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Gemini 3.5 Flash vs GPT-5.4 Mini

Gemini 3.5 Flash and GPT-5.4 Mini score 81.8% vs 53.2% averaged across five Vision Evals tasks (#6 and #55 of 67 models), so Gemini 3.5 Flash leads overall.

  • Gemini 3.5 Flash wins all 5 tasks, with its widest lead on object detection (70.6% vs 15.8%).
  • GPT-5.4 Mini is about a quarter of the cost: $0.0034 vs $0.012 overall average cost per sample.
  • GPT-5.4 Mini is faster: 5.5s vs 11.5s per sample.

Compare Gemini 3.5 Flash vs GPT-5.4 Mini live

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

Score by taskGemini 3.5 FlashGPT-5.4 Mini
Object Detection
70.6
15.8
OCR
76.7
51.1
Reasoning
82.1
57.0
Counting
80.6
58.6
Identification
99.0
83.3

Roboflow Vision Evals. Full breakdown below.

Gemini 3.5 Flash vs GPT-5.4 Mini Comparison Table

Evals updated November 10, 2026Pricing updated November 10, 2026

PropertyGemini 3.5 FlashGPT-5.4 Mini
OrganizationGoogleOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateMay 2026Mar 2026
Context Window1.0M400K
ParametersUnknownUnknown
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$1.50$0.750
Output $/1M$9.00$4.50
Vision Tasks
Both support 10 more tasks
Model Features
Both support 3 more features
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
81.8%
53.2%
Avg cost / sample$0.012$0.0034
Avg speed / sample11.52s5.51s
By task
Object Detection (low)
70.6%
±2.0, Mean of 3 runs, range 68.7 to 72.6
$0.016
15.8%
±0.4, Mean of 3 runs, range 15.3 to 16.1
$0.0044
Object Detection (high)
69.8%
±1.8, Mean of 3 runs, range 67.5 to 71.1
$0.021
16.6%
±0.8, Mean of 3 runs, range 15.8 to 17.4
$0.030
Counting (low)
80.6%
±0.7, Mean of 3 runs, range 79.7 to 81.1
$0.0075
58.6%
±2.0, Mean of 3 runs, range 56.8 to 60.8
$0.0019
Counting (high)
82.4%
±0.0, Mean of 3 runs, range 82.4 to 82.4
$0.017
64.9%
±2.0, Mean of 3 runs, range 63.5 to 67.6
$0.0073
Identification (low)
99.0%
±1.6, Mean of 3 runs, range 96.9 to 100.0
$0.0040
83.3%
±1.6, Mean of 3 runs, range 81.3 to 84.4
$0.0013
Identification (high)
97.9%
±1.6, Mean of 3 runs, range 96.9 to 100.0
$0.0068
82.3%
±3.1, Mean of 3 runs, range 78.1 to 84.4
$0.0055
OCR (low)
76.7%
$0.013
51.1%
$0.0035
by category
Single value
71.7%
Transcription
90.0%
Structured JSON
86.8%
Text localization
56.2%
Single value
41.3%
Transcription
77.3%
Structured JSON
73.6%
Text localization
4.1%
OCR (high)
75.5%
$0.031
55.8%
$0.027
by category
Single value
69.6%
Transcription
85.4%
Structured JSON
88.1%
Text localization
53.3%
Single value
47.0%
Transcription
78.5%
Structured JSON
79.0%
Text localization
6.1%
Reasoning (low)
82.1%
±2.0, Mean of 3 runs, range 80.1 to 84.1
$0.0082
57.0%
±3.3, Mean of 3 runs, range 54.3 to 60.9
$0.0022
Reasoning (high)
81.0%
±1.7, Mean of 3 runs, range 79.5 to 82.8
$0.018
64.0%
±1.3, Mean of 3 runs, range 62.9 to 65.6
$0.0096

Gemini 3.5 Flash vs GPT-5.4 Mini: Overview

Gemini 3.5 Flash

Gemini 3.5 Flash is a multimodal language model developed by Google DeepMind and released at Google I/O 2026. It is built on the Gemini 3 Flash reasoning foundation and introduces configurable thinking levels (minimal, low, medium, and high) that allow developers to tune the depth of internal reasoning before a response is generated. The model accepts text, image, video, audio, and PDF inputs and produces text output, with a 1 million token context window and up to 65,000 output tokens per request. It is natively multimodal, processing visual inputs alongside text to support tasks such as image captioning, classification, optical character recognition, object detection, and visual grounding, where the model references specific regions within an image or video frame.

Its vision capabilities extend to interpreting UI screenshots, diagrams, charts, and real-world scenes, as well as understanding video and live frame sequences for activity and scene recognition. The model supports combined tool use, including Google Search, URL context, code execution, and custom functions, within a single request, and it uses reasoning context from previous turns when thought signatures are present in the conversation history, enabling persistent multi-turn reasoning chains. Gemini 3.5 Flash carries a knowledge cutoff of January 2026 and is available via the Gemini API, Google AI Studio, Google Antigravity, and the Gemini Enterprise Agent Platform.

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.