head-to-head

OpenAI: GPT-5.6 Luna vs Anthropic: Claude Sonnet 5

Side-by-side comparison of specs, pricing, benchmark scores, and task rankings. Updated 2026-10-09.

OpenAI: GPT-5.6 Luna Anthropic: Claude Sonnet 5
Vendoropenaianthropic
Quality Score100100
Benchmark Score62.363.9
Input Price$0.20/M$2.00/M
Output Price$1.20/M$10.00/M
Context Window1,050,0001,000,000
Max Output128,000128,000
Tool Calling✓✓
Structured Output✓✓
Reasoning Mode✓✓
Vision✓✓
Audio--
Benchmark Scores
ai_index61.663.0

The verdict

For a sample job of 1 million input tokens and 250,000 output tokens, GPT-5.6 Luna costs $0.50 and Claude Sonnet 5 costs $4.50, so GPT-5.6 Luna is about 9.0 times cheaper for the same work.

Both take in a similar amount of text: 1,050,000 and 1,000,000 tokens, about 1,575 and 1,500 pages.

On our blended benchmark score Claude Sonnet 5 leads 63.9 to 62.3.

Across the 20 tasks where they differ, GPT-5.6 Luna scores higher on 10 (SQL Generation, Code Completion, Code Documentation and others), Claude Sonnet 5 on 10 (Code Review, Code Refactoring, Bug Fixing and others).

GPT-5.6 Luna was released in July 2026 and Claude Sonnet 5 in June 2026.

Pick GPT-5.6 Luna if you need lower cost; pick Claude Sonnet 5 if you need benchmark performance.

Costs use current list prices per million tokens; page counts assume about 0.75 words per token and 500 words per page.

Who wins by task?

TaskOpenAI: GPT-5.6 LunaAnthropic: Claude Sonnet 5
SQL Generation 141 140
Code Review 143 144
Code Completion 133 118
Code Refactoring 145 146
Bug Fixing 147 148
Unit Test Generation 131 131
Code Documentation 137 135
Regex Writing 125 123
CI/CD Pipelines 127 127
Frontend Component Design 129 129
Data Analysis 133 133
CSV / Spreadsheet Cleanup 136 135
ETL Scripting 136 136
JSON Extraction 131 121
Bulk Data Labeling 129 118
OCR / Document Parsing 134 134
Table Extraction from PDFs 134 134
Long-Document Summarization 147 146
Short-Form Summarization 126 116
Blog Post Writing 129 128

Scores reflect capability match + benchmark data + pricing for each task. Methodology →

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