When you are choosing between models, or between prompt variants, eyeballing two walls of text rarely gives a clear answer. This tool lays up to five responses side by side, lets you define the quality dimensions that matter, and tallies your scores so the winner is obvious.
When to use a structured comparison table
Informal model comparisons are often biased by anchoring: whichever response you read first sets the reference frame. A structured table breaks that pattern by separating the criteria definition phase from the scoring phase. Define what matters before you read the outputs, then score each against the same criteria. This is the same approach used in formal LLM evaluation studies and competitive bake-offs — scaled down to a browser tool.
How it works
Paste each response and give it a label — the model name, prompt variant, or temperature setting. Add rating dimensions — your own criteria such as accuracy, tone, or completeness — and score every response from 0 to 5 on each. The tool sums the scores into a per-response total and shows objective stats like word and character count beside them. Everything runs locally; nothing is uploaded.
Useful rating dimensions by task type
| Task type | Suggested dimensions |
|---|---|
| Factual question answering | Accuracy, completeness, citation quality |
| Customer-facing copy | Tone, clarity, brand alignment, conciseness |
| Code generation | Correctness, readability, edge-case handling |
| Summarization | Coverage of key points, conciseness, no hallucinations |
| Instruction following | Adherence to format, all parts answered |
Keep dimensions to three or four — more than that and scores cluster together, making the comparison less useful.
Why score manually rather than use an LLM judge
Automated graders can assign scores quickly but they are blind to domain-specific nuance: a subtle factual error, an off-brand phrase, a missing safety caveat. Manual scoring keeps domain expertise in the loop while the tool handles the part machines are good at — structuring the comparison and adding up the numbers. For high-volume evaluation, combine both: use automated grading for a first pass and manual review for the borderline cases.
Tips
- Define your dimensions before reading the responses to avoid anchoring on whichever you see first.
- Watch the word counts — a higher total score on a much longer answer may reflect more padding rather than more quality; adjust for length if that matters.
- If two responses score identically, the dimensions may be too broad — add a more specific sub-criterion to break the tie.
Saving and sharing your comparison
The table runs locally and nothing is uploaded, so you will need to export your results before closing the tab. Copy the scored table as a screenshot or use the browser’s built-in print-to-PDF if you want to share a record of the comparison with a team member or document it in a decision log. Keeping a record of close bake-offs is valuable because it forces you to articulate why one response was better — which often surfaces prompt improvements that apply to whichever model you choose.