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Gemini 3.1 Pro vs Grok 4.7

Compare Gemini 3.1 Pro and Grok 4.7 side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, Classification, OCR, and Object Detection.

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Run the same image across every model that supports a task and compare their outputs side-by-side.

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GoogleGemini 3.1 Pro
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GrokGrok 4.7
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Models in this comparison

Gemini 3.1 Pro vs Grok 4.7 on Vision Evals

Gemini 3.1 Pro scores higher on all five Vision Evals tasks.

The widest gap is Object Detection, where Gemini 3.1 Pro leads 67.4% to 40.4%.

Overall, Gemini 3.1 Pro averages 76.8% (#11 of 61) against 63.5% (#29 of 61) for Grok 4.7.

Gemini 3.1 Pro is both cheaper ($0.010 vs $0.020 per sample) and faster (8.4s vs 33.3s per sample).

Gemini 3.1 ProGrok 4.7

Gemini 3.1 Pro vs Grok 4.7 Comparison Table

Evals updated October 8, 2026Pricing updated October 10, 2026

PropertyGemini 3.1 ProGrok 4.7
OrganizationGoogleSpaceXAI
Categoryclosedclosed
Modalitymultimodal—
Release DateFeb 2026Sep 2026
Context Window1.0M500K
ParametersUnknownUnknown
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$2.00$2.00
Output $/1M$12.00$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
Model Features
Foundation VisionSupportedNot listed
LLMs with Vision CapabilitiesSupportedNot listed
Multimodal VisionSupportedNot listed
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
76.8%
63.5%
Avg cost / sample$0.010$0.020
Avg speed / sample8.44s33.31s
By task
Object Detection (low)
67.4%
$0.010
40.4%
±0.6, Mean of 3 runs, range 39.8 to 41.0
$0.021
Object Detection (high)–
41.2%
±1.6, Mean of 3 runs, range 39.6 to 42.8
$0.028
Counting (low)
71.6%
$0.0071
61.7%
±1.3, Mean of 3 runs, range 60.8 to 63.5
$0.011
Counting (high)–
60.8%
±1.3, Mean of 3 runs, range 59.5 to 62.2
$0.017
Identification (low)
100.0%
$0.0070
87.5%
±3.1, Mean of 3 runs, range 84.4 to 90.6
$0.0063
Identification (high)–
80.2%
±1.6, Mean of 3 runs, range 78.1 to 81.3
$0.0093
OCR (low)
72.6%
$0.010
63.6%
$0.023
by category
Single value
67.8%
Transcription
88.5%
Structured JSON
82.0%
Text localization
52.0%
Single value
54.4%
Transcription
89.5%
Structured JSON
86.4%
Text localization
14.8%
OCR (high)
73.1%
$0.026
–
by category
Single value
68.7%
Transcription
90.8%
Structured JSON
83.9%
Text localization
47.1%
Reasoning (low)
72.2%
$0.012
64.2%
±2.3, Mean of 3 runs, range 62.3 to 66.9
$0.015
Reasoning (high)
74.8%
$0.021
66.9%
±1.3, Mean of 3 runs, range 65.6 to 68.2
$0.024

Gemini 3.1 Pro vs Grok 4.7: Overview

Gemini 3.1 Pro

Gemini 3.1 Pro is a proprietary multimodal model from Google’s Gemini 3 series, released in early 2026 and designed for advanced reasoning across large multimodal datasets. It accepts text, images, audio, video, and documents, supporting up to a 1-million-token input context with up to 64k output tokens. Compared with Gemini 3 Pro, it improves long-context synthesis and multi-step reasoning, enabling more reliable analysis of large documents, datasets, and software codebases.

The model also advances visual understanding and grounding, allowing it to interpret UI screenshots, diagrams, and real-world scenes while referencing specific regions within images or video. These capabilities make Gemini 3.1 Pro well suited for multimodal workflows involving document processing, interface analysis, robotics research, and complex visual reasoning.

Grok 4.7

Grok 4.7 is a proprietary multimodal reasoning model from SpaceXAI that accepts images alongside text and returns text-only output. On visual inputs it supports image captioning, visual question answering, OCR, document and chart question answering, and image classification and tagging, with all results expressed as generated text rather than bounding boxes or masks. Its 500,000 token context window leaves room for several images, long documents, or extended conversations about visual content in a single request.

The model exposes a configurable reasoning effort setting with low, medium, high, and xhigh levels (high by default), letting callers trade latency for the amount of deliberation spent on a prompt, including multi-step questions about an image. Built on a larger base model than Grok 4.6 with extended reinforcement learning on harder tasks, it works longer on difficult problems and checks its own work more carefully at the same serving speed. SpaceXAI's launch materials focus on coding and agentic knowledge work and report no image benchmark results.

Frequently Asked Questions

On Roboflow's Vision Evals, Gemini 3.1 Pro performed better. It scores higher on all five vision tasks and averages 76.8% (#11 of 61) against 63.5% (#29 of 61) for Grok 4.7. 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 3.1 Pro leads with 67.4% against 40.4%. This is the widest gap between the two models across the benchmark's tasks.

Gemini 3.1 Pro is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.010 per sample against $0.020. Gemini 3.1 Pro is priced at $2.00 per 1M input tokens and $12.00 per 1M output; Grok 4.7 is priced at $2.00 per 1M input tokens and $6.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

Gemini 3.1 Pro is faster. Across Roboflow's Vision Evals it averaged 8.4s per inference against 33.3s. 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.