Roboflow

GPT-6 Luna vs Grok 4.6

Compare GPT-6 Luna and Grok 4.6 side-by-side.

Compare GPT-6 Luna vs Grok 4.6 live

Run the same image across every model that supports a task and compare their outputs side-by-side.

These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.

Models in this comparison

GPT-6 Luna vs Grok 4.6 on Vision Evals

Grok 4.6 scores higher on 4 of the six Vision Evals tasks.

The widest gap is Object Detection, where GPT-6 Luna leads 56.8% to 23.8%.

Overall, GPT-6 Luna averages 68.6% (#32 of 57) against 68.7% (#30 of 57) for Grok 4.6.

GPT-6 Luna is both cheaper ($0.0004 vs $0.0097 per sample) and faster (11.3s vs 17.5s per sample).

GPT-6 LunaGrok 4.6

GPT-6 Luna vs Grok 4.6 Comparison Table

Evals updated September 22, 2026Pricing updated September 22, 2026

PropertyGPT-6 LunaGrok 4.6
OrganizationOpenAISpaceXAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateSep 2026Aug 2026
Context Window1.1M500K
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$2.00
Output $/1M$6.00
Vision Tasks
CaptioningDemo
Chart Question Answering
ClassificationDemo
Document Question Answering
Image Tagging
Multi-Label Classification
Object DetectionDemo
OCRDemo
Vision Language
Visual Question AnsweringDemo
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort
Overall
68.6%
68.7%
Avg cost / sample$0.0004$0.0097
Avg speed / sample11.27s17.55s
By task
Object Detection (low)
56.8%
±1.9, Mean of 3 runs, range 54.8 to 58.5
$0.0006
23.8%
±2.8, Mean of 3 runs, range 20.2 to 25.9
$0.013
Object Detection (high)
64.1%
±0.5, Mean of 3 runs, range 63.6 to 64.5
$0.0016
24.0%
±1.0, Mean of 3 runs, range 23.1 to 25.1
$0.041
Counting (low)
65.8%
±1.4, Mean of 3 runs, range 64.9 to 67.6
$0.0003
65.8%
±4.1, Mean of 3 runs, range 62.2 to 70.3
$0.0079
Counting (high)
64.4%
±2.0, Mean of 3 runs, range 62.2 to 66.2
$0.0006
56.8%
±1.4, Mean of 3 runs, range 55.4 to 58.1
$0.027
Identification (low)
81.3%
±0.0, Mean of 3 runs, range 81.3 to 81.3
$0.0002
84.4%
±3.1, Mean of 3 runs, range 81.3 to 87.5
$0.0055
Identification (high)
80.2%
±1.6, Mean of 3 runs, range 78.1 to 81.3
$0.0003
85.4%
±1.6, Mean of 3 runs, range 84.4 to 87.5
$0.015
OCR (low)
87.9%
±0.6, Mean of 3 runs, range 87.2 to 88.3
$0.0005
91.8%
±0.3, Mean of 3 runs, range 91.5 to 92.1
$0.0091
OCR (high)
88.5%
±0.6, Mean of 3 runs, range 87.9 to 89.2
$0.0014
91.6%
±0.2, Mean of 3 runs, range 91.4 to 91.7
$0.023
Data Extraction (low)
68.0%
±3.1, Mean of 3 runs, range 65.0 to 71.1
$0.0002
85.6%
±1.0, Mean of 3 runs, range 84.5 to 86.6
$0.0050
Data Extraction (high)
66.7%
±0.5, Mean of 3 runs, range 66.0 to 67.0
$0.0004
85.6%
±1.0, Mean of 3 runs, range 84.5 to 86.6
$0.0090
Reasoning (low)
52.1%
±2.0, Mean of 3 runs, range 49.7 to 53.6
$0.0003
61.1%
±1.3, Mean of 3 runs, range 59.6 to 62.3
$0.0093
Reasoning (high)
60.7%
±1.7, Mean of 3 runs, range 58.9 to 62.3
$0.0006
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.

Frequently Asked Questions

On Roboflow's Vision Evals, Grok 4.6 performed better. It scores higher on 4 of the six vision tasks and averages 68.7% (#30 of 57) against 68.6% (#32 of 57) for GPT-6 Luna. 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 Luna leads with 56.8% against 23.8%. This is the widest gap between the two models across the benchmark's tasks.

GPT-6 Luna is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0004 per sample against $0.0097. Actual costs depend on your image sizes, prompts, and output length.

GPT-6 Luna is faster. Across Roboflow's Vision Evals it averaged 11.3s per inference against 17.5s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.