Meta: Muse Wonder vs OpenAI: GPT-6 Luna Pro (batch)
Side-by-side comparison of specs, pricing, benchmark scores, and task rankings. Updated 2026-10-13.
| Meta: Muse Wonder | OpenAI: GPT-6 Luna Pro (batch) | |
|---|---|---|
| Vendor | meta | openai |
| Quality Score | 100 | 100 |
| Benchmark Score | 90.2 | - |
| Input Price | $4.00/M | $0.05/M |
| Output Price | $20.00/M | $0.25/M |
| Context Window | 1,048,576 | 1,050,000 |
| Max Output | 943,718 | 128,000 |
| Tool Calling | ✓ | ✓ |
| Structured Output | ✓ | ✓ |
| Reasoning Mode | ✓ | ✓ |
| Vision | ✓ | ✓ |
| Audio | ✓ | - |
| Benchmark Scores | ||
| ai_index | 86.4 | - |
The verdict
For a sample job of 1 million input tokens and 250,000 output tokens, GPT-6 Luna Pro (batch) costs $0.11 and Muse Wonder costs $9.00, so GPT-6 Luna Pro (batch) is about 80.0 times cheaper for the same work.
Both take in a similar amount of text: 1,048,576 and 1,050,000 tokens, about 1,573 and 1,575 pages.
Only Muse Wonder offers audio input and video input.
Across the 20 tasks where they differ, Muse Wonder scores higher on 16 (SQL Generation, Code Review, Code Refactoring and others), GPT-6 Luna Pro (batch) on 4 (Code Completion, JSON Extraction, Bulk Data Labeling and others).
Muse Wonder was released in October 2026 and GPT-6 Luna Pro (batch) in September 2026.
Pick Muse Wonder if you need audio input, video input or the stronger all-round task scores; pick GPT-6 Luna Pro (batch) if you need lower cost.
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 | Meta: Muse Wonder | OpenAI: GPT-6 Luna Pro (batch) |
|---|---|---|
| SQL Generation | 142 | 134 |
| Code Review | 148 | 132 |
| Code Completion | 116 | 131 |
| Code Refactoring | 149 | 136 |
| Bug Fixing | 152 | 136 |
| Unit Test Generation | 134 | 124 |
| Code Documentation | 135 | 131 |
| Regex Writing | 123 | 119 |
| CI/CD Pipelines | 130 | 120 |
| Frontend Component Design | 132 | 122 |
| Data Analysis | 137 | 124 |
| CSV / Spreadsheet Cleanup | 135 | 134 |
| ETL Scripting | 140 | 128 |
| JSON Extraction | 119 | 131 |
| Bulk Data Labeling | 114 | 129 |
| OCR / Document Parsing | 135 | 131 |
| Table Extraction from PDFs | 135 | 131 |
| Long-Document Summarization | 149 | 138 |
| Short-Form Summarization | 115 | 123 |
| Blog Post Writing | 130 | 122 |
Scores reflect capability match + benchmark data + pricing for each task. Methodology →