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Head-to-Head Model Comparison

o4-mini vs o3-mini

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado por el equipo de investigación de GetInfoUsÚltima revisión: October 2026Política editorial
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
o4-mini vs o3-mini: Benchmarks, Pricing & Speed Comparison

o4-mini de OpenAI y o3-mini de OpenAI compiten directamente en el ecosistema actual de IA. o4-mini ofrece una ventana de contexto de 200K tokens a $1.10/$4.40 por millón de tokens (entrada/salida), mientras que o3-mini ofrece 200K tokens de contexto a $1.10/$4.40 por millón de tokens. Analizamos sus benchmarks, latencia y costos reales.

Comparación Directa (VS)

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Popular Matchups:
OpenAI

o4-mini

Motor de Razonamiento Profundo

o4-mini de OpenAI es un modelo de IA diseñado para razonamiento lógico profundo, matemáticas avanzadas y resolución científica paso a paso, con ventana de contexto de 200K, velocidad de 105 tokens/s, índice de inteligencia de 50/100 y tarif

Intelligence Index50 / 100
Input Token:

$1.10 / 1M

Output Token:

$4.40 / 1M

Context Window:

200K tokens

SWE-bench Verified:

57.5%

OpenAI

o3-mini

Motor de Razonamiento Profundo

o3-mini de OpenAI es un modelo de IA diseñado para razonamiento lógico profundo, matemáticas avanzadas y resolución científica paso a paso, con ventana de contexto de 200K, velocidad de 85 tokens/s, índice de inteligencia de 51/100 y tarifa

Intelligence Index51 / 100
Input Token:

$1.10 / 1M

Output Token:

$4.40 / 1M

Context Window:

200K tokens

SWE-bench Verified:

49%

Diferencias Clave de un Vistazo

  • o4-mini is 1.1x cheaper for API usage (input + output tokens combined).
  • o4-mini is faster at 105 tok/s vs 85 tok/s.
  • o4-mini leads on SWE-bench coding (57.5% vs 49.0%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
Característicao4-minio3-mini
Intelligence Index
Composite reasoning & knowledge score (0-100)
5051
Provider
OpenAIOpenAI
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 text200K tokens~300 A4 pages of text
Max Output Tokens
Per single request
100K100K
Throughput Speed
Tokens generated per second
105 tok/s85 tok/s
SWE-bench Verified
Real-world software engineering
57.5%49%
GPQA Diamond
Graduate-level reasoning
73%79.7%
Cached Input Price
Prompt caching discount per 1M tokens
$0.28 / 1M$0.55 / 1M

¿Quién gana en cada área?

At-a-glance decision framework
Código
o4-mini
+8.5% SWE-bench lead
Costo
o4-mini
1.1x cheaper monthly runtime
Velocidad
o4-mini
+24% faster generation
Contexto
Tied
200K tokens window

Puntuaciones de Benchmarks Verificadas

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

o4-minio3-mini
SWE-bench Verified (Autonomous Coding)

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

57.5%vs49%
GPQA Diamond (Doctorate-Level Science & Math)

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

73%vs79.7%
MMLU-Pro (Multi-task Language Understanding)

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

83%vs82.4%
LMSYS Chatbot Arena Elo

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

1325ptsvs1310pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

11.2%vs12.8%
Terminal-Bench (Agentic Coding & Terminal Use)

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

36%vs32.3%
SciCode (Scientific Computing)

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

38.5%vs29.4%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

65%vs51.6%
AIME (Advanced Math Competition)

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

89.5%vs91.7%
Long-Context Reasoning

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

74.5%vs74.2%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

105 tok/s vs85 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
o4-mini1.80s
o3-mini1.40s
Est. End-to-End Response (500 tokens)TTFT + generation time
o4-mini6.56s
o3-mini7.28s

Simulador Interactivo de Costos de Tokens

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

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
o4-mini Monthly API Bill
$6.02 / month
$1.1/1M in · $4.4/1M out
o3-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

