PrismML: Ternary Bonsai 2 9B vs Anthropic: Claude Haiku 5.5 (batch)
Side-by-side comparison of specs, pricing, benchmark scores, and task rankings. Updated 2026-11-02.
| PrismML: Ternary Bonsai 2 9B | Anthropic: Claude Haiku 5.5 (batch) | |
|---|---|---|
| Vendor | prism-ml | anthropic |
| Quality Score | 100 | 100 |
| Input Price | $0.08/M | $0.05/M |
| Output Price | $0.13/M | $0.25/M |
| Context Window | 262,144 | 1,000,000 |
| Max Output | 65,536 | 128,000 |
| Tool Calling | ✓ | ✓ |
| Structured Output | ✓ | ✓ |
| Reasoning Mode | ✓ | ✓ |
| Vision | ✓ | ✓ |
| Audio | - | - |
The verdict
For a sample job of 1 million input tokens and 250,000 output tokens, both cost $0.11 at current list prices.
Claude Haiku 5.5 (batch) reads up to 1,000,000 tokens at once, roughly 1,500 pages of text, against about 393 pages for Ternary Bonsai 2 9B.
Across the 17 tasks where they differ, Ternary Bonsai 2 9B scores higher on 3 (Regex Writing, JSON Extraction, Short-Form Summarization), Claude Haiku 5.5 (batch) on 14 (SQL Generation, Code Review, Code Completion and others).
Pick Claude Haiku 5.5 (batch) if you need 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 | PrismML: Ternary Bonsai 2 9B | Anthropic: Claude Haiku 5.5 (batch) |
|---|---|---|
| SQL Generation | 131 | 134 |
| Code Review | 126 | 132 |
| Code Completion | 129 | 131 |
| Code Refactoring | 127 | 136 |
| Bug Fixing | 130 | 136 |
| Unit Test Generation | 121 | 124 |
| Code Documentation | 126 | 131 |
| Regex Writing | 120 | 119 |
| CI/CD Pipelines | 117 | 120 |
| CSV / Spreadsheet Cleanup | 128 | 134 |
| ETL Scripting | 122 | 128 |
| JSON Extraction | 132 | 131 |
| OCR / Document Parsing | 128 | 131 |
| Table Extraction from PDFs | 128 | 131 |
| Long-Document Summarization | 129 | 138 |
| Short-Form Summarization | 124 | 123 |
| Blog Post Writing | 119 | 122 |
Scores reflect capability match + benchmark data + pricing for each task. Methodology →