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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 all five 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 72.4% (#18 of 63) against 59.1% (#36 of 63) for Grok 4.6.

GPT-5.6 Sol is both cheaper ($0.0096 vs $0.011 per sample) and faster (12.2s vs 19.3s per sample).

GPT-5.6 SolGrok 4.6

GPT-5.6 Sol vs Grok 4.6 Comparison Table

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

PropertyGPT-5.6 SolGrok 4.6
OrganizationOpenAISpaceXAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateJul 2026Aug 2026
Context Window1.5M500K
ParametersUnknownUnknown
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$2.00$2.00
Output $/1M$10.00$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
72.4%
59.1%
Avg cost / sample$0.0096$0.011
Avg speed / sample12.16s19.32s
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)
63.6%
$0.0095
60.6%
$0.011
by category
Single value
43.5%
Transcription
91.7%
Structured JSON
82.0%
Text localization
52.2%
Single value
50.0%
Transcription
89.0%
Structured JSON
84.4%
Text localization
11.0%
OCR (high)
65.3%
$0.024
61.2%
$0.030
by category
Single value
46.1%
Transcription
92.3%
Structured JSON
83.9%
Text localization
51.6%
Single value
50.9%
Transcription
88.7%
Structured JSON
85.7%
Text localization
9.4%
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.