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GPT-5.5 vs Grok 4.6

Compare GPT-5.5 and Grok 4.6 side-by-side. See how these vision models stack up in Object Detection, Image Captioning, Classification, Open Prompt, and OCR.

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OpenAIGPT-5.5
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GrokGrok 4.6
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Models in this comparison

OpenAI

GPT-5.5 vs Grok 4.6 on Vision Evals

GPT-5.5 scores higher on 4 of the six Vision Evals tasks.

The widest gap is Object Detection, where GPT-5.5 leads 41.7% to 20.2%.

Overall, GPT-5.5 averages 73.9% (#12 of 31) against 67.8% (#17 of 31) for Grok 4.6.

Grok 4.6 is both cheaper ($0.0069 vs $0.022 per sample) and faster (7.4s vs 9.3s per sample).

GPT-5.5Grok 4.6

GPT-5.5 vs Grok 4.6 Comparison Table

Evals updated August 20, 2026Pricing updated August 23, 2026

PropertyGPT-5.5Grok 4.6
OrganizationOpenAISpaceXAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateApr 2026Aug 2026
Context Window1.0M500K
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$5.00$2.00
Output $/1M$30.00$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
73.9%
67.8%
Avg cost / sample$0.022$0.0069
Avg speed / sample9.35s7.39s
By task
Object Detection
41.7%
$0.034
20.2%
$0.0068
Counting
64.9%
$0.015
70.3%
$0.0074
Identification
90.6%
$0.0085
78.1%
$0.0048
OCR
91.2%
$0.023
92.0%
$0.0086
Data Extraction
87.6%
$0.010
84.5%
$0.0042
Reasoning (low)
67.5%
$0.014
61.6%
$0.0087
Reasoning (high)
69.5%
$0.030
61.6%
$0.027

GPT-5.5 vs Grok 4.6: Overview

GPT-5.5

GPT-5.5 is a multimodal large language model released by OpenAI on April 23, 2026, engineered for autonomous, multi-step knowledge work and agentic workflows. It accepts text, images, and code as input, featuring enhanced spatial reasoning and visual grounding to support its computer use capabilities for operating software and navigating UI elements. Built to execute complex workflows end-to-end, the model interprets loosely defined tasks, selects appropriate tools, and performs self-verification with minimal user intervention. It is available in a standard version, a Thinking mode for extended reasoning budgets, and a Pro variant that uses parallel test-time compute for maximum precision on complex tasks.

Co-optimized with NVIDIA for GB200 NVL72 infrastructure, GPT-5.5 delivers per-token latency comparable to its predecessor GPT-5.4 while maintaining a 1-million-token context window. Despite increased capability, the model achieves greater token efficiency in coding and data analysis workflows, often completing tasks with fewer total tokens than previous versions. OpenAI reports a 60% reduction in hallucination rate compared to GPT-5.4, improving reliability for accuracy-sensitive applications. API access is available via the Responses and Chat Completions endpoints at $5 per million input tokens and $30 per million output tokens, double the unit price of GPT-5.4.

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.