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Claude Sonnet 4.6 vs GPT-5.6 Luna

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

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AnthropicClaude Sonnet 4.6
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OpenAIGPT-5.6 Luna
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Claude Sonnet 4.6 vs GPT-5.6 Luna: Overview

Claude Sonnet 4.6

Claude Sonnet 4.6 is Anthropic's mid-tier large language model, released February 17, 2026, designed to balance performance, cost, and versatility for professional and developer use. It supports text and vision-based tasks with advanced reasoning, agentic capabilities, and Adaptive Thinking — a mode where the model dynamically scales its internal reasoning depth. A beta context window of up to 1,000,000 tokens (200K standard) enables processing of entire codebases or document collections in a single request. Parameters are undisclosed.

Optimized for coding, computer use, long-context reasoning, agent planning, and knowledge work, Sonnet 4.6 delivers a full generational upgrade over Sonnet 4.5 and approaches Opus 4.5-level performance across many benchmarks at a fraction of the cost. It is the default model on Claude.ai, Claude Cowork, and is available via API and major cloud platforms — making it well suited for production workloads requiring strong reasoning without flagship pricing.

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 is priced at $1 per million input tokens and $6 per million output tokens, with cached input reads at $0.10 per million tokens under 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.

Claude Sonnet 4.6 vs GPT-5.6 Luna Comparison Table

PropertyClaude Sonnet 4.6GPT-5.6 Luna
OrganizationAnthropicOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateFeb 2026Jul 2026
Context Window1.0M1.5M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$3.00$1.00
Output $/1M$15.00$6.00
Vision Tasks
CaptioningDemoDemo
ClassificationDemoDemo
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Document Question Answering
Model Features
LLMs with Vision Capabilities
Multimodal Vision
Foundation Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
70.15%
Avg Response Time4.24s
Median input tokensincl. image tokens2.2K
Median output tokens105
Est. cost / taskon this benchmark$0.0080
Defect Detection
80%(12/15)
Document Understanding
77.8%(7/9)
Object Counting
30%(3/10)
Object Understanding
71.4%(10/14)
Spatial Understanding
78.9%(15/19)
OCR
Overall Score
81.66%
Avg Response Time3.42s
Median input tokensincl. image tokens736
Median output tokens85
Est. cost / taskon this benchmark$0.0035
Focused Scene OCR
85.9%(85/99)
Handwritten Math
50%(5/10)
License Plate Recognition
90%(27/30)
Text Recognition
86.7%(26/30)
VQA & Extraction
73.3%(44/60)

Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology