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Gemini 3.1 Flash-Lite vs GPT-5.4 Mini

Compare Gemini 3.1 Flash-Lite and GPT-5.4 Mini side-by-side. See how these vision models stack up in Object Detection, Classification, Image Captioning, Open Prompt, and OCR.

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GoogleGemini 3.1 Flash-Lite
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Gemini 3.1 Flash-Lite vs GPT-5.4 Mini: Overview

Gemini 3.1 Flash-Lite

Gemini 3.1 Flash-Lite is a natively multimodal reasoning model from Google DeepMind in the Gemini 3 series, based on the Gemini 3 Pro architecture. It processes text, image, video, audio, and PDF inputs within a 1 million token context window and produces text output up to 64K tokens. The model targets high-volume, latency-sensitive workloads and supports visual question answering, image and document data extraction, content moderation, classification, translation, automated speech recognition, and agentic data pipelines. It exposes configurable thinking levels of minimal, low, medium, and high, which set the depth of internal reasoning applied per request and let developers balance response quality against cost and latency.

On benchmarks reported at launch, Gemini 3.1 Flash-Lite scores 86.9% on GPQA Diamond and 76.8% on the MMMU Pro multimodal benchmark, and reaches an Elo score of 1432 on the Arena.ai leaderboard. According to Artificial Analysis benchmarks, it produces a 2.5 times faster time to first answer token and a 45% increase in output speed relative to Gemini 2.5 Flash. It also shows improved instruction following, higher audio input quality for automated speech recognition tasks, and support for structured JSON output used in data extraction pipelines.

GPT-5.4 Mini

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.

Gemini 3.1 Flash-Lite vs GPT-5.4 Mini Comparison Table

PropertyGemini 3.1 Flash-LiteGPT-5.4 Mini
OrganizationGoogleOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateMar 2026Mar 2026
Context Window1.0M400K
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.250$0.750
Output $/1M$1.50$4.50
Vision Tasks
CaptioningDemoDemo
ClassificationDemoDemo
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Document Question Answering
Image Tagging
Multi-Label Classification
Model Features
LLMs with Vision Capabilities
Multimodal Vision
Foundation Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
68.66%
77.61%
Avg Response Time1.86s5.80s
Median input tokensincl. image tokens1.1K1.4K
Median output tokens6104
Est. cost / taskon this benchmark$0.0003$0.0015
Defect Detection
73.3%(11/15)
73.3%(11/15)
Document Understanding
77.8%(7/9)
88.9%(8/9)
Object Counting
30%(3/10)
40%(4/10)
Object Understanding
64.3%(9/14)
92.9%(13/14)
Spatial Understanding
84.2%(16/19)
84.2%(16/19)
OCR
Overall Score
89.96%
77.29%
Avg Response Time1.32s3.24s
Median input tokensincl. image tokens1.1K105
Median output tokens10126
Est. cost / taskon this benchmark$0.0003$0.0006
Focused Scene OCR
91.9%(91/99)
75.8%(75/99)
Handwritten Math
80%(8/10)
40%(4/10)
License Plate Recognition
100%(30/30)
86.7%(26/30)
Text Recognition
90%(27/30)
73.3%(22/30)
VQA & Extraction
83.3%(50/60)
83.3%(50/60)

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