head-to-head

OpenAI: GPT-5.6 Luna (batch) vs Qwen: Qwen3.7 Plus

Side-by-side comparison of specs, pricing, benchmark scores, and task rankings. Updated 2026-11-07.

OpenAI: GPT-5.6 Luna (batch) Qwen: Qwen3.7 Plus
Vendoropenaiqwen
Quality Score100100
Benchmark Score89.464.8
Input Price$0.10/M$0.32/M
Output Price$0.60/M$1.28/M
Context Window1,050,0001,000,000
Max Output128,000131,072
Tool Calling✓✓
Structured Output✓✓
Reasoning Mode✓✓
Vision✓✓
Audio--
Benchmark Scores
ai_index84.564.3
ai_index_agentic75.234.3
ai_index_coding100.092.2

The verdict

For a sample job of 1 million input tokens and 250,000 output tokens, GPT-5.6 Luna (batch) costs $0.25 and Qwen3.7 Plus costs $0.64, so GPT-5.6 Luna (batch) is about 2.6 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 GPT-5.6 Luna (batch) leads 89.4 to 64.8.

Across the 20 tasks where they differ, GPT-5.6 Luna (batch) scores higher on 20 (SQL Generation, Code Review, Code Completion and others).

GPT-5.6 Luna (batch) was released in July 2026 and Qwen3.7 Plus in June 2026.

Pick GPT-5.6 Luna (batch) if you need lower cost, benchmark performance or the stronger all-round task scores.

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 Luna (batch)Qwen: Qwen3.7 Plus
SQL Generation 171 162
Code Review 166 157
Code Completion 133 133
Code Refactoring 163 157
Bug Fixing 179 166
Unit Test Generation 154 147
Code Documentation 143 140
Regex Writing 136 131
CI/CD Pipelines 144 138
Frontend Component Design 146 140
Data Analysis 170 156
CSV / Spreadsheet Cleanup 154 151
ETL Scripting 154 147
JSON Extraction 149 146
Bulk Data Labeling 135 134
OCR / Document Parsing 146 144
Table Extraction from PDFs 146 144
Long-Document Summarization 160 153
Short-Form Summarization 132 129
Blog Post Writing 140 135

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

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