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GPT-5.4 Mini vs Gemini 3 Flash+ 1 other

Compare GPT-5.4 Mini, Gemini 3 Flash, and 1 other vision model side-by-side. Test these models on Open Prompt, Object Detection, Classification, Image Captioning, and OCR in the Playground.

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OpenAIGPT-5.4 Mini
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GoogleGemini 3 Flash
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AnthropicClaude Haiku 4.5
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Models in this comparison

Model Overviews

GPT-5.4 mini is a fast, cost-efficient model developed by OpenAI and released on March 17, 2026, optimized for high-throughput workloads and subagent orchestration. It supports text and image inputs within a 400,000-token context window, making it ideal for processing extensive visual datasets and large codebases in a single request. Designed for low-latency production environments, the model integrates with key API features including function calling, web search, and tool-based computer use, allowing it to assist in automated workflows that require navigating digital interfaces.

Compared to the previous GPT-5 mini, this version runs more than twice as fast while approaching the performance levels of the flagship GPT-5.4 on reasoning and coding benchmarks. While the larger GPT-5.4 introduces native, state-of-the-art computer-use capabilities, GPT-5.4 mini provides a scalable alternative for interpreting screenshots and reasoning over dense UI layouts. For vision tasks on Playground, it excels at extracting structured information from visual documents and assisting in agentic tasks that involve real-time interpretation of software interfaces alongside text.

GPT-5.4 Mini vs Gemini 3 Flash Comparison Table + 1 other

PropertyGPT-5.4 MiniGemini 3 FlashClaude Haiku 4.5
OrganizationOpenAIGoogleAnthropic
Categoryclosedclosedclosed
Modalitymultimodalmultimodalmultimodal
Release DateMar 2026Dec 2025Oct 2025
Context Window400K1.0M200K
Parameters
LicenseProprietaryProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.750$0.500$1.00
Output $/1M$4.50$3.00$5.00
Vision Tasks
CaptioningDemoDemoDemo
ClassificationDemoDemoDemo
Object DetectionDemoDemoDemo
OCRDemoDemoDemo
Vision Language
Visual Question AnsweringDemoDemoDemo
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
77.61%
74.63%
58.21%
Avg Response Time5.80s9.85s3.15s
Median input tokensincl. image tokens1.4K1.1K2.2K
Median output tokens104290174
Est. cost / taskon this benchmark$0.0015$0.0014$0.0030
Defect Detection
73.3%(11/15)
73.3%(11/15)
80%(12/15)
Document Understanding
88.9%(8/9)
88.9%(8/9)
77.8%(7/9)
Object Counting
40%(4/10)
30%(3/10)
0%(0/10)
Object Understanding
92.9%(13/14)
85.7%(12/14)
71.4%(10/14)
Spatial Understanding
84.2%(16/19)
84.2%(16/19)
52.6%(10/19)
OCR
Overall Score
77.29%
93.01%
61.57%
Avg Response Time3.24s12.40s2.13s
Median input tokensincl. image tokens1051.1K735
Median output tokens126160101
Est. cost / taskon this benchmark$0.0006$0.0010$0.0012
Focused Scene OCR
75.8%(75/99)
94.9%(94/99)
61.6%(61/99)
Handwritten Math
40%(4/10)
100%(10/10)
20%(2/10)
License Plate Recognition
86.7%(26/30)
100%(30/30)
66.7%(20/30)
Text Recognition
73.3%(22/30)
86.7%(26/30)
63.3%(19/30)
VQA & Extraction
83.3%(50/60)
88.3%(53/60)
65%(39/60)

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