Llama 3 pairwise human evaluation (Meta)
Human raters compare Llama responses with a rival model on about 7,000 prompts across six capabilities and three multiturn sets.
In-house Meta evaluation. Results are win and loss rates per capability and rival, with no overall score.
- Made by
- Meta
- Released
- July 2024
- Kind
- Overall chat quality
- Scored
- Ranked by votes
- Answers
- Open-ended
- Tasks
- Kept private
- Last in a system card
- November 2024
- Last model added
- November 2024
1
System card reports it, from 1 lab
No scores here yet
No lab, leaderboard or Epoch AI run has reported a score for Llama 3 pairwise human evaluation (Meta) yet.
Labs report it only in system cards
We read the headline results of 522 launch posts from 52 labs, and none of them list this benchmark. Some system cards do, below.
One system card reports it
Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| Nov 23, 2024 |  Llama 3.1-405B technical report | Pairwise human evaluation win and loss rates per capability against GPT-4 (0125), GPT-4o and Claude 3.5 Sonnet, no overall (Figure 17) | Capabilities |
More in Instructions and chat
- IFEval In use Models answer about 500 prompts containing verifiable instructions such as word limits or required keywords. Code checks each instruction for strict and loose accuracy.
Gemma 4 31B IT 98.9% - IFBench In use 58 new out-of-domain verifiable output constraints. Scored by programmatic constraint checks (strict or loose).
Nemotron-Cascade 2 30B-A3B 82.9% - MultiChallenge In use Multi-turn conversations testing instruction retention, inference memory, versioned editing and self-coherence. Graded by LLM judge with rubrics. GPT-6.1 Sol 82%
- Multi-IF In use Models follow verifiable instructions over three-turn conversations in eight languages, extending IFEval. Scored by instruction accuracy at each turn. LFM3-1.7B 89.1%
- AdvancedIF In use Models respond to over 1,600 prompts with complex, multi-turn, and system-level instructions. A judge model checks each response against expert-written rubrics.
MAI-Thinking-1 85% - IFStruct In use Models generate JSON or YAML that must follow a requested schema and phrasing constraints, without constrained decoding. A response passes only if every structural constraint holds. Gemma 4 31B IT 95.9%