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GPT-5.6 Sol vs Grok 4.6

Compare GPT-5.6 Sol and Grok 4.6 side-by-side. See how these vision models stack up in OCR, Image Captioning, Object Detection, Open Prompt, and Classification.

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OpenAIGPT-5.6 Sol
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GrokGrok 4.6
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Models in this comparison

GPT-5.6 Sol vs Grok 4.6 on Vision Evals

GPT-5.6 Sol scores higher on 4 of the six Vision Evals tasks.

The widest gap is Object Detection, where GPT-5.6 Sol leads 68.4% to 23.8%.

Overall, GPT-5.6 Sol averages 79.0% (#14 of 59) against 68.7% (#31 of 59) for Grok 4.6.

GPT-5.6 Sol is both cheaper ($0.0088 vs $0.0097 per sample) and faster (10.3s vs 17.5s per sample).

GPT-5.6 SolGrok 4.6

GPT-5.6 Sol vs Grok 4.6 Comparison Table

Evals updated September 27, 2026Pricing updated September 27, 2026

PropertyGPT-5.6 SolGrok 4.6
OrganizationOpenAISpaceXAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateJul 2026Aug 2026
Context Window1.5M500K
Parameters
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
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort
Overall
79.0%
68.7%
Avg cost / sample$0.0088$0.0097
Avg speed / sample10.32s17.55s
By task
Object Detection (low)
68.4%
±0.7, Mean of 3 runs, range 67.9 to 69.3
$0.015
23.8%
±2.8, Mean of 3 runs, range 20.2 to 25.9
$0.013
Object Detection (high)
68.4%
±0.8, Mean of 3 runs, range 67.7 to 69.3
$0.035
24.0%
±1.0, Mean of 3 runs, range 23.1 to 25.1
$0.041
Counting (low)
74.3%
±1.4, Mean of 3 runs, range 73.0 to 75.7
$0.0049
65.8%
±4.1, Mean of 3 runs, range 62.2 to 70.3
$0.0079
Counting (high)
76.1%
±2.0, Mean of 3 runs, range 74.3 to 78.4
$0.0078
56.8%
±1.4, Mean of 3 runs, range 55.4 to 58.1
$0.027
Identification (low)
89.6%
±4.7, Mean of 3 runs, range 84.4 to 93.8
$0.0028
84.4%
±3.1, Mean of 3 runs, range 81.3 to 87.5
$0.0055
Identification (high)
92.7%
±1.6, Mean of 3 runs, range 90.6 to 93.8
$0.0030
85.4%
±1.6, Mean of 3 runs, range 84.4 to 87.5
$0.015
OCR (low)
90.7%
±0.1, Mean of 3 runs, range 90.6 to 90.7
$0.011
91.8%
±0.3, Mean of 3 runs, range 91.5 to 92.1
$0.0091
OCR (high)
90.2%
±0.2, Mean of 3 runs, range 90.0 to 90.4
$0.025
91.6%
±0.2, Mean of 3 runs, range 91.4 to 91.7
$0.023
Data Extraction (low)
84.9%
±1.0, Mean of 3 runs, range 83.5 to 85.6
$0.0033
85.6%
±1.0, Mean of 3 runs, range 84.5 to 86.6
$0.0050
Data Extraction (high)
86.9%
±0.5, Mean of 3 runs, range 86.6 to 87.6
$0.0041
85.6%
±1.0, Mean of 3 runs, range 84.5 to 86.6
$0.0090
Reasoning (low)
66.0%
±2.6, Mean of 3 runs, range 63.6 to 68.9
$0.0043
61.1%
±1.3, Mean of 3 runs, range 59.6 to 62.3
$0.0093
Reasoning (high)
71.7%
±1.3, Mean of 3 runs, range 70.2 to 72.8
$0.0061
63.8%
±2.0, Mean of 3 runs, range 62.3 to 66.2
$0.032

GPT-5.6 Sol vs Grok 4.6: Overview

GPT-5.6 Sol

GPT-5.6 Sol is the flagship model in OpenAI's GPT-5.6 family, which also includes Terra (a balanced everyday-work tier) and Luna (a fast, cost-efficient tier). Sol is designed for demanding reasoning, long-horizon agentic workflows, software engineering, computer use, scientific research, and cybersecurity tasks. It introduces two new capability modes: a "max" reasoning effort setting that allocates additional compute time for difficult problems, and an "ultra" mode that coordinates multiple subagents in parallel to accelerate complex, multi-step work. The model supports native multimodal input, allowing it to process screenshots, diagrams, charts, documents, and photographs alongside text. A reported context window of approximately 1.5 million tokens enables processing of large codebases, lengthy research documents, and extended agentic sessions.

GPT-5.6 Sol was announced on June 26, 2026, initially in a limited preview for trusted partners, and reached general availability on July 9, 2026. On the Agents' Last Exam benchmark, which evaluates long-running professional workflows across 55 fields, Sol scores 53.6. On Terminal-Bench 2.1, which tests command-line agentic coding workflows, Sol Ultra achieves 91.9%. The model also demonstrates gains in life sciences evaluations, including long-horizon genomics and quantitative biology analyses. OpenAI paired the release with its most extensive safety evaluation to date, combining human red teaming with large-scale automated testing, and classified Sol as High capability in both cybersecurity and biological risk under its Preparedness Framework, though it does not cross the Critical threshold in either category.

Grok 4.6

Grok 4.6 is a proprietary reasoning model from xAI aimed at long-running agentic workflows, coding, and knowledge work. It accepts text and image input and returns text, with a 500,000 token context window and a knowledge cutoff of February 1, 2026. The model exposes an adjustable reasoning budget with low, medium, high, and xhigh settings, where high is the default, and it supports function calling, structured outputs, web and X search, and code execution as documented tool behaviors. Its visual capability covers interpreting images supplied alongside text prompts, which places it in the visual question answering and document understanding family, and it can also return object detection boxes as text coordinates when prompted.

xAI characterizes Grok 4.6 as the result of an extended post-training run over the Grok 4.5 lineage rather than a new pretrained base. The described recipe combines curated model-generated reasoning and technical data, engineering data, a revised optimizer, regenerated supervised fine-tuning trajectories, and reinforcement learning across agent environments spanning knowledge work, coding, kernel optimization, web development, and computer-aided design. Parameter count and architecture specifics are not disclosed. Independent measurement from Artificial Analysis places the model at 61 on its Intelligence Index, five points above Grok 4.5.

Frequently Asked Questions

On Roboflow's Vision Evals, GPT-5.6 Sol performed better. It scores higher on 4 of the six vision tasks and averages 79.0% (#14 of 59) against 68.7% (#31 of 59) for Grok 4.6. 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-5.6 Sol leads with 68.4% against 23.8%. This is the widest gap between the two models across the benchmark's tasks.

GPT-5.6 Sol is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0088 per sample against $0.0097. GPT-5.6 Sol is priced at $2.00 per 1M input tokens and $10.00 per 1M output; Grok 4.6 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-5.6 Sol is faster. Across Roboflow's Vision Evals it averaged 10.3s per inference against 17.5s. 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 OCR and image captioning in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.