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GPT-5.6 Luna vs Grok 4.6

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

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

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OpenAIGPT-5.6 Luna
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GrokGrok 4.6
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Models in this comparison

GPT-5.6 Luna vs Grok 4.6 on Vision Evals

Grok 4.6 scores higher on 3 of the five Vision Evals tasks.

The widest gap is Object Detection, where GPT-5.6 Luna leads 61.0% to 23.8%.

Overall, GPT-5.6 Luna averages 64.7% (#27 of 61) against 59.1% (#34 of 61) for Grok 4.6.

GPT-5.6 Luna is both cheaper ($0.0011 vs $0.011 per sample) and faster (8.6s vs 19.3s per sample).

GPT-5.6 LunaGrok 4.6

GPT-5.6 Luna vs Grok 4.6 Comparison Table

Evals updated October 8, 2026Pricing updated October 9, 2026

PropertyGPT-5.6 LunaGrok 4.6
OrganizationOpenAISpaceXAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateJul 2026Aug 2026
Context Window1.5M500K
ParametersUnknownUnknown
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.200$2.00
Output $/1M$1.20$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
64.7%
59.1%
Avg cost / sample$0.0011$0.011
Avg speed / sample8.60s19.32s
By task
Object Detection (low)
61.0%
±1.2, Mean of 3 runs, range 59.9 to 62.2
$0.0015
23.8%
±2.8, Mean of 3 runs, range 20.2 to 25.9
$0.013
Object Detection (high)
62.3%
±1.2, Mean of 3 runs, range 61.4 to 63.8
$0.0050
24.0%
±1.0, Mean of 3 runs, range 23.1 to 25.1
$0.041
Counting (low)
67.1%
±1.4, Mean of 3 runs, range 66.2 to 68.9
$0.0006
65.8%
±4.1, Mean of 3 runs, range 62.2 to 70.3
$0.0079
Counting (high)
70.7%
±3.4, Mean of 3 runs, range 66.2 to 73.0
$0.0015
56.8%
±1.4, Mean of 3 runs, range 55.4 to 58.1
$0.027
Identification (low)
83.3%
±1.6, Mean of 3 runs, range 81.3 to 84.4
$0.0004
84.4%
±3.1, Mean of 3 runs, range 81.3 to 87.5
$0.0055
Identification (high)
84.4%
±6.3, Mean of 3 runs, range 78.1 to 90.6
$0.0007
85.4%
±1.6, Mean of 3 runs, range 84.4 to 87.5
$0.015
OCR (low)
51.8%
$0.0011
60.6%
$0.011
by category
Single value
29.6%
Transcription
75.5%
Structured JSON
73.6%
Text localization
39.7%
Single value
50.0%
Transcription
89.0%
Structured JSON
84.4%
Text localization
11.0%
OCR (high)
55.7%
$0.0041
61.2%
$0.030
by category
Single value
33.0%
Transcription
83.5%
Structured JSON
77.0%
Text localization
43.8%
Single value
50.9%
Transcription
88.7%
Structured JSON
85.7%
Text localization
9.4%
Reasoning (low)
60.5%
±5.0, Mean of 3 runs, range 55.0 to 64.9
$0.0006
61.1%
±1.3, Mean of 3 runs, range 59.6 to 62.3
$0.0093
Reasoning (high)
65.6%
±3.6, Mean of 3 runs, range 60.9 to 68.2
$0.0015
63.8%
±2.0, Mean of 3 runs, range 62.3 to 66.2
$0.032

GPT-5.6 Luna vs Grok 4.6: Overview

GPT-5.6 Luna

GPT-5.6 Luna is the fastest and most cost-efficient model in OpenAI's GPT-5.6 family, which also includes Sol (the flagship tier) and Terra (the balanced mid-tier). Introduced under a new naming convention where the generation number (5.6) and a durable capability tier name (Luna, Terra, Sol) together define each model, Luna occupies the lightweight end of the family and is designed for high-volume, latency-sensitive workloads such as summarization, drafting, autocomplete, classification, and routine automation. The GPT-5.6 family as a whole advances capabilities in software engineering, computer use, professional knowledge work, scientific research, and cybersecurity, with all three tiers rated at the "High" capability level under OpenAI's Preparedness Framework for both cybersecurity and biological/chemical risk domains.

GPT-5.6 Luna supports multimodal input and function calling, and shares the family's 1.5 million token context window. On Terminal-Bench 2.1, Luna scores 82.5%, and on the Artificial Analysis Coding Agent Index it outperforms comparable models at roughly one-quarter the estimated cost of higher-tier alternatives. Luna supports the GPT-5.6 prompt caching scheme, which introduces explicit cache breakpoints and a 30-minute minimum cache life. The model was previewed on June 26, 2026 to a limited group of trusted partners via the OpenAI API and Codex, with general availability rolling out on July 9, 2026 across ChatGPT, Codex, and the API.

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 3 of the five vision tasks and averages 59.1% (#34 of 61) against 64.7% (#27 of 61) for GPT-5.6 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-5.6 Luna leads with 61.0% against 23.8%. This is the widest gap between the two models across the benchmark's tasks.

GPT-5.6 Luna is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0011 per sample against $0.011. GPT-5.6 Luna is priced at $0.20 per 1M input tokens and $1.20 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 Luna is faster. Across Roboflow's Vision Evals it averaged 8.6s per inference against 19.3s. 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 image classification and image captioning in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.