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Gemini 4 Argon vs Grok 4.6

Compare Gemini 4 Argon and Grok 4.6 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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GrokGrok 4.6
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Models in this comparison

Gemini 4 Argon vs Grok 4.6 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 23.8%.

Overall, Gemini 4 Argon averages 87.5% (#1 of 63) against 59.1% (#36 of 63) for Grok 4.6.

Gemini 4 Argon is both cheaper ($0.0058 vs $0.011 per sample) and faster (13.4s vs 19.3s per sample).

Gemini 4 ArgonGrok 4.6

Gemini 4 Argon vs Grok 4.6 Comparison Table

Evals updated October 13, 2026Pricing updated October 22, 2026

PropertyGemini 4 ArgonGrok 4.6
OrganizationGoogleSpaceXAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateOct 2026Aug 2026
Context Window1.0M500K
ParametersUndisclosedUnknown
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$2.00$2.00
Output $/1M$10.00$6.00
Vision Tasks
CaptioningDemoDemo
Chart Question AnsweringSupportedSupported
ClassificationDemoDemo
Document Question AnsweringSupportedSupported
Image TaggingSupportedSupported
Multi-Label ClassificationSupportedSupported
Object DetectionDemoDemo
OCRDemoDemo
Vision LanguageSupportedSupported
Visual Question AnsweringDemoDemo
Promptable Concept SegmentationDemoNot listed
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%
59.1%
Avg cost / sample$0.0058$0.011
Avg speed / sample13.40s19.32s
By task
Object Detection (low)
82.5%
±0.5, Mean of 3 runs, range 81.9 to 83.0
$0.011
23.8%
±2.8, Mean of 3 runs, range 20.2 to 25.9
$0.013
Object Detection (high)
85.1%
±0.6, Mean of 3 runs, range 84.3 to 85.5
$0.027
24.0%
±1.0, Mean of 3 runs, range 23.1 to 25.1
$0.041
Counting (low)
86.0%
±0.7, Mean of 3 runs, range 85.1 to 86.5
$0.0024
65.8%
±4.1, Mean of 3 runs, range 62.2 to 70.3
$0.0079
Counting (high)
90.5%
±1.4, Mean of 3 runs, range 89.2 to 91.9
$0.010
56.8%
±1.4, Mean of 3 runs, range 55.4 to 58.1
$0.027
Identification (low)
93.8%
±0.0, Mean of 3 runs, range 93.8 to 93.8
$0.0024
84.4%
±3.1, Mean of 3 runs, range 81.3 to 87.5
$0.0055
Identification (high)
96.9%
±0.0, Mean of 3 runs, range 96.9 to 96.9
$0.0057
85.4%
±1.6, Mean of 3 runs, range 84.4 to 87.5
$0.015
OCR (low)
85.7%
$0.0051
60.6%
$0.011
by category
Single value
78.3%
Transcription
95.5%
Structured JSON
92.6%
Text localization
81.5%
Single value
50.0%
Transcription
89.0%
Structured JSON
84.4%
Text localization
11.0%
OCR (high)
88.0%
$0.022
61.2%
$0.030
by category
Single value
80.0%
Transcription
98.8%
Structured JSON
94.8%
Text localization
84.7%
Single value
50.9%
Transcription
88.7%
Structured JSON
85.7%
Text localization
9.4%
Reasoning (low)
89.4%
±1.3, Mean of 3 runs, range 88.1 to 90.7
$0.0024
61.1%
±1.3, Mean of 3 runs, range 59.6 to 62.3
$0.0093
Reasoning (high)
91.4%
±0.7, Mean of 3 runs, range 90.7 to 92.0
$0.013
63.8%
±2.0, Mean of 3 runs, range 62.3 to 66.2
$0.032

Gemini 4 Argon vs Grok 4.6: 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.

Grok 4.6

Grok 4.6 is a proprietary reasoning model from xAI aimed at long-running agentic workflows, coding, and knowledge work. It accepts text and image input and returns text, with a 500,000 token context window and a knowledge cutoff of February 1, 2026. The model exposes an adjustable reasoning budget with low, medium, high, and xhigh settings, where high is the default, and it supports function calling, structured outputs, web and X search, and code execution as documented tool behaviors. Its visual capability covers interpreting images supplied alongside text prompts, which places it in the visual question answering and document understanding family, and it can also return object detection boxes as text coordinates when prompted.

xAI characterizes Grok 4.6 as the result of an extended post-training run over the Grok 4.5 lineage rather than a new pretrained base. The described recipe combines curated model-generated reasoning and technical data, engineering data, a revised optimizer, regenerated supervised fine-tuning trajectories, and reinforcement learning across agent environments spanning knowledge work, coding, kernel optimization, web development, and computer-aided design. Parameter count and architecture specifics are not disclosed. Independent measurement from Artificial Analysis places the model at 61 on its Intelligence Index, five points above Grok 4.5.