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Claude Sonnet 4.5 vs GPT-6 Luna

Compare Claude Sonnet 4.5 and GPT-6 Luna side-by-side. See how these vision models stack up in Object Detection, Classification, Image Captioning, OCR, and Open Prompt.

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AnthropicClaude Sonnet 4.5
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OpenAIGPT-6 Luna
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Claude Sonnet 4.5 vs GPT-6 Luna Comparison Table

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

PropertyClaude Sonnet 4.5GPT-6 Luna
OrganizationAnthropicOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateSep 2025Sep 2026
Context Window200K1.1M
ParametersUnknownUnknown
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$3.00$0.100
Output $/1M$15.00$0.500
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 SegmentationNot listedDemo
Model Features
Foundation VisionSupportedSupported
LLMs with Vision CapabilitiesSupportedSupported
Multimodal VisionSupportedSupported
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
OverallNot evaluated
69.7%
Avg cost / sample–$0.0004
Avg speed / sample–10.21s
By task
Object Detection (low)–
65.5%
±0.4, Mean of 3 runs, range 65.2 to 66.0
$0.0006
Object Detection (high)–
68.0%
±0.7, Mean of 3 runs, range 67.3 to 68.6
$0.0014
Counting (low)–
71.6%
±0.0, Mean of 3 runs, range 71.6 to 71.6
$0.0002
Counting (high)–
72.1%
±0.7, Mean of 3 runs, range 71.6 to 73.0
$0.0004
Identification (low)–
87.5%
±3.1, Mean of 3 runs, range 84.4 to 90.6
$0.0002
Identification (high)–
87.5%
±3.1, Mean of 3 runs, range 84.4 to 90.6
$0.0002
OCR (low)–
59.8%
$0.0004
by category
Single value
41.7%
Transcription
80.0%
Structured JSON
78.2%
Text localization
47.4%
OCR (high)–
62.5%
$0.0013
by category
Single value
44.4%
Transcription
84.0%
Structured JSON
81.0%
Text localization
50.0%
Reasoning (low)–
64.2%
±3.0, Mean of 3 runs, range 60.3 to 66.2
$0.0003
Reasoning (high)–
71.1%
±2.3, Mean of 3 runs, range 68.2 to 72.8
$0.0004

Claude Sonnet 4.5 vs GPT-6 Luna: Overview

Claude Sonnet 4.5

Claude Sonnet 4.5, released by Anthropic in September 2025, is the company’s most advanced Sonnet-series model, built for high-performance reasoning, coding, and long-horizon agentic workflows. It is a multimodal system that accepts both text and images, with a 200,000-token context window designed for handling large documents and extended interactions. Anthropic highlights its improvements in reliability, reduced sycophancy, and alignment, making it suitable for sustained enterprise use.

The model delivers strong results in coding and autonomous workflows, achieving 61.4% on the OSWorld benchmark and leading performance on SWE-bench Verified. It introduces infrastructure features such as a memory tool (beta), checkpointing for Claude Code, parallel tool use, and tighter integration with VS Code. Compared to Opus, which targets broader reasoning, Sonnet 4.5 is optimized for structured, long-duration tasks. Positioned against leading offerings from OpenAI and Google, it is aimed at enterprise automation, software engineering, and research-intensive applications.

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.

Frequently Asked Questions

Claude Sonnet 4.5 has not yet been evaluated on Roboflow's current Vision Evals, so this comparison shows specs, licensing, and pricing rather than benchmark scores.

Yes. The comparison demo on this page runs both models on the same image side by side for object detection and image classification in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.