Head-to-Head Model Comparison

o4-mini vs o1-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.

o4-miniProfile

o1-miniProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

OpenAI

o4-mini

Motore di Ragionamento Profondo

o4-mini di OpenAI è un modello di IA progettato per il ragionamento logico profondo, la matematica avanzata e l’analisi scientifica, con finestra di contesto da 200K, velocità di 105 token/s, indice di intelligenza di 50/100 e tariffa di $1

Intelligence Index50 / 100

Input Token:

$1.10 / 1M

Output Token:

$4.40 / 1M

Context Window:

200K tokens

SWE-bench Verified:

57.5%

OpenAI

o1-mini

Motore di Ragionamento Profondo

o1-mini di OpenAI è un modello di IA progettato per il ragionamento logico profondo, la matematica avanzata e l’analisi scientifica, con finestra di contesto da 128K, velocità di 90 token/s, indice di intelligenza di 44/100 e tariffa di $1.

Intelligence Index44 / 100

Input Token:

$1.10 / 1M

Output Token:

$4.40 / 1M

Context Window:

128K tokens

SWE-bench Verified:

44%

Differenze Chiave a Colpo d’Occhio

o4-mini is 1.1x cheaper for API usage (input + output tokens combined).
o4-mini has a larger context window (200K vs 128K tokens).
o4-mini is faster at 105 tok/s vs 90 tok/s.
o4-mini leads on SWE-bench coding (57.5% vs 44.0%).

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

Feature	o4-mini	o1-mini
Intelligence Index Composite reasoning & knowledge score (0-100)	50	44
Provider	OpenAI	OpenAI
Input Price Cost per million input tokens	$1.10 / 1M	$1.10 / 1M
Output Price Cost per million output tokens	$4.40 / 1M	$4.40 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.85 / 1M	$1.05 / 1M
Context Window Maximum input + output tokens	200K tokens~300 A4 pages of text	128K tokens~192 A4 pages of text
Max Output Tokens Per single request	100K	66K
Throughput Speed Tokens generated per second	105 tok/s	90 tok/s
SWE-bench Verified Real-world software engineering	57.5%	44%
GPQA Diamond Graduate-level reasoning	73%	68.5%
Cached Input Price Prompt caching discount per 1M tokens	$0.28 / 1M	$0.55 / 1M

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

o4-mini

+13.5% SWE-bench lead

Cost

o4-mini

1.1x cheaper monthly runtime

Speed

o4-mini

+17% faster generation

Context

o4-mini

200K tokens window

Punteggi Benchmark Verificati

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

o4-minio1-mini

SWE-bench Verified (Autonomous Coding)

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

57.5%vs44%

GPQA Diamond (Doctorate-Level Science & Math)

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

73%vs68.5%

MMLU-Pro (Multi-task Language Understanding)

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

83%vs79%

LMSYS Chatbot Arena Elo

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

1325ptsvs1330pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

11.2%vs8.6%

Terminal-Bench (Agentic Coding & Terminal Use)

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

36%vs30.5%

SciCode (Scientific Computing)

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

38.5%vs36%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

65%vs60.5%

AIME (Advanced Math Competition)

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

89.5%vs88%

Long-Context Reasoning

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

74.5%vs70.5%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

105 tok/s vs90 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

o4-mini1.80s

o1-mini2.20s

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

o4-mini6.56s

o1-mini7.76s

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)

o4-mini Monthly API Bill

$6.02 / month

$1.1/1M in · $4.4/1M out

o1-mini Monthly API Bill

$6.44 / month

$1.1/1M in · $4.4/1M out

o4-mini saves approximately $0.41 / month (6%) under this workload.1.1x Savings

o4-mini · Strengths & Key Advantages

Finestra di contesto da 200K con capacità massima di output fino a 100K token (SWE-bench Verified 57.5% · GPQA Diamond 73%)
Throughput elevato di 105 token/secondo con latenza al primo token (TTFT) di soli 1800 ms
Economia API competitiva ($1.1/$4.4 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

o1-mini · Strengths & Key Advantages

Finestra di contesto da 128K con capacità massima di output fino a 66K token (SWE-bench Verified 44% · GPQA Diamond 68.5%)
Throughput elevato di 90 token/secondo con latenza al primo token (TTFT) di soli 2200 ms
Economia API competitiva ($1.1/$4.4 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

Token Use & Task Economics

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

Metric	o4-mini	o1-mini
Cost per Task Estimated USD to finish one task	$0.72	$0.85
Output Tokens / Task Tokens generated per task	45K	45K
Reasoning Tokens / Task Thinking tokens burned per task	25K	26K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	o4-mini	o1-mini
Official API Model ID (SDK)	o4-mini	o1-mini
API Protocol	OpenAI Responses API & Chat Completions v1	OpenAI Responses API & Chat Completions v1
Release Date	2025-04	2024-09
Knowledge Cutoff	June 2024	October 2023
Architecture	Compact reasoning transformer	Reasoning transformer with hidden chain-of-thought
Parameters	Confidential	Confidential
Enterprise Rate Limits (RPM / TPM)	10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM	10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)	Flat rate ($1.1/1M in) across full 200K window	Flat rate ($1.1/1M in) across full 128K window
Prompt Cache Policy & TTL	Automatic prefix caching at $0.275/1M (no extra cache-write fee)	Automatic prefix caching at $0.55/1M (no extra cache-write fee)
Computer Use / GUI Agent	Supported (Screen coordinate grounding, browser & desktop GUI action loops)	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	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)
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
Output Modalities	text	text

o4-mini vs o1-mini: Risposte Rapide (FAQ)

Which is more intelligent, o4-mini or o1-mini?

o4-mini is more intelligent. It scores 50 compared with 44 on the composite Intelligence Index.

Which model is faster?

o4-mini is faster, generating 105 tokens per second compared with 90 tokens per second.

Which model is cheaper to run?

o4-mini is cheaper at $0.85 per 1M tokens blended, compared with $1.05 (7:2:1 cache:input:output ratio).

Which model has lower latency?

o4-mini has lower latency, with a time to first token of 1.80s compared with 2.20s.

Which model has a larger context window?

o4-mini has a larger context window, supporting 200K tokens compared with 128K tokens.

Verdetto

Sia o4-mini (Ideale per il ragionamento logico profondo, la matematica avanzata e l’analisi scientifica, sistemi RAG aziendali e integrazioni API scalabili.) sia o1-mini (Ideale per il ragionamento logico profondo, la matematica avanzata e l’analisi scientifica, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: o4-mini guida nei benchmark di ragionamento e programmazione, o4-mini garantisce una generazione più rapida e o4-mini offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, o4-mini o o1-mini?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale o4-mini. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra o4-mini e o1-mini?+

o4-mini costa $1.10 / $4.40 per milione di token di input/output, mentre o1-mini costa $1.10 / $4.40. Nel costo combinato, o4-mini risulta più economico.

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

o4-mini supporta 200K token di contesto contro 128K token di o1-mini, mentre o4-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)

o4-mini vs o1-mini

Cambia e Confronta Qualsiasi Modello

o4-mini

o1-mini

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

o4-mini · Strengths & Key Advantages

Considerations & Trade-offs

o1-mini · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

o4-mini vs o1-mini: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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