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Gemini 3.8 Flash vs GPT-4o mini

Compare Gemini 3.8 Flash and GPT-4o mini side-by-side. See how these vision models stack up in Object Detection, Image Captioning, OCR, Classification, and Open Prompt.

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GoogleGemini 3.8 Flash
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OpenAIGPT-4o mini

GPT-4o mini is deprecated and can no longer be run. Details and evals are still available on its model page.

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Gemini 3.8 Flash vs GPT-4o mini Comparison Table

Evals updated September 5, 2026Pricing updated September 20, 2026

PropertyGemini 3.8 FlashGPT-4o mini
OrganizationGoogleOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateSep 2026Jul 2024
Context Window1.0M128K
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.750$0.150
Output $/1M$3.75$0.600
Vision Tasks
CaptioningDemo
Chart Question Answering
ClassificationDemo
Document Question Answering
Image Tagging
Multi-Label Classification
Object DetectionDemo
OCRDemo
Vision Language
Visual Question AnsweringDemo
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort
Overall
85.1%
Deprecated
Avg cost / sample$0.0033
Avg speed / sample11.65s
By task
Object Detection (low)
68.1%
±0.8, Mean of 3 runs, range 67.3 to 69.0
$0.0041
Object Detection (high)
74.8%
±1.1, Mean of 3 runs, range 73.4 to 75.6
$0.021
Counting (low)
78.8%
±2.7, Mean of 3 runs, range 75.7 to 81.1
$0.0036
Counting (high)
79.3%
±1.4, Mean of 3 runs, range 78.4 to 81.1
$0.024
Identification (low)
97.9%
±1.6, Mean of 3 runs, range 96.9 to 100.0
$0.0014
Identification (high)
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.0034
OCR (low)
87.3%
±0.8, Mean of 3 runs, range 86.5 to 88.2
$0.0024
OCR (high)
88.8%
±0.7, Mean of 3 runs, range 88.0 to 89.4
$0.064
Data Extraction (low)
97.3%
±0.5, Mean of 3 runs, range 96.9 to 97.9
$0.0018
Data Extraction (high)
94.8%
±1.0, Mean of 3 runs, range 93.8 to 95.9
$0.0089
Reasoning (low)
81.2%
±0.3, Mean of 3 runs, range 80.8 to 81.5
$0.0034
Reasoning (high)
84.5%
±1.0, Mean of 3 runs, range 83.4 to 85.4
$0.021

Gemini 3.8 Flash vs GPT-4o mini: Overview

Gemini 3.8 Flash

Gemini 3.8 Flash is a natively multimodal reasoning model in Google's Gemini 3 series, positioned as the speed and cost oriented Flash tier while targeting long-horizon software engineering, autonomous agents, and enterprise workflows. It accepts text, images, video, audio, and PDF documents in a single request and returns text, with an input limit of 1,048,576 tokens and an output limit of 65,536 tokens. Thinking is configurable at low, medium, and high levels, and the model supports function calling, code execution, structured outputs, context caching, search and Maps grounding, file search, and computer use in preview. Image generation, audio generation, and the Live API are not supported.

On vision oriented evaluations the model reports 86.2% on CharXiv Reasoning for chart and figure synthesis and 87.8% on LVBench for long video understanding in agentic mode, alongside 90.8% on Terminal-Bench 2.1 and 61.6% on SWE-Bench Pro for coding. Following Gemini API conventions, it can localize objects by emitting bounding boxes as [ymin, xmin, ymax, xmax] integers normalized to a 0 to 1000 range, which supports prompt driven detection and grounding in addition to captioning, document parsing, and visual question answering. The knowledge cutoff is March 2026, though coverage in some domains reflects the January 2025 cutoff shared across the Gemini 3 family.

GPT-4o mini

GPT-4o mini, launched by OpenAI in July 2024, is a lightweight, cost-efficient variant of GPT-4o designed for developers who need strong multimodal reasoning at scale. It supports text and vision inputs (with audio/video support planned) and offers a 128,000-token input context window with outputs up to ~16,000 tokens. Like GPT-4o, it has a knowledge cutoff of October 2023 and integrates the same safety mitigations against misuse and prompt attacks.

GPT-4o mini is significantly cheaper than full GPT-4o while outperforming older models such as GPT-3.5 Turbo. It achieves around 82% on MMLU, reflecting solid reasoning, math, and coding capabilities despite its efficiency focus. The model replaced GPT-3.5 Turbo as the default in ChatGPT for many users, making it widely accessible for everyday conversational AI, educational tools, content generation, and scalable multimodal applications where affordability and speed are priorities.