need to compare model outputs at different temperatures

To compare model outputs at different temperatures, use the same prompt and generate multiple samples at each temperature, then compare the results on the dimensions you care about.

Practical approach

1. Choose temperatures to test, e.g.:

   • 0.0
   • 0.2
   • 0.5
   • 0.8
   • 1.0

2. Keep everything else fixed:

   • same prompt
   • same model
   • same max tokens
   • same top_p / sampling settings
   • same system instructions

3. Generate multiple runs per temperature

   • because higher temperatures are more variable
   • e.g. 5–20 samples each

4. Compare outputs Common comparison criteria:

   • Accuracy / correctness
   • Consistency
   • Creativity / diversity
   • Formatting reliability
   • Hallucination rate
   • Task-specific score (e.g. exact match, F1, human preference)

What to expect

• Low temperature: more deterministic, stable, conservative
• Medium temperature: balanced variety and reliability
• High temperature: more diverse, but more errors and less consistency

Simple comparison table

Temp	Typical behavior
0.0	Most deterministic
0.2	Slight variation, still stable
0.5	Balanced
0.8	More creative, less predictable
1.0+	Highly variable

If you want to evaluate systematically

You can build a small test set and score each output using:

• exact match for factual tasks
• rubric-based human evaluation
• automated metrics like BLEU/ROUGE for summarization
• pass/fail checks for code or structured output

Example workflow

For each prompt:

• run model 10 times at each temperature
• store outputs
• compute:
  • average quality score
  • variance
  • uniqueness rate
  • error rate

If you want, I can help you with:

• a Python script to do this,
• a JSON schema for logging outputs,
• or a comparison rubric for your specific task.