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Gemini 3.5 Flash-Lite vs GPT-5.4 Mini

Compare Gemini 3.5 Flash-Lite and GPT-5.4 Mini side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, Classification, Object Detection, and OCR.

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GoogleGemini 3.5 Flash-Lite
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OpenAIGPT-5.4 Mini
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Models in this comparison

Gemini 3.5 Flash-Lite vs GPT-5.4 Mini on Vision Evals

Gemini 3.5 Flash-Lite scores higher on 3 of the five Vision Evals tasks.

The widest gap is Object Detection, where Gemini 3.5 Flash-Lite leads 57.5% to 15.8%.

Overall, Gemini 3.5 Flash-Lite averages 61.7% (#33 of 63) against 53.2% (#51 of 63) for GPT-5.4 Mini.

Gemini 3.5 Flash-Lite is both cheaper ($0.0014 vs $0.0034 per sample) and faster (3.9s vs 5.5s per sample).

Gemini 3.5 Flash-LiteGPT-5.4 Mini

Gemini 3.5 Flash-Lite vs GPT-5.4 Mini Comparison Table

Evals updated October 13, 2026Pricing updated October 19, 2026

PropertyGemini 3.5 Flash-LiteGPT-5.4 Mini
OrganizationGoogleOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateJul 2026Mar 2026
Context Window1.0M400K
ParametersUnknownUnknown
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$0.300$0.750
Output $/1M$2.50$4.50
Vision Tasks
CaptioningDemoDemo
Chart Question AnsweringSupportedSupported
ClassificationDemoDemo
Document Question AnsweringSupportedSupported
Image TaggingSupportedSupported
Multi-Label ClassificationSupportedSupported
Object DetectionDemoDemo
OCRDemoDemo
Vision LanguageSupportedSupported
Visual Question AnsweringDemoDemo
Video ClassificationSupportedNot listed
Model Features
Foundation VisionSupportedSupported
LLMs with Vision CapabilitiesSupportedSupported
Multimodal VisionSupportedSupported
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
61.7%
53.2%
Avg cost / sample$0.0014$0.0034
Avg speed / sample3.92s5.51s
By task
Object Detection (low)
57.5%
$0.0023
15.8%
±0.4, Mean of 3 runs, range 15.3 to 16.1
$0.0044
Object Detection (high)–
16.6%
±0.8, Mean of 3 runs, range 15.8 to 17.4
$0.030
Counting (low)
52.7%
$0.0007
58.6%
±2.0, Mean of 3 runs, range 56.8 to 60.8
$0.0019
Counting (high)–
64.9%
±2.0, Mean of 3 runs, range 63.5 to 67.6
$0.0073
Identification (low)
84.4%
$0.0004
83.3%
±1.6, Mean of 3 runs, range 81.3 to 84.4
$0.0013
Identification (high)–
82.3%
±3.1, Mean of 3 runs, range 78.1 to 84.4
$0.0055
OCR (low)
65.8%
$0.0012
51.1%
$0.0035
by category
Single value
64.3%
Transcription
87.5%
Structured JSON
75.8%
Text localization
31.9%
Single value
41.3%
Transcription
77.3%
Structured JSON
73.6%
Text localization
4.1%
OCR (high)
71.2%
$0.0063
55.8%
$0.027
by category
Single value
66.1%
Transcription
88.6%
Structured JSON
82.1%
Text localization
46.4%
Single value
47.0%
Transcription
78.5%
Structured JSON
79.0%
Text localization
6.1%
Reasoning (low)
48.3%
$0.0012
57.0%
±3.3, Mean of 3 runs, range 54.3 to 60.9
$0.0022
Reasoning (high)
68.9%
$0.0042
64.0%
±1.3, Mean of 3 runs, range 62.9 to 65.6
$0.0096

Gemini 3.5 Flash-Lite vs GPT-5.4 Mini: Overview

Gemini 3.5 Flash-Lite

Gemini 3.5 Flash-Lite is a natively multimodal reasoning model developed by Google DeepMind, released on July 21, 2026 as part of the Gemini 3.5 model family. It is the fastest model in the 3.5 series, designed for both low-latency tasks and high-throughput production workloads such as agentic search, document processing, receipt translation, and large-scale data extraction. The model accepts text, images, audio, and video as inputs, with a context window of up to 1 million tokens, and produces text output. It supports configurable thinking levels, allowing developers to tune the balance between response quality, cost, and latency depending on workload requirements.

On agentic and coding benchmarks, Gemini 3.5 Flash-Lite significantly outperforms its predecessor, Gemini 3.1 Flash-Lite, including on Terminal-Bench 2.1 (54% vs. 31%), GDM-MRCR v2 long-context (72.2% vs. 60.1%), and real-world task execution as measured by GDPval-AA v2 (1140 vs. 642). It also surpasses Gemini 3 Flash on SWE-Bench Pro (54.2% vs. 49.6%) and OSWorld-Verified (74.0% vs. 65.1%). According to the Artificial Analysis Index, the model generates output at approximately 350 tokens per second. It is built on the Gemini 3.5 Flash foundation and is evaluated across reasoning, coding, multimodal understanding, multilingual performance, and long-context tasks. The model is developed under Google's Frontier Safety Framework.

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 3.5 Flash-Lite performed better. It scores higher on 3 of the five vision tasks and averages 61.7% (#33 of 63) against 53.2% (#51 of 63) 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 3.5 Flash-Lite leads with 57.5% against 15.8%. This is the widest gap between the two models across the benchmark's tasks.

Gemini 3.5 Flash-Lite is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0014 per sample against $0.0034. Gemini 3.5 Flash-Lite is priced at $0.30 per 1M input tokens and $2.50 per 1M output; GPT-5.4 Mini is priced at $0.75 per 1M input tokens and $4.50 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

Gemini 3.5 Flash-Lite is faster. Across Roboflow's Vision Evals it averaged 3.9s per inference against 5.5s. 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 image captioning and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.