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Gemini 3.1 Pro vs Grok 4.5

Gemini 3.1 Pro and Grok 4.5 score 76.8% vs 55.3% averaged across five Vision Evals tasks (#16 and #51 of 68 models), so Gemini 3.1 Pro leads overall.

  • Gemini 3.1 Pro wins all 5 tasks, with its widest lead on object detection (67.4% vs 19.0%).
  • Grok 4.5 is cheaper: $0.0078 vs $0.010 overall average cost per sample.
  • Gemini 3.1 Pro is faster: 8.4s vs 16.4s per sample.

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GoogleGemini 3.1 Pro
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GrokGrok 4.5
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Gemini 3.1 Pro vs Grok 4.5 on Vision Evals

Score by taskGemini 3.1 ProGrok 4.5
Object Detection
67.4
19.0
Identification
100.0
83.3
OCR
72.6
57.1
Reasoning
72.2
57.6
Counting
71.6
59.5

Roboflow Vision Evals. Full breakdown below.

Gemini 3.1 Pro vs Grok 4.5 Comparison Table

Evals updated November 10, 2026Pricing updated November 11, 2026

PropertyGemini 3.1 ProGrok 4.5
OrganizationGoogleSpaceXAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateFeb 2026Jul 2026
Context Window1.0M500K
ParametersUnknownUnknown
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$2.00$2.00
Output $/1M$12.00$6.00
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
76.8%
55.3%
Avg cost / sample$0.010$0.0078
Avg speed / sample8.44s16.41s
By task
Object Detection (low)
67.4%
$0.010
19.0%
±0.8, Mean of 3 runs, range 18.0 to 19.6
$0.011
Object Detection (high)–
17.8%
±0.3, Mean of 3 runs, range 17.5 to 18.0
$0.020
Counting (low)
71.6%
$0.0071
59.5%
±3.4, Mean of 3 runs, range 55.4 to 62.2
$0.0065
Counting (high)–
57.7%
±3.4, Mean of 3 runs, range 54.0 to 60.8
$0.012
Identification (low)
100.0%
$0.0070
83.3%
±1.6, Mean of 3 runs, range 81.3 to 84.4
$0.0046
Identification (high)–
85.4%
±3.1, Mean of 3 runs, range 81.3 to 87.5
$0.0067
OCR (low)
72.6%
$0.010
57.1%
$0.0065
by category
Single value
67.8%
Transcription
88.5%
Structured JSON
82.0%
Text localization
52.0%
Single value
47.8%
Transcription
85.4%
Structured JSON
80.3%
Text localization
6.1%
OCR (high)
73.1%
$0.026
57.5%
$0.013
by category
Single value
68.7%
Transcription
90.8%
Structured JSON
83.9%
Text localization
47.1%
Single value
47.8%
Transcription
86.1%
Structured JSON
80.9%
Text localization
7.0%
Reasoning (low)
72.2%
$0.012
57.6%
±1.7, Mean of 3 runs, range 55.6 to 58.9
$0.0082
Reasoning (high)
74.8%
$0.021
59.8%
±2.6, Mean of 3 runs, range 57.0 to 62.3
$0.019

Gemini 3.1 Pro vs Grok 4.5: 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 4.5

Grok 4.5 is a proprietary reasoning model from SpaceXAI (xAI) that accepts interleaved text and image input and returns text, with a 500,000 token context window. xAI positions it as a model for coding, agentic software work, and knowledge tasks, and states it was trained in the company's Memphis data centers on datasets spanning science, engineering, and mathematics. Its reinforcement learning stage covers hundreds of thousands of multi step software engineering tasks scored by automated checks and model based grading, and training is reported to have run on tens of thousands of NVIDIA GB300 GPUs using an asynchronous scheme in which multi hour agentic rollouts continue while learning proceeds in parallel, targeting long horizon autonomous operation rather than single turn inference.

For vision, the model consumes JPEG and PNG images in any order relative to text prompts, covering visual question answering, description of chart and document imagery, and reading text rendered inside a scene. Reasoning effort is configurable, and the model supports function calling and structured outputs, so image inputs can be interleaved with tool calls inside agent loops. xAI has not published a technical report, architecture details, or parameter count, and reported mixture of experts sizing figures come from secondary coverage rather than official documentation.