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Gemini 3 Flash vs Grok 4.5

Compare Gemini 3 Flash and Grok 4.5 side-by-side. See how these vision models stack up in Object Detection, Classification, Open Prompt, OCR, and Image Captioning.

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GoogleGemini 3 Flash
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GrokGrok 4.5
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Models in this comparison

Gemini 3 Flash vs Grok 4.5 on Vision Evals

Gemini 3 Flash scores higher on all five Vision Evals tasks.

The widest gap is Object Detection, where Gemini 3 Flash leads 38.6% to 19.0%.

Overall, Gemini 3 Flash averages 65.7% (#27 of 63) against 55.3% (#46 of 63) for Grok 4.5.

Gemini 3 Flash is both cheaper ($0.0024 vs $0.0078 per sample) and faster (5.8s vs 16.4s per sample).

Gemini 3 FlashGrok 4.5

Gemini 3 Flash vs Grok 4.5 Comparison Table

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

PropertyGemini 3 FlashGrok 4.5
OrganizationGoogleSpaceXAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateDec 2025Jul 2026
Context Window1.0M500K
ParametersUnknownUnknown
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$0.500$2.00
Output $/1M$3.00$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 VisionSupportedSupported
LLMs with Vision CapabilitiesSupportedSupported
Multimodal VisionSupportedSupported
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
65.7%
55.3%
Avg cost / sample$0.0024$0.0078
Avg speed / sample5.83s16.41s
By task
Object Detection (low)
38.6%
$0.0031
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)
67.6%
$0.0012
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)
93.8%
$0.0009
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)
63.9%
$0.0024
57.1%
$0.0065
by category
Single value
63.9%
Transcription
90.5%
Structured JSON
80.2%
Text localization
7.6%
Single value
47.8%
Transcription
85.4%
Structured JSON
80.3%
Text localization
6.1%
OCR (high)
73.1%
$0.013
57.5%
$0.013
by category
Single value
69.1%
Transcription
90.0%
Structured JSON
87.5%
Text localization
36.9%
Single value
47.8%
Transcription
86.1%
Structured JSON
80.9%
Text localization
7.0%
Reasoning (low)
64.9%
$0.0020
57.6%
±1.7, Mean of 3 runs, range 55.6 to 58.9
$0.0082
Reasoning (high)
74.2%
$0.0040
59.8%
±2.6, Mean of 3 runs, range 57.0 to 62.3
$0.019

Gemini 3 Flash vs Grok 4.5: Overview

Gemini 3 Flash

Gemini 3 Flash is a proprietary multimodal large language model developed by Google through Google DeepMind, designed to deliver fast, cost-efficient reasoning across real-time products and developer workflows. Released in December 2025, it is the Flash-tier variant of the Gemini 3 family, balancing low latency with reasoning quality approaching Pro models.

The model supports text, images, audio, and video, with an exceptionally large context window of roughly one million input tokens and outputs up to ~65k tokens. It emphasizes rapid responses for coding, summarization, analysis, and agentic tasks, and exposes configurable “thinking levels” via API to trade speed for deeper reasoning. Today, Gemini 3 Flash positions itself as a high-throughput, production-ready model, serving as the default in the Gemini app and Google Search’s AI Mode, optimized for scalable, interactive AI applications.

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.