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Gemini 3.1 Flash-Lite vs GPT-5.4

Compare Gemini 3.1 Flash-Lite and GPT-5.4 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: 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

GPT-5.4 is a proprietary multimodal large language model developed by OpenAI and released on March 5, 2026. It is designed for professional workloads such as advanced software development, research, and agentic automation. The model combines the general reasoning capabilities of the GPT-5 series with software engineering improvements derived from GPT-5.3-Codex. In the API and Codex environments it supports context windows of up to 1 million tokens, enabling long-context reasoning and large-scale code or document workflows.

Compared with GPT-5.2, GPT-5.4 reduces false individual claims by 33% and lowers overall response errors by 18%, improving factual reliability across complex tasks. It is also the first general-purpose OpenAI release with native computer-use capabilities, allowing agents to interact with desktops, browsers, and external applications to complete multi-step workflows. The model family includes three variants: GPT-5.4 (standard), GPT-5.4 Pro for higher-performance workloads, and GPT-5.4 Thinking, a reasoning-oriented version in ChatGPT that presents an upfront plan before generating its response. The API also introduces a Tool Search system that allows models to retrieve tool definitions dynamically, reducing token usage in tool-heavy integrations.

Gemini 3.1 Flash-Lite vs GPT-5.4 Comparison Table

PropertyGemini 3.1 Flash-LiteGPT-5.4
OrganizationGoogleOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateMar 2026Mar 2026
Context Window1.0M1.1M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.250$2.50
Output $/1M$1.50$15.00
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 Time1.86s7.16s
Median input tokensincl. image tokens1.1K1.4K
Median output tokens6108
Est. cost / taskon this benchmark$0.0003$0.0052
Defect Detection
73.3%(11/15)
86.7%(13/15)
Document Understanding
77.8%(7/9)
88.9%(8/9)
Object Counting
30%(3/10)
40%(4/10)
Object Understanding
64.3%(9/14)
85.7%(12/14)
Spatial Understanding
84.2%(16/19)
78.9%(15/19)
OCR
Overall Score
89.96%
79.48%
Avg Response Time1.32s3.98s
Median input tokensincl. image tokens1.1K105
Median output tokens1095
Est. cost / taskon this benchmark$0.0003$0.0017
Focused Scene OCR
91.9%(91/99)
75.8%(75/99)
Handwritten Math
80%(8/10)
60%(6/10)
License Plate Recognition
100%(30/30)
90%(27/30)
Text Recognition
90%(27/30)
83.3%(25/30)
VQA & Extraction
83.3%(50/60)
81.7%(49/60)

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