NVIDIA: Nemotron 3.5 Super VL (free) vs Anthropic: Claude Haiku 5.5
Side-by-side comparison of specs, pricing, benchmark scores, and task rankings. Updated 2026-10-19.
| NVIDIA: Nemotron 3.5 Super VL (free) | Anthropic: Claude Haiku 5.5 | |
|---|---|---|
| Vendor | nvidia | anthropic |
| Quality Score | 100 | 100 |
| Benchmark Score | 40.1 | 72.6 |
| Input Price | Free | $0.10/M |
| Output Price | Free | $0.50/M |
| Context Window | 1,000,000 | 1,000,000 |
| Max Output | 131,072 | 128,000 |
| Tool Calling | ✓ | ✓ |
| Structured Output | ✓ | ✓ |
| Reasoning Mode | ✓ | ✓ |
| Vision | ✓ | ✓ |
| Audio | - | - |
| Benchmark Scores | ||
| ai_index | 43.7 | 71.6 |
The verdict
For a sample job of 1 million input tokens and 250,000 output tokens, Nemotron 3.5 Super VL (free) costs $0 and Claude Haiku 5.5 costs $0.23, so Nemotron 3.5 Super VL (free) is free for the same work.
Both read up to 1,000,000 tokens at once, roughly 1,500 pages of text.
Only Nemotron 3.5 Super VL (free) offers video input.
On our blended benchmark score Claude Haiku 5.5 leads 72.6 to 40.1.
Across the 20 tasks where they differ, Nemotron 3.5 Super VL (free) scores higher on 2 (JSON Extraction, Bulk Data Labeling), Claude Haiku 5.5 on 18 (SQL Generation, Code Review, Code Completion and others).
Pick Nemotron 3.5 Super VL (free) if you need lower cost or video input; pick Claude Haiku 5.5 if you need benchmark performance 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 | NVIDIA: Nemotron 3.5 Super VL (free) | Anthropic: Claude Haiku 5.5 |
|---|---|---|
| SQL Generation | 139 | 142 |
| Code Review | 140 | 145 |
| Code Completion | 132 | 133 |
| Code Refactoring | 143 | 147 |
| Bug Fixing | 144 | 149 |
| Unit Test Generation | 129 | 132 |
| Code Documentation | 135 | 138 |
| Regex Writing | 124 | 127 |
| CI/CD Pipelines | 125 | 128 |
| Frontend Component Design | 127 | 130 |
| Data Analysis | 130 | 134 |
| CSV / Spreadsheet Cleanup | 135 | 136 |
| ETL Scripting | 134 | 138 |
| JSON Extraction | 132 | 131 |
| Bulk Data Labeling | 130 | 129 |
| OCR / Document Parsing | 133 | 134 |
| Table Extraction from PDFs | 133 | 134 |
| Long-Document Summarization | 145 | 149 |
| Short-Form Summarization | 126 | 127 |
| Blog Post Writing | 127 | 130 |
Scores reflect capability match + benchmark data + pricing for each task. Methodology →