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Gemini 4 Argon vs GPT-5.4 Mini

Compare Gemini 4 Argon 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 4 Argon
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OpenAIGPT-5.4 Mini
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Models in this comparison

Gemini 4 Argon vs GPT-5.4 Mini on Vision Evals

Gemini 4 Argon scores higher on all five Vision Evals tasks.

The widest gap is Object Detection, where Gemini 4 Argon leads 82.5% to 15.8%.

Overall, Gemini 4 Argon averages 87.5% (#1 of 63) against 53.2% (#51 of 63) for GPT-5.4 Mini.

GPT-5.4 Mini is both cheaper ($0.0034 vs $0.0058 per sample) and faster (5.5s vs 13.4s per sample).

Gemini 4 ArgonGPT-5.4 Mini

Gemini 4 Argon vs GPT-5.4 Mini Comparison Table

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

PropertyGemini 4 ArgonGPT-5.4 Mini
OrganizationGoogleOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateOct 2026Mar 2026
Context Window1.0M400K
ParametersUndisclosedUnknown
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$2.00$0.750
Output $/1M$10.00$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
Model Features
Foundation VisionSupportedSupported
LLMs with Vision CapabilitiesSupportedSupported
Multimodal VisionSupportedSupported
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
87.5%
53.2%
Avg cost / sample$0.0058$0.0034
Avg speed / sample13.40s5.51s
By task
Object Detection (low)
82.5%
±0.5, Mean of 3 runs, range 81.9 to 83.0
$0.011
15.8%
±0.4, Mean of 3 runs, range 15.3 to 16.1
$0.0044
Object Detection (high)
85.1%
±0.6, Mean of 3 runs, range 84.3 to 85.5
$0.027
16.6%
±0.8, Mean of 3 runs, range 15.8 to 17.4
$0.030
Counting (low)
86.0%
±0.7, Mean of 3 runs, range 85.1 to 86.5
$0.0024
58.6%
±2.0, Mean of 3 runs, range 56.8 to 60.8
$0.0019
Counting (high)
90.5%
±1.4, Mean of 3 runs, range 89.2 to 91.9
$0.010
64.9%
±2.0, Mean of 3 runs, range 63.5 to 67.6
$0.0073
Identification (low)
93.8%
±0.0, Mean of 3 runs, range 93.8 to 93.8
$0.0024
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
$0.0057
82.3%
±3.1, Mean of 3 runs, range 78.1 to 84.4
$0.0055
OCR (low)
85.7%
$0.0051
51.1%
$0.0035
by category
Single value
78.3%
Transcription
95.5%
Structured JSON
92.6%
Text localization
81.5%
Single value
41.3%
Transcription
77.3%
Structured JSON
73.6%
Text localization
4.1%
OCR (high)
88.0%
$0.022
55.8%
$0.027
by category
Single value
80.0%
Transcription
98.8%
Structured JSON
94.8%
Text localization
84.7%
Single value
47.0%
Transcription
78.5%
Structured JSON
79.0%
Text localization
6.1%
Reasoning (low)
89.4%
±1.3, Mean of 3 runs, range 88.1 to 90.7
$0.0024
57.0%
±3.3, Mean of 3 runs, range 54.3 to 60.9
$0.0022
Reasoning (high)
91.4%
±0.7, Mean of 3 runs, range 90.7 to 92.0
$0.013
64.0%
±1.3, Mean of 3 runs, range 62.9 to 65.6
$0.0096

Gemini 4 Argon vs GPT-5.4 Mini: Overview

Gemini 4 Argon

Gemini 4 Argon is Google DeepMind's frontier Gemini 4 model, built to sustain extended reasoning across long-horizon workflows in software engineering, enterprise knowledge work such as legal and finance, scientific analysis, and defensive cybersecurity. It accepts text, image, video, and document input and returns text, operating over a one million token context window. The model raises the maximum output length to one million tokens, compared with a 64K ceiling on earlier Gemini releases, so a single reasoning trajectory can run for hundreds of thousands of tokens before terminating.

On the visual side, Argon targets multimodal document comprehension, professional chart analysis, and retrieval of specific details from long recordings, reporting 91.7% on LVBench for long-video understanding and 71.6% on Chartography for chart reasoning. Google also reports 77.9% on DeepSWE v1.1, 51.3% on AutomationBench, 68% on CWE-bench v1, and first place on the Vals Index, which aggregates finance, coding, legal, and tax tasks. Following Gemini API conventions, it localizes 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 alongside captioning, document parsing, and visual question answering. Google describes adversarial training and automated red teaming for indirect prompt injection resistance, plus chain-of-thought and action monitors that halt execution when behavior drifts outside user intent.

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 4 Argon performed better. It scores higher on all five vision tasks and averages 87.5% (#1 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 4 Argon leads with 82.5% against 15.8%. 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 $0.0034 per sample against $0.0058. Gemini 4 Argon is priced at $2.00 per 1M input tokens and $10.00 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 13.4s. 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.