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GPT-6 Luna vs Grok 4.6

Compare GPT-6 Luna and Grok 4.6 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 Luna
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GrokGrok 4.6
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Models in this comparison

GPT-6 Luna vs Grok 4.6 on Vision Evals

GPT-6 Luna scores higher on 4 of the five Vision Evals tasks.

The widest gap is Object Detection, where GPT-6 Luna leads 65.5% to 23.8%.

Overall, GPT-6 Luna averages 69.7% (#21 of 63) against 59.1% (#36 of 63) for Grok 4.6.

GPT-6 Luna is both cheaper ($0.0004 vs $0.011 per sample) and faster (10.2s vs 19.3s per sample).

GPT-6 LunaGrok 4.6

GPT-6 Luna vs Grok 4.6 Comparison Table

Evals updated October 13, 2026Pricing updated October 16, 2026

PropertyGPT-6 LunaGrok 4.6
OrganizationOpenAISpaceXAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateSep 2026Aug 2026
Context Window1.1M500K
ParametersUnknownUnknown
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$0.100$2.00
Output $/1M$0.500$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
Promptable Concept SegmentationDemoNot listed
Model Features
Foundation VisionSupportedSupported
LLMs with Vision CapabilitiesSupportedSupported
Multimodal VisionSupportedSupported
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
69.7%
59.1%
Avg cost / sample$0.0004$0.011
Avg speed / sample10.21s19.32s
By task
Object Detection (low)
65.5%
±0.4, Mean of 3 runs, range 65.2 to 66.0
$0.0006
23.8%
±2.8, Mean of 3 runs, range 20.2 to 25.9
$0.013
Object Detection (high)
68.0%
±0.7, Mean of 3 runs, range 67.3 to 68.6
$0.0014
24.0%
±1.0, Mean of 3 runs, range 23.1 to 25.1
$0.041
Counting (low)
71.6%
±0.0, Mean of 3 runs, range 71.6 to 71.6
$0.0002
65.8%
±4.1, Mean of 3 runs, range 62.2 to 70.3
$0.0079
Counting (high)
72.1%
±0.7, Mean of 3 runs, range 71.6 to 73.0
$0.0004
56.8%
±1.4, Mean of 3 runs, range 55.4 to 58.1
$0.027
Identification (low)
87.5%
±3.1, Mean of 3 runs, range 84.4 to 90.6
$0.0002
84.4%
±3.1, Mean of 3 runs, range 81.3 to 87.5
$0.0055
Identification (high)
87.5%
±3.1, Mean of 3 runs, range 84.4 to 90.6
$0.0002
85.4%
±1.6, Mean of 3 runs, range 84.4 to 87.5
$0.015
OCR (low)
59.8%
$0.0004
60.6%
$0.011
by category
Single value
41.7%
Transcription
80.0%
Structured JSON
78.2%
Text localization
47.4%
Single value
50.0%
Transcription
89.0%
Structured JSON
84.4%
Text localization
11.0%
OCR (high)
62.5%
$0.0013
61.2%
$0.030
by category
Single value
44.4%
Transcription
84.0%
Structured JSON
81.0%
Text localization
50.0%
Single value
50.9%
Transcription
88.7%
Structured JSON
85.7%
Text localization
9.4%
Reasoning (low)
64.2%
±3.0, Mean of 3 runs, range 60.3 to 66.2
$0.0003
61.1%
±1.3, Mean of 3 runs, range 59.6 to 62.3
$0.0093
Reasoning (high)
71.1%
±2.3, Mean of 3 runs, range 68.2 to 72.8
$0.0004
63.8%
±2.0, Mean of 3 runs, range 62.3 to 66.2
$0.032

GPT-6 Luna vs Grok 4.6: Overview

GPT-6 Luna

GPT-6 Luna is the fast, cost-efficient tier of OpenAI's GPT-6 model family, sitting below GPT-6 Sol and the larger GPT-6 Astra model that opened the generation. It is a proprietary multimodal transformer that accepts text and image input and returns text, and it exposes an adjustable reasoning effort setting so the same model can run in a low-latency mode or spend additional inference compute on harder problems. OpenAI positions it for high-volume and latency-sensitive workloads such as conversational assistants, classification, and lightweight agentic pipelines, while noting that at higher reasoning effort it handles software engineering and computer-use tasks that previously required a Sol-tier model.

The model supports a context window of roughly 1,050,000 input tokens with a maximum output of 128,000 tokens, which allows long documents, extended agent traces, and large code repositories to be processed in a single request. OpenAI describes the GPT-6 generation as improving factual reliability and adopting a more concise communication style relative to the GPT-5.6 series, and attributes the efficiency of the Sol and Luna tiers to gains in caching and inference rather than to reduced capability. Architecture details, parameter counts, and training data are not published.

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.