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GPT-4.1 vs GPT-5 Mini

Compare GPT-4.1 and GPT-5 Mini side-by-side. See how these vision models stack up in Open Prompt, Image Captioning, OCR, Object Detection, and Classification.

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OpenAIGPT-4.1

GPT-4.1 is deprecated and can no longer be run. Details and evals are still available on its model page.

OpenAIGPT-5 Mini
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Models in this comparison

OpenAI

GPT-4.1 vs GPT-5 Mini: Overview

GPT-4.1

GPT-4.1, released by OpenAI in April 2025, is a multimodal large language model that advances the GPT-4 series with major improvements in coding, reasoning, and instruction following. It accepts both text and images, supports tool calling and structured outputs, and features an expanded context window of up to ~1 million tokens—enabling it to process very large documents, multi-file codebases, or long conversations in a single prompt. Its knowledge is current through June 2024.

The GPT-4.1 family includes standard, mini, and nano variants, offering trade-offs between performance, cost, and latency. While parameter counts remain undisclosed, the series improves efficiency and responsiveness compared to GPT-4, making it suitable for both enterprise-scale tasks and cost-sensitive applications. Common use cases include software development, technical research, knowledge management, multimodal analysis, and high-context enterprise assistants.

GPT-5 Mini

GPT-5 Mini, released by OpenAI on August 7, 2025, is a mid-tier variant of the GPT-5 family that balances cost, speed, and capability. It is multimodal, supporting both text and image inputs, and offers a substantial input context window of ~400,000 tokens with output lengths up to ~128,000 tokens. While less powerful than the full GPT-5, it inherits its safety tuning, instruction-following improvements, and multimodal reasoning, making it a practical choice for developers who need large context handling without the expense of premium models.

GPT-5 Mini is optimized for affordability while retaining strong reasoning performance. Benchmarks show it outperforming earlier models such as GPT-4o on many multimodal and medical VQA tasks, though it lags behind GPT-5 on the most complex problems. Ideal use cases include prototyping, scalable content generation, document analysis, and mid-range reasoning tasks where efficiency and context capacity matter more than top-tier accuracy.

GPT-4.1 vs GPT-5 Mini Comparison Table

PropertyGPT-4.1GPT-5 Mini
OrganizationOpenAIOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateApr 2025Aug 2025
Context Window1.0M400K
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$2.00$0.250
Output $/1M$8.00$2.00
Vision Tasks
CaptioningDemo
ClassificationDemo
Object DetectionDemo
OCRDemo
Vision Language
Visual Question AnsweringDemo
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
73.13%
Avg Response Time11.72s
Median input tokensincl. image tokens1.4K
Median output tokens143
Est. cost / taskon this benchmark$0.0006
Defect Detection
80%(12/15)
Document Understanding
77.8%(7/9)
Object Counting
10%(1/10)
Object Understanding
85.7%(12/14)
Spatial Understanding
89.5%(17/19)
OCR
Overall Score
76.86%
Avg Response Time4.63s
Median input tokensincl. image tokens105
Median output tokens209
Est. cost / taskon this benchmark$0.0004
Focused Scene OCR
72.7%(72/99)
Handwritten Math
50%(5/10)
License Plate Recognition
93.3%(28/30)
Text Recognition
80%(24/30)
VQA & Extraction
78.3%(47/60)

Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology