DeepSeek: DeepSeek V4.1 Flash 1017 (batch) vs PrismML: Ternary Bonsai 2 9B
Side-by-side comparison of specs, pricing, benchmark scores, and task rankings. Updated 2026-11-07.
| DeepSeek: DeepSeek V4.1 Flash 1017 (batch) | PrismML: Ternary Bonsai 2 9B | |
|---|---|---|
| Vendor | deepseek | prism-ml |
| Quality Score | 100 | 100 |
| Input Price | $0.01/M | $0.08/M |
| Output Price | $0.20/M | $0.13/M |
| Context Window | 1,048,576 | 262,144 |
| Max Output | 943,718 | 65,536 |
| Tool Calling | ✓ | ✓ |
| Structured Output | ✓ | ✓ |
| Reasoning Mode | ✓ | ✓ |
| Vision | ✓ | ✓ |
| Audio | - | - |
The verdict
For a sample job of 1 million input tokens and 250,000 output tokens, DeepSeek V4.1 Flash 1017 (batch) costs $0.061 and Ternary Bonsai 2 9B costs $0.11, so DeepSeek V4.1 Flash 1017 (batch) is about 1.8 times cheaper for the same work.
DeepSeek V4.1 Flash 1017 (batch) reads up to 1,048,576 tokens at once, roughly 1,573 pages of text, against about 393 pages for Ternary Bonsai 2 9B.
Across the 14 tasks where they differ, DeepSeek V4.1 Flash 1017 (batch) scores higher on 14 (SQL Generation, Code Review, Code Completion and others).
Pick DeepSeek V4.1 Flash 1017 (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 | DeepSeek: DeepSeek V4.1 Flash 1017 (batch) | PrismML: Ternary Bonsai 2 9B |
|---|---|---|
| SQL Generation | 134 | 131 |
| Code Review | 132 | 126 |
| Code Completion | 132 | 129 |
| Code Refactoring | 136 | 127 |
| Bug Fixing | 136 | 130 |
| Unit Test Generation | 124 | 121 |
| Code Documentation | 132 | 126 |
| CI/CD Pipelines | 120 | 117 |
| CSV / Spreadsheet Cleanup | 134 | 128 |
| ETL Scripting | 128 | 122 |
| 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 →