Roboflow

Grok 4.5 vs Grok 4.7

Compare Grok 4.5 and Grok 4.7 side-by-side.

Compare Grok 4.5 vs Grok 4.7 live

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

These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.

Models in this comparison

Grok 4.5 vs Grok 4.7 on Vision Evals

Grok 4.7 scores higher on all six Vision Evals tasks.

The widest gap is Object Detection, where Grok 4.7 leads 40.4% to 19.0%.

Overall, Grok 4.5 averages 65.8% (#37 of 54) against 71.9% (#22 of 54) for Grok 4.7.

Grok 4.5 is both cheaper ($0.0084 vs $0.012 per sample) and faster (20.8s vs 23.6s per sample).

Grok 4.5Grok 4.7

Grok 4.5 vs Grok 4.7 Comparison Table

Evals updated September 22, 2026Pricing updated September 22, 2026

PropertyGrok 4.5Grok 4.7
OrganizationSpaceXAISpaceXAI
Categoryclosedclosed
Modalitymultimodal
Release DateJul 2026Sep 2026
Context Window500K500K
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$2.00$1.60
Output $/1M$6.00$4.80
Vision Tasks
CaptioningDemo
Chart Question Answering
ClassificationDemo
Document Question Answering
Image Tagging
Multi-Label Classification
Object DetectionDemo
OCRDemo
Vision Language
Visual Question AnsweringDemo
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort
Overall
65.8%
71.9%
Avg cost / sample$0.0084$0.012
Avg speed / sample20.75s23.55s
By task
Object Detection (low)
19.0%
±0.8, Mean of 3 runs, range 18.0 to 19.6
$0.011
40.4%
±0.6, Mean of 3 runs, range 39.8 to 41.0
$0.017
Object Detection (high)
17.8%
±0.3, Mean of 3 runs, range 17.5 to 18.0
$0.020
41.2%
±1.6, Mean of 3 runs, range 39.6 to 42.8
$0.023
Counting (low)
59.5%
±3.4, Mean of 3 runs, range 55.4 to 62.2
$0.0065
61.7%
±1.3, Mean of 3 runs, range 60.8 to 63.5
$0.0086
Counting (high)
57.7%
±3.4, Mean of 3 runs, range 54.0 to 60.8
$0.012
60.8%
±1.3, Mean of 3 runs, range 59.5 to 62.2
$0.013
Identification (low)
83.3%
±1.6, Mean of 3 runs, range 81.3 to 84.4
$0.0046
87.5%
±3.1, Mean of 3 runs, range 84.4 to 90.6
$0.0050
Identification (high)
85.4%
±3.1, Mean of 3 runs, range 81.3 to 87.5
$0.0067
80.2%
±1.6, Mean of 3 runs, range 78.1 to 81.3
$0.0074
OCR (low)
92.1%
±0.3, Mean of 3 runs, range 91.9 to 92.5
$0.0068
92.6%
±0.7, Mean of 3 runs, range 92.1 to 93.4
$0.014
OCR (high)
92.3%
±0.5, Mean of 3 runs, range 91.9 to 92.9
$0.012
93.5%
±0.3, Mean of 3 runs, range 93.1 to 93.8
$0.034
Data Extraction (low)
83.5%
±1.6, Mean of 3 runs, range 81.4 to 84.5
$0.0044
84.9%
±2.6, Mean of 3 runs, range 82.5 to 87.6
$0.0048
Data Extraction (high)
81.8%
±1.6, Mean of 3 runs, range 80.4 to 83.5
$0.0061
87.6%
±1.5, Mean of 3 runs, range 86.6 to 89.7
$0.0054
Reasoning (low)
57.6%
±1.7, Mean of 3 runs, range 55.6 to 58.9
$0.0082
64.2%
±2.3, Mean of 3 runs, range 62.3 to 66.9
$0.012
Reasoning (high)
59.8%
±2.6, Mean of 3 runs, range 57.0 to 62.3
$0.019
66.9%
±1.3, Mean of 3 runs, range 65.6 to 68.2
$0.019

Grok 4.5 vs Grok 4.7: Overview

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.

Grok 4.7

Grok 4.7 is a proprietary model from SpaceXAI, released on September 21, 2026. It accepts text and images as input and returns text. It extends Grok 4.6 and is listed at the same API price.

Its Vision Evals scores are on the leaderboard. Running it in the Playground is not available yet, because the inference workflow is not ready.

Frequently Asked Questions

On Roboflow's Vision Evals, Grok 4.7 performed better. It scores higher on all six vision tasks and averages 71.9% (#22 of 54) against 65.8% (#37 of 54) for Grok 4.5. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

No. On the Vision Evals Object Detection benchmark at low effort, Grok 4.7 leads with 40.4% against 19.0%. This is the widest gap between the two models across the benchmark's tasks.

Grok 4.5 is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0084 per sample against $0.012. Grok 4.5 is priced at $2.00 per 1M input tokens and $6.00 per 1M output; Grok 4.7 is priced at $1.60 per 1M input tokens and $4.80 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

Grok 4.5 is faster. Across Roboflow's Vision Evals it averaged 20.8s per inference against 23.6s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.