Head-to-Head Model Comparison

GPT-4.1 vs GPT-4.1 mini

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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

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GPT-4.1Profile

GPT-4.1 miniProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

OpenAI

GPT-4.1

Modello Ammiraglia di Frontiera

GPT-4.1 di OpenAI è un modello di IA progettato per il ragionamento aziendale di frontiera, la programmazione avanzata e i carichi di produzione, con finestra di contesto da 1.0M, velocità di 110 token/s, indice di intelligenza di 48/100 e

Intelligence Index48 / 100

Input Token:

$2.00 / 1M

Output Token:

$8.00 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

54.6%

OpenAI

GPT-4.1 mini

Modello Leggero ed Economico

GPT-4.1 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 1.0M, velocità di 165 token/s, indice di intelligenza di 36/100 e tariffa di

Intelligence Index36 / 100

Input Token:

$0.40 / 1M

Output Token:

$1.60 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

36.5%

Differenze Chiave a Colpo d’Occhio

GPT-4.1 mini is 5.0x cheaper for API usage (input + output tokens combined).
GPT-4.1 mini is faster at 165 tok/s vs 110 tok/s.
GPT-4.1 leads on SWE-bench coding (54.6% vs 36.5%).

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

Feature	GPT-4.1	GPT-4.1 mini
Intelligence Index Composite reasoning & knowledge score (0-100)	48	36
Provider	OpenAI	OpenAI
Input Price Cost per million input tokens	$2.00 / 1M	$0.40 / 1M
Output Price Cost per million output tokens	$8.00 / 1M	$1.60 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$1.55 / 1M	$0.31 / 1M
Context Window Maximum input + output tokens	1.05M tokens~1,571 A4 pages of text	1.05M tokens~1,571 A4 pages of text
Max Output Tokens Per single request	33K	33K
Throughput Speed Tokens generated per second	110 tok/s	165 tok/s
SWE-bench Verified Real-world software engineering	54.6%	36.5%
GPQA Diamond Graduate-level reasoning	65%	49.5%
Cached Input Price Prompt caching discount per 1M tokens	$0.50 / 1M	$0.10 / 1M

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

GPT-4.1

+18.1% SWE-bench lead

Cost

GPT-4.1 mini

5.0x cheaper monthly runtime

Speed

GPT-4.1 mini

+50% faster generation

Context

Tied

1.05M tokens window

Punteggi Benchmark Verificati

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

GPT-4.1GPT-4.1 mini

SWE-bench Verified (Autonomous Coding)

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

54.6%vs36.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

65%vs49.5%

MMLU-Pro (Multi-task Language Understanding)

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

81%vs68%

LMSYS Chatbot Arena Elo

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

1338ptsvs1258pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

9.8%vs6.2%

Terminal-Bench (Agentic Coding & Terminal Use)

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

30%vs19%

SciCode (Scientific Computing)

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

36.5%vs24.5%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

58.5%vs38%

AIME (Advanced Math Competition)

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

55.5%vs42%

Long-Context Reasoning

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

70%vs57.5%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

110 tok/s vs165 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-4.10.48s

GPT-4.1 mini0.30s

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

GPT-4.15.03s

GPT-4.1 mini3.33s

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 Monthly API Bill

$10.95 / month

$2/1M in · $8/1M out

GPT-4.1 mini Monthly API Bill

$2.19 / month

$0.4/1M in · $1.6/1M out

GPT-4.1 mini saves approximately $8.76 / month (80%) under this workload.5.0x Savings

GPT-4.1 · Strengths & Key Advantages

Finestra di contesto da 1.0M con capacità massima di output fino a 33K token (SWE-bench Verified 54.6% · GPQA Diamond 65%)
Throughput elevato di 110 token/secondo con latenza al primo token (TTFT) di soli 480 ms
Economia API competitiva ($2/$8 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
Per volumi di traffico elevati in produzione si consiglia di attivare il prompt caching e la Batch API

GPT-4.1 mini · Strengths & Key Advantages

Finestra di contesto da 1.0M con capacità massima di output fino a 33K token (SWE-bench Verified 36.5% · GPQA Diamond 49.5%)
Throughput elevato di 165 token/secondo con latenza al primo token (TTFT) di soli 300 ms
Economia API competitiva ($0.4/$1.6 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	GPT-4.1 mini
Cost per Task Estimated USD to finish one task	$0.72	$0.14
Output Tokens / Task Tokens generated per task	52K	48K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	GPT-4.1	GPT-4.1 mini
Official API Model ID (SDK)	gpt-4-1	gpt-4-1-mini
API Protocol	OpenAI Responses API & Chat Completions v1	OpenAI Responses API & Chat Completions v1
Release Date	2025-04	2025-04
Knowledge Cutoff	June 2024	June 2024
Architecture	Dense multimodal transformer	Dense multimodal transformer (small)
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) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)	Flat rate ($2/1M in) across full 1048K window	Flat rate ($0.4/1M in) across full 1048K window
Prompt Cache Policy & TTL	Automatic prefix caching at $0.5/1M (no extra cache-write fee)	Automatic prefix caching at $0.1/1M (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 vs GPT-4.1 mini: Risposte Rapide (FAQ)

Which is more intelligent, GPT-4.1 or GPT-4.1 mini?

GPT-4.1 is more intelligent. It scores 48 compared with 36 on the composite Intelligence Index.

Which model is faster?

GPT-4.1 mini is faster, generating 165 tokens per second compared with 110 tokens per second.

Which model is cheaper to run?

GPT-4.1 mini is cheaper at $0.31 per 1M tokens blended, compared with $1.55 (7:2:1 cache:input:output ratio).

Which model has lower latency?

GPT-4.1 mini has lower latency, with a time to first token of 0.30s compared with 0.48s.

Verdetto

Sia GPT-4.1 (Ideale per il ragionamento aziendale di frontiera, la programmazione avanzata e i carichi di produzione, sistemi RAG aziendali e integrazioni API scalabili.) sia GPT-4.1 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-4.1 guida nei benchmark di ragionamento e programmazione, GPT-4.1 mini garantisce una generazione più rapida e GPT-4.1 mini offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

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

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale GPT-4.1. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

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

GPT-4.1 costa $2.00 / $8.00 per milione di token di input/output, mentre GPT-4.1 mini costa $0.40 / $1.60. Nel costo combinato, GPT-4.1 mini risulta più economico.

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

GPT-4.1 supporta 1.05M token di contesto contro 1.05M token di GPT-4.1 mini, mentre GPT-4.1 mini 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 vs GPT-4.1 mini

Cambia e Confronta Qualsiasi Modello

GPT-4.1

GPT-4.1 mini

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

GPT-4.1 · Strengths & Key Advantages

Considerations & Trade-offs

GPT-4.1 mini · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

GPT-4.1 vs GPT-4.1 mini: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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