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Gemini 3.7 Flash vs GPT-5.4 Mini

Gemini 3.7 Flash and GPT-5.4 Mini score 80.1% vs 53.2% averaged across five Vision Evals tasks (#10 and #57 of 69 models), so Gemini 3.7 Flash leads overall.

  • Gemini 3.7 Flash wins all 5 tasks, with its widest lead on object detection (70.5% vs 15.8%).
  • GPT-5.4 Mini is faster: 5.5s vs 11.0s per sample.

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GoogleGemini 3.7 Flash
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OpenAIGPT-5.4 Mini
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Gemini 3.7 Flash vs GPT-5.4 Mini on Vision Evals

Score by taskGemini 3.7 FlashGPT-5.4 Mini
Object Detection
70.5
15.8
Reasoning
80.8
57.0
OCR
73.9
51.1
Counting
78.4
58.6
Identification
96.9
83.3

Roboflow Vision Evals. Full breakdown below.

Gemini 3.7 Flash vs GPT-5.4 Mini Comparison Table

Evals updated November 12, 2026Pricing updated November 13, 2026

PropertyGemini 3.7 FlashGPT-5.4 Mini
OrganizationGoogleOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateAug 2026Mar 2026
Context Window1.0M400K
ParametersUndisclosedUnknown
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$0.750$0.750
Output $/1M$3.75$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
80.1%
53.2%
Avg cost / sample$0.0033$0.0034
Avg speed / sample11.04s5.51s
By task
Object Detection (low)
70.5%
±1.1, Mean of 3 runs, range 69.4 to 71.5
$0.0047
15.8%
±0.4, Mean of 3 runs, range 15.3 to 16.1
$0.0044
Object Detection (high)
74.3%
±0.8, Mean of 3 runs, range 73.3 to 75.0
$0.0089
16.6%
±0.8, Mean of 3 runs, range 15.8 to 17.4
$0.030
Counting (low)
78.4%
±1.4, Mean of 3 runs, range 77.0 to 79.7
$0.0025
58.6%
±2.0, Mean of 3 runs, range 56.8 to 60.8
$0.0019
Counting (high)
79.3%
±2.0, Mean of 3 runs, range 77.0 to 81.1
$0.0056
64.9%
±2.0, Mean of 3 runs, range 63.5 to 67.6
$0.0073
Identification (low)
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.0013
83.3%
±1.6, Mean of 3 runs, range 81.3 to 84.4
$0.0013
Identification (high)
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.0021
82.3%
±3.1, Mean of 3 runs, range 78.1 to 84.4
$0.0055
OCR (low)
73.9%
$0.0032
51.1%
$0.0035
by category
Single value
74.8%
Transcription
91.1%
Structured JSON
86.7%
Text localization
29.4%
Single value
41.3%
Transcription
77.3%
Structured JSON
73.6%
Text localization
4.1%
OCR (high)
78.8%
$0.010
55.8%
$0.027
by category
Single value
71.7%
Transcription
91.9%
Structured JSON
90.6%
Text localization
59.2%
Single value
47.0%
Transcription
78.5%
Structured JSON
79.0%
Text localization
6.1%
Reasoning (low)
80.8%
±2.0, Mean of 3 runs, range 78.8 to 82.8
$0.0022
57.0%
±3.3, Mean of 3 runs, range 54.3 to 60.9
$0.0022
Reasoning (high)
81.9%
±1.3, Mean of 3 runs, range 80.1 to 82.8
$0.0050
64.0%
±1.3, Mean of 3 runs, range 62.9 to 65.6
$0.0096

Gemini 3.7 Flash vs GPT-5.4 Mini: Overview

Gemini 3.7 Flash

Gemini 3.7 Flash is a proprietary multimodal model from Google, positioned in the Flash branch of the Gemini 3 series that trades some of the capacity of the larger Pro models for lower latency and lower cost per token. It accepts interleaved text and image input alongside other modalities handled by the Gemini family and returns text, and it continues the series pattern of exposing a configurable thinking budget so that reasoning effort can be scaled up for harder problems or reduced for high throughput extraction, routing and classification work. The model is announced roughly three weeks after Gemini 3.6 Flash, part of an unusually fast iteration cadence within the Flash line.

Google reports gains concentrated in agentic coding and front end generation, citing a WebDev Arena Elo of 1588 for this release compared with 1538 for the preceding Flash model, and describes it as producing more functional layouts and more feature complete applications in fewer prompts. Weights are not published and the architecture, parameter count and training corpus are undisclosed, consistent with prior Gemini releases. Visual capability follows the Flash lineage, covering image and document understanding, chart and diagram interpretation, text recognition in images, and general visual question answering.

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 3.7 Flash performed better. It scores higher on all five vision tasks and averages 80.1% (#10 of 69) against 53.2% (#57 of 69) 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 3.7 Flash leads with 70.5% against 15.8%. This is the widest gap between the two models across the benchmark's tasks.

Gemini 3.7 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0033 per sample against $0.0034. Gemini 3.7 Flash is priced at $0.75 per 1M input tokens and $3.75 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.

GPT-5.4 Mini is faster. Across Roboflow's Vision Evals it averaged 5.5s per inference against 11.0s. 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.