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Gemini 2.5 Flash vs Grok 4.5

Compare Gemini 2.5 Flash and Grok 4.5 side-by-side. See how these vision models stack up in Open Prompt, OCR, Classification, Image Captioning, and Object Detection.

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GoogleGemini 2.5 Flash
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Gemini 2.5 Flash vs Grok 4.5 Comparison Table

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

PropertyGemini 2.5 FlashGrok 4.5
OrganizationGoogleSpaceXAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateJul 2025Jul 2026
Context Window1.0M500K
ParametersUnknownUnknown
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$0.300$2.00
Output $/1M$2.50$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
OverallNot evaluated
55.3%
Avg cost / sample–$0.0078
Avg speed / sample–16.41s
By task
Object Detection (low)–
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)–
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)–
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)–
57.1%
$0.0065
by category
Single value
47.8%
Transcription
85.4%
Structured JSON
80.3%
Text localization
6.1%
OCR (high)–
57.5%
$0.013
by category
Single value
47.8%
Transcription
86.1%
Structured JSON
80.9%
Text localization
7.0%
Reasoning (low)–
57.6%
±1.7, Mean of 3 runs, range 55.6 to 58.9
$0.0082
Reasoning (high)–
59.8%
±2.6, Mean of 3 runs, range 57.0 to 62.3
$0.019

Gemini 2.5 Flash vs Grok 4.5: Overview

Gemini 2.5 Flash

Gemini 2.5 Flash, released on June 17, 2025, is Google DeepMind’s production-ready, efficiency-focused model in the Gemini 2.5 family. It is multimodal, accepting text, images, video, and audio as inputs, with text as the primary output format. The model supports 1 million input tokens and up to 65K output tokens, enabling it to process very large contexts such as books, long video transcripts, or extensive datasets. Its training knowledge extends to January 2025.

Designed as a price-performance leader, Gemini 2.5 Flash balances speed and reasoning power, making it suitable for everyday enterprise and developer use cases without the higher latency and cost of Pro models. It supports advanced workflows like function calling, code execution, search grounding, URL context ingestion, and structured outputs. While efficient and scalable, output length is still limited compared to its input capacity, and multimodal outputs (e.g. image or audio generation) remain restricted to specialized or preview variants.

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.