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LLM Stats
AlpacaEval 2.0
AlpacaEval 2.0 is a length-controlled automatic evaluator for instruction-following language models that uses GPT-4 Turbo to assess model responses against a baseline. It evaluates models on 805 diverse instruction-following tasks including creative writing, classification, programming, and general knowledge questions. The benchmark achieves 0.98 Spearman correlation with ChatBot Arena while being fast (< 3 minutes) and affordable (< $10 in OpenAI credits). It addresses length bias in automatic evaluation through length-controlled win-rates and uses weighted scoring based on response quality.
Evaluation context and evidence
- Source
- LLM Stats
- Publisher
- Not recorded
- Benchmark release
- Not recorded
- Collected
- 2026-08-17
- Modality
- text
These are source-reported observations, not independent reruns by this site. Check the task version, evaluation protocol, unit and reporting date before comparing models. Results from different setups are not a universal ranking.
Original sources
Reported results 4
Shown in source observation order, not ranked. Dates retain the precision provided by the source.
| Model | Value | Unit | Protocol | Reported date and basis | Source |
|---|---|---|---|---|---|
| Granite 3.3 8B Base | 0.6268 | Not recorded | Not recorded | 2025-04-16 · model_announcement | LLM Stats |
| Granite 3.3 8B Instruct | 0.6268 | Not recorded | Not recorded | 2025-04-16 · model_announcement | LLM Stats |
| DeepSeek-V2.5 | 0.505 | Not recorded | Not recorded | 2024-05-08 · model_announcement | LLM Stats |
| IBM Granite 4.0 Tiny Preview | 0.3516 | Not recorded | Not recorded | 2025-05-02 · model_announcement | LLM Stats |