OpenAI: GPT-6.1 Sol Pro vs OpenAI: GPT-6 Luna (batch)
Side-by-side comparison of specs, pricing, benchmark scores, and task rankings. Updated 2026-09-30.
| OpenAI: GPT-6.1 Sol Pro | OpenAI: GPT-6 Luna (batch) | |
|---|---|---|
| Vendor | openai | openai |
| Quality Score | 100 | 100 |
| Benchmark Score | - | 62.2 |
| Input Price | $2.00/M | $0.05/M |
| Output Price | $10.00/M | $0.25/M |
| Context Window | 1,050,000 | 1,050,000 |
| Max Output | 128,000 | 128,000 |
| Tool Calling | ✓ | ✓ |
| Structured Output | ✓ | ✓ |
| Reasoning Mode | ✓ | ✓ |
| Vision | ✓ | ✓ |
| Audio | - | - |
| Benchmark Scores | ||
| ai_index | - | 61.5 |
The verdict
For a sample job of 1 million input tokens and 250,000 output tokens, GPT-6 Luna (batch) costs $0.11 and GPT-6.1 Sol Pro costs $4.50, so GPT-6 Luna (batch) is about 40.0 times cheaper for the same work.
Both read up to 1,050,000 tokens at once, roughly 1,575 pages of text.
Across the 20 tasks where they differ, GPT-6 Luna (batch) on 20 (SQL Generation, Code Review, Code Completion and others).
Pick GPT-6 Luna (batch) if you need lower cost 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?
| Task | OpenAI: GPT-6.1 Sol Pro | OpenAI: GPT-6 Luna (batch) |
|---|---|---|
| SQL Generation | 132 | 141 |
| Code Review | 132 | 143 |
| Code Completion | 117 | 133 |
| Code Refactoring | 136 | 145 |
| Bug Fixing | 136 | 147 |
| Unit Test Generation | 124 | 131 |
| Code Documentation | 129 | 137 |
| Regex Writing | 117 | 126 |
| CI/CD Pipelines | 120 | 127 |
| Frontend Component Design | 122 | 129 |
| Data Analysis | 124 | 133 |
| CSV / Spreadsheet Cleanup | 132 | 136 |
| ETL Scripting | 128 | 136 |
| JSON Extraction | 121 | 131 |
| Bulk Data Labeling | 118 | 129 |
| OCR / Document Parsing | 131 | 134 |
| Table Extraction from PDFs | 131 | 134 |
| Long-Document Summarization | 136 | 147 |
| Short-Form Summarization | 113 | 127 |
| Blog Post Writing | 120 | 129 |
Scores reflect capability match + benchmark data + pricing for each task. Methodology →