Occhio

GPT-4.1 nano is 1.6x cheaper for API usage (input + output tokens combined).
GPT-4.1 nano has a larger context window (1M vs 128K tokens).
GPT-4.1 nano is faster at 220 tok/s vs 140 tok/s.
GPT-4o mini leads on SWE-bench coding (24.5% vs 24.0%).

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

Feature	GPT-4.1 nano	GPT-4o mini
Intelligence Index Composite reasoning & knowledge score (0-100)	26	30
Provider	OpenAI	OpenAI
Input Price Cost per million input tokens	$0.10 / 1M	$0.15 / 1M
Output Price Cost per million output tokens	$0.40 / 1M	$0.60 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.08 / 1M	$0.14 / 1M
Context Window Maximum input + output tokens	1.05M tokens~1,571 A4 pages of text	128K tokens~192 A4 pages of text
Max Output Tokens Per single request	33K	16K
Throughput Speed Tokens generated per second	220 tok/s	140 tok/s
SWE-bench Verified Real-world software engineering	24%	24.5%
GPQA Diamond Graduate-level reasoning	38%	40.2%
Cached Input Price Prompt caching discount per 1M tokens	$0.03 / 1M	$0.07 / 1M

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

GPT-4o mini

+0.5% SWE-bench lead

Cost

GPT-4.1 nano

1.6x cheaper monthly runtime

Speed

GPT-4.1 nano

+57% faster generation

Context

GPT-4.1 nano

1.05M tokens window

Punteggi Benchmark Verificati

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

GPT-4.1 nanoGPT-4o mini

SWE-bench Verified (Autonomous Coding)

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

24%vs24.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

38%vs40.2%

MMLU-Pro (Multi-task Language Understanding)

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

55.5%vs58.5%

LMSYS Chatbot Arena Elo

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

1195ptsvs1228pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

4.4%vs6.4%

Terminal-Bench (Agentic Coding & Terminal Use)

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

12.5%vs16.2%

SciCode (Scientific Computing)

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

15%vs14.7%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

25.5%vs28.3%

AIME (Advanced Math Competition)

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

30.5%vs46.2%

Long-Context Reasoning

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

48%vs52.7%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

220 tok/s vs140 tok/s

Latenza e Tempo di Risposta

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

GPT-4.1 nano0.22s

GPT-4o mini0.31s

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

GPT-4.1 nano2.49s

GPT-4o mini3.88s

Simulatore Interattivo dei Costi Token

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)

GPT-4.1 nano Monthly API Bill

$0.55 / month

$0.1/1M in · $0.4/1M out

GPT-4o mini Monthly API Bill

$0.88 / month

$0.15/1M in · $0.6/1M out

GPT-4.1 nano saves approximately $0.33 / month (38%) under this workload.1.6x Savings

GPT-4.1 nano · Strengths & Key Advantages

Finestra di contesto da 1.0M con capacità massima di output fino a 33K token (SWE-bench Verified 24% · GPQA Diamond 38%)
Throughput elevato di 220 token/secondo con latenza al primo token (TTFT) di soli 220 ms
Economia API competitiva ($0.1/$0.4 per 1M di token) con supporto per prompt caching, tool calling e output JSON strutturati

Considerations & Trade-offs

Modello cloud proprietario chiuso; non consente il download locale dei pesi per l’esecuzione offline
Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

GPT-4o mini · Strengths & Key Advantages

Finestra di contesto da 128K con capacità massima di output fino a 16K token (SWE-bench Verified 24.5% · GPQA Diamond 40.2%)
Throughput elevato di 140 token/secondo con latenza al primo token (TTFT) di soli 310 ms
Economia API competitiva ($0.15/$0.6 per 1M di token) con supporto per prompt caching, tool calling e output JSON strutturati

Considerations & Trade-offs

Modello cloud proprietario chiuso; non consente il download locale dei pesi per l’esecuzione offline
Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

Token Use & Task Economics

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

Metric	GPT-4.1 nano	GPT-4o mini
Cost per Task Estimated USD to finish one task	$0.04	$0.16
Output Tokens / Task Tokens generated per task	42K	45K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	GPT-4.1 nano	GPT-4o mini
Official API Model ID (SDK)	gpt-4-1-nano	gpt-4o-mini
API Protocol	OpenAI Responses API & Chat Completions v1	OpenAI Responses API & Chat Completions v1
Release Date	2025-04	2024-07
Knowledge Cutoff	June 2024	October 2023
Architecture	Dense multimodal transformer (nano)	Small Multimodal Dense
Parameters	Confidential	Confidential
Enterprise Rate Limits (RPM / TPM)	10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM	10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)	Flat rate ($0.1/1M in) across full 1048K window	Flat rate ($0.15/1M in) across full 128K window
Prompt Cache Policy & TTL	Automatic prefix caching at $0.025/1M (no extra cache-write fee)	Automatic prefix caching at $0.075/1M (no extra cache-write fee)
Computer Use / GUI Agent	Supported (Screen coordinate grounding, browser & desktop GUI action loops)	Supported (Screen coordinate grounding, browser & desktop GUI action loops)
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	Native Web Search tool + Hosted Code Interpreter & File Search RAG	Native Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning Support	Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)	Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
Multimodal Token Billing	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAM	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier Classification	OpenAI Preparedness Framework Audited & System Card Published	OpenAI Preparedness Framework Audited & System Card Published
Enterprise Compliance (ZDR/HIPAA/SOC2)	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalities	text, vision	text, vision
Output Modalities	text	text

GPT-4.1 nano vs GPT-4o mini: Risposte Rapide (FAQ)

Which is more intelligent, GPT-4.1 nano or GPT-4o mini?

GPT-4o mini is more intelligent. It scores 30 compared with 26 on the composite Intelligence Index.

Which model is faster?

GPT-4.1 nano is faster, generating 220 tokens per second compared with 140 tokens per second.

Which model is cheaper to run?

GPT-4.1 nano is cheaper at $0.08 per 1M tokens blended, compared with $0.14 (7:2:1 cache:input:output ratio).

Which model has lower latency?

GPT-4.1 nano has lower latency, with a time to first token of 0.22s compared with 0.31s.

Which model has a larger context window?

GPT-4.1 nano has a larger context window, supporting 1.05M tokens compared with 128K tokens.

Verdetto

Sia GPT-4.1 nano (Ideale per la massima efficienza dei costi per token e l’inferenza rapida in produzione, sistemi RAG aziendali e integrazioni API scalabili.) sia GPT-4o mini (Ideale per la massima efficienza dei costi per token e l’inferenza rapida in produzione, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: GPT-4o mini guida nei benchmark di ragionamento e programmazione, GPT-4.1 nano garantisce una generazione più rapida e GPT-4.1 nano offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, GPT-4.1 nano o GPT-4o mini?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale GPT-4o mini. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra GPT-4.1 nano e GPT-4o mini?+

GPT-4.1 nano costa $0.10 / $0.40 per milione di token di input/output, mentre GPT-4o mini costa $0.15 / $0.60. Nel costo combinato, GPT-4.1 nano risulta più economico.

Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+

GPT-4.1 nano supporta 1.05M token di contesto contro 128K token di GPT-4o mini, mentre GPT-4.1 nano offre una velocità di generazione superiore.

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)

GPT-4.1 nano vs GPT-4o mini

Cambia e Confronta Qualsiasi Modello

GPT-4.1 nano

GPT-4o mini

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

GPT-4.1 nano · Strengths & Key Advantages

Considerations & Trade-offs

GPT-4o mini · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

GPT-4.1 nano vs GPT-4o mini: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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