  • Ventana de contexto de 200K con hasta 100K tokens de salida máxima (SWE-bench Verified 57.5% · GPQA Diamond 73%)
  • Rendimiento ágil de 105 tokens/segundo con latencia inicial (TTFT) de solo 1800 ms
  • Economía de API competitiva ($1.1/$4.4 por 1M de tokens) con soporte para caché de prompts y salidas JSON estructuradas
Considerations & Trade-offs
  • Modelo propietario en la nube; no permite descargar los pesos para ejecución local sin conexión
  • En demostraciones científicas extremas, los modelos de razonamiento pesado de gama alta ofrecen mayor profundidad

o3-mini · Strengths & Key Advantages

  • Ventana de contexto de 200K con hasta 100K tokens de salida máxima (SWE-bench Verified 49% · GPQA Diamond 79.7%)
  • Rendimiento ágil de 85 tokens/segundo con latencia inicial (TTFT) de solo 1400 ms
  • Economía de API competitiva ($1.1/$4.4 por 1M de tokens) con soporte para caché de prompts y salidas JSON estructuradas
Considerations & Trade-offs
  • Modelo propietario en la nube; no permite descargar los pesos para ejecución local sin conexión
  • 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.

Metrico4-minio3-mini
Cost per Task
Estimated USD to finish one task
$0.72$1.30
Output Tokens / Task
Tokens generated per task
45K45K
Reasoning Tokens / Task
Thinking tokens burned per task
25K25K

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

Característicao4-minio3-mini
Official API Model ID (SDK)o4-minio3-mini
API ProtocolOpenAI Responses API & Chat Completions v1OpenAI Responses API & Chat Completions v1
Release Date2025-042025-01
Knowledge CutoffJune 2024October 2023
ArchitectureCompact reasoning transformerChain-of-Thought Reasoning Dense
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM10,000 – 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 windowFlat rate ($1.1/1M in) across full 200K window
Prompt Cache Policy & TTLAutomatic 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 AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxNative Web Search tool + Hosted Code Interpreter & File Search RAGNative Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning SupportSupervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
Multimodal Token BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMCloud-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 ClassificationOpenAI Preparedness Framework Audited & System Card PublishedOpenAI Preparedness Framework Audited & System Card Published
Enterprise Compliance (ZDR/HIPAA/SOC2)SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise TierSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestext, visiontext
Output Modalitiestexttext

o4-mini vs o3-mini: Respuestas Rápidas (FAQ)

Which is more intelligent, o4-mini or o3-mini?

o3-mini is more intelligent. It scores 51 compared with 50 on the composite Intelligence Index.

Which model is faster?

o4-mini is faster, generating 105 tokens per second compared with 85 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?

o3-mini has lower latency, with a time to first token of 1.40s compared with 1.80s.

Veredicto

Tanto o4-mini (Ideal para razonamiento lógico profundo, matemáticas avanzadas y resolución científica paso a paso, sistemas RAG empresariales y automatización mediante API.) como o3-mini (Ideal para razonamiento lógico profundo, matemáticas avanzadas y resolución científica paso a paso, sistemas RAG empresariales y automatización mediante API.) destacan en producción: o3-mini lidera en inteligencia general y programación, o4-mini ofrece mayor velocidad de generación de tokens y o4-mini resulta más rentable para cargas de trabajo API de alto volumen.

Preguntas frecuentes

¿Cuál es mejor para programación y razonamiento, o4-mini o o3-mini?+

En las pruebas de inteligencia compuesta y resolución de código, o3-mini obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.

¿Cuánto cuesta la API de o4-mini frente a o3-mini?+

o4-mini cuesta $1.10 / $4.40 por millón de tokens de entrada/salida, mientras que o3-mini cuesta $1.10 / $4.40. En costo combinado, o4-mini es la opción más económica.

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

o4-mini cuenta con 200K tokens de contexto frente a 200K tokens de o3-mini. En velocidad de inferencia, o4-mini 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 & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.

Fuentes: Documentación oficial de API de OpenAI y OpenAI, tarjetas de sistema y benchmarks independientes (2026).