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GPT-6.1 Sol vs Grok 4.7

Compare GPT-6.1 Sol and Grok 4.7 side-by-side. See how these vision models stack up in Image Captioning, OCR, Classification, Object Detection, and Open Prompt.

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OpenAIGPT-6.1 Sol
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GrokGrok 4.7
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Models in this comparison

GPT-6.1 Sol vs Grok 4.7 on Vision Evals

GPT-6.1 Sol scores higher on 5 of the six Vision Evals tasks.

The widest gap is Object Detection, where GPT-6.1 Sol leads 80.8% to 40.4%.

Overall, GPT-6.1 Sol averages 85.5% (#4 of 61) against 71.9% (#27 of 61) for Grok 4.7.

GPT-6.1 Sol is both cheaper ($0.0061 vs $0.015 per sample) and faster (14.3s vs 23.6s per sample).

GPT-6.1 SolGrok 4.7

GPT-6.1 Sol vs Grok 4.7 Comparison Table

Evals updated September 29, 2026Pricing updated September 29, 2026

PropertyGPT-6.1 SolGrok 4.7
OrganizationOpenAISpaceXAI
Categoryclosedclosed
Modalitymultimodal—
Release DateSep 2026Sep 2026
Context Window1.1M500K
Parametersundisclosed
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$2.00$2.00
Output $/1M$10.00$6.00
Vision Tasks
CaptioningDemoDemo
Chart Question Answering
ClassificationDemoDemo
Document Question Answering
Image Tagging
Multi-Label Classification
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Promptable Concept SegmentationDemo
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort
Overall
85.5%
71.9%
Avg cost / sample$0.0061$0.015
Avg speed / sample14.31s23.55s
By task
Object Detection (low)
80.8%
±0.1, Mean of 3 runs, range 80.7 to 80.9
$0.010
40.4%
±0.6, Mean of 3 runs, range 39.8 to 41.0
$0.021
Object Detection (high)
81.6%
±0.4, Mean of 3 runs, range 81.1 to 82.0
$0.022
41.2%
±1.6, Mean of 3 runs, range 39.6 to 42.8
$0.028
Counting (low)
78.8%
±3.4, Mean of 3 runs, range 75.7 to 82.4
$0.0037
61.7%
±1.3, Mean of 3 runs, range 60.8 to 63.5
$0.011
Counting (high)
80.2%
±3.4, Mean of 3 runs, range 77.0 to 83.8
$0.0057
60.8%
±1.3, Mean of 3 runs, range 59.5 to 62.2
$0.017
Identification (low)
89.6%
±1.6, Mean of 3 runs, range 87.5 to 90.6
$0.0025
87.5%
±3.1, Mean of 3 runs, range 84.4 to 90.6
$0.0063
Identification (high)
88.5%
±1.6, Mean of 3 runs, range 87.5 to 90.6
$0.0030
80.2%
±1.6, Mean of 3 runs, range 78.1 to 81.3
$0.0093
OCR (low)
92.0%
±0.5, Mean of 3 runs, range 91.5 to 92.5
$0.0064
92.6%
±0.7, Mean of 3 runs, range 92.1 to 93.4
$0.018
OCR (high)
91.7%
±0.3, Mean of 3 runs, range 91.2 to 91.9
$0.017
93.5%
±0.3, Mean of 3 runs, range 93.1 to 93.8
$0.042
Data Extraction (low)
88.0%
±0.5, Mean of 3 runs, range 87.6 to 88.7
$0.0030
84.9%
±2.6, Mean of 3 runs, range 82.5 to 87.6
$0.0060
Data Extraction (high)
90.0%
±1.0, Mean of 3 runs, range 88.7 to 90.7
$0.0037
87.6%
±1.5, Mean of 3 runs, range 86.6 to 89.7
$0.0067
Reasoning (low)
83.7%
±1.3, Mean of 3 runs, range 82.1 to 84.8
$0.0033
64.2%
±2.3, Mean of 3 runs, range 62.3 to 66.9
$0.015
Reasoning (high)
88.7%
±2.0, Mean of 3 runs, range 87.4 to 91.4
$0.0042
66.9%
±1.3, Mean of 3 runs, range 65.6 to 68.2
$0.024

GPT-6.1 Sol vs Grok 4.7: Overview

GPT-6.1 Sol

GPT-6.1 Sol is a reasoning model in OpenAI's GPT-6 series that accepts text and image input and returns text. It is an upgrade to GPT-6 Sol positioned to approach the intelligence of the larger GPT-6 Astra model on agentic coding, computer use, and professional knowledge work. The model exposes an adjustable reasoning effort control, ranging from low settings for simple turns to maximum settings for harder tasks, and can be driven with tool use enabled or disabled. It operates over a context window of roughly one million tokens and emits up to 128,000 output tokens in a single response, which supports long-running agent loops over large codebases and multi-document collections. Audio and video inputs are not supported.

On the visual side, the model is evaluated on GDP.pdf, a benchmark that asks professional questions about complex PDF documents containing tables, charts, diagrams, and fine-print details, and on OSWorld 2.0, which measures agents operating graphical computer applications. OpenAI reports that GPT-6.1 Sol performs on par with or better than GPT-6 Sol across its image input safety evaluations, and that the share of responses containing a factual error at low reasoning effort falls from 11.4 percent to 7.7 percent.

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, GPT-6.1 Sol performed better. It scores higher on 5 of the six vision tasks and averages 85.5% (#4 of 61) against 71.9% (#27 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, GPT-6.1 Sol leads with 80.8% against 40.4%. This is the widest gap between the two models across the benchmark's tasks.

GPT-6.1 Sol is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0061 per sample against $0.015. GPT-6.1 Sol is priced at $2.00 per 1M input tokens and $10.00 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.

GPT-6.1 Sol is faster. Across Roboflow's Vision Evals it averaged 14.3s per inference against 23.6s. 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 OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.