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Gemini 3.8 Flash vs GPT-5.4 Mini

Compare Gemini 3.8 Flash and GPT-5.4 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-5.4 Mini
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Models in this comparison

Gemini 3.8 Flash vs GPT-5.4 Mini on Vision Evals

Gemini 3.8 Flash scores higher on 5 of the six Vision Evals tasks.

The widest gap is Object Detection, where Gemini 3.8 Flash leads 68.1% to 16.1%.

Overall, Gemini 3.8 Flash averages 85.1% (#3 of 36) against 64.7% (#30 of 36) for GPT-5.4 Mini.

GPT-5.4 Mini is both cheaper ($0.0030 vs $0.0033 per sample) and faster (5.3s vs 11.6s per sample).

Gemini 3.8 FlashGPT-5.4 Mini

Gemini 3.8 Flash vs GPT-5.4 Mini Comparison Table

Evals updated September 2, 2026Pricing updated September 2, 2026

PropertyGemini 3.8 FlashGPT-5.4 Mini
OrganizationGoogleOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateSep 2026Mar 2026
Context Window1.0M400K
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.750
Output $/1M$4.50
Vision Tasks
CaptioningDemoDemo
Chart Question Answering
ClassificationDemoDemo
Document Question Answering
Image Tagging
Multi-Label Classification
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
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%
64.7%
Avg cost / sample$0.0033$0.0030
Avg speed / sample11.65s5.35s
By task
Object Detection (low)
68.1%
±0.8, Mean of 3 runs, range 67.3 to 69.0
$0.0041
16.1%
$0.0044
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
60.8%
$0.0019
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
84.4%
$0.0013
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
88.1%
$0.0042
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
83.5%
$0.0014
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
55.6%
$0.0023
Reasoning (high)
84.5%
±1.0, Mean of 3 runs, range 83.4 to 85.4
$0.021
62.9%
$0.0081

Gemini 3.8 Flash vs GPT-5.4 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-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.

Frequently Asked Questions

On Roboflow's Vision Evals, Gemini 3.8 Flash performed better. It scores higher on 5 of the six vision tasks and averages 85.1% (#3 of 36) against 64.7% (#30 of 36) for GPT-5.4 Mini. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

Yes. On the Vision Evals Object Detection benchmark at low effort, Gemini 3.8 Flash leads with 68.1% against 16.1%. This is the widest gap between the two models across the benchmark's tasks.

GPT-5.4 Mini is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0030 per sample against $0.0033. Actual costs depend on your image sizes, prompts, and output length.

GPT-5.4 Mini is faster. Across Roboflow's Vision Evals it averaged 5.3s per inference against 11.6s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.

Yes. The comparison demo on this page runs both models on the same image side by side for object detection and image captioning in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.