OpenAI: GPT-6 Luna Pro (batch) vs PrismML: Ternary Bonsai 2 27B
Side-by-side comparison of specs, pricing, benchmark scores, and task rankings. Updated 2026-10-13.
| OpenAI: GPT-6 Luna Pro (batch) | PrismML: Ternary Bonsai 2 27B | |
|---|---|---|
| Vendor | openai | prism-ml |
| Quality Score | 100 | 100 |
| Input Price | $0.05/M | $0.07/M |
| Output Price | $0.25/M | $0.50/M |
| Context Window | 1,050,000 | 262,144 |
| Max Output | 128,000 | 32,768 |
| Tool Calling | ✓ | ✓ |
| Structured Output | ✓ | ✓ |
| Reasoning Mode | ✓ | ✓ |
| Vision | ✓ | ✓ |
| Audio | - | - |
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 Ternary Bonsai 2 27B costs $0.20, so GPT-6 Luna Pro (batch) is about 1.8 times cheaper for the same work.
GPT-6 Luna Pro (batch) reads up to 1,050,000 tokens at once, roughly 1,575 pages of text, against about 393 pages for Ternary Bonsai 2 27B.
Across the 15 tasks where they differ, GPT-6 Luna Pro (batch) scores higher on 15 (SQL Generation, Code Review, Code Completion and others).
Pick GPT-6 Luna Pro (batch) if you need lower cost, long documents 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 Luna Pro (batch) | PrismML: Ternary Bonsai 2 27B |
|---|---|---|
| SQL Generation | 134 | 131 |
| Code Review | 132 | 126 |
| Code Completion | 131 | 129 |
| Code Refactoring | 136 | 127 |
| Bug Fixing | 136 | 130 |
| Unit Test Generation | 124 | 121 |
| Code Documentation | 131 | 126 |
| CI/CD Pipelines | 120 | 117 |
| CSV / Spreadsheet Cleanup | 134 | 128 |
| ETL Scripting | 128 | 122 |
| Bulk Data Labeling | 129 | 129 |
| OCR / Document Parsing | 131 | 128 |
| Table Extraction from PDFs | 131 | 128 |
| Long-Document Summarization | 138 | 129 |
| Blog Post Writing | 122 | 119 |
Scores reflect capability match + benchmark data + pricing for each task. Methodology →