Honesty human feedback win rate (Anthropic)
Crowdworkers try to make models say something false and pick the more honest reply in head-to-head chats. Score is the win rate against a baseline.
In-house Anthropic evaluation. The baseline model was Claude Sonnet 4, so 50% means equal honesty.
- Made by
- Anthropic
- Released
- September 2025
- Kind
- Hallucination rates
- Level
- Everyday
- Scored
- Ranked by votes
- Answers
- Open-ended
- Tasks
- Kept private
- Strongest model tested
- Claude Opus 4.6 #37 on the capability index
- Last in a system card
- February 2026
- Last model added
- February 2026
6 models have ratings here, all from their labs’ own posts
Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs. A rating ranks a model only against others rated in the same arena at the same time, so these don’t show who leads.
The newest model with a score here came out in February 2026, and nobody has reported a newer one. Among the 20 highest-ranked models on the capability index, these have no score yet:
- Claude Fable 5.2
-
Gemini 4 Carbon - GPT-6 Astra
- Claude Opus 5.5
- and 16 more
- 1 Claude 3 Sonnet 69%
- 2 Claude 3.5 Sonnet 66%
- 3 Claude Opus 4.6 61%
- 4 Claude Sonnet 4.5 57%
- 5 Claude 3.5 Sonnet (October 2024) 55%
- 6 Claude 3.5 Haiku 42%
Source: Labs’ model cards
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Claude 3 Sonnet | 69% | Mar 2024 | Lab model card | |
| 2 | Claude 3.5 Sonnet | 66% | Jun 2024 | Lab model card | |
| 3 | Claude Opus 4.6 | 61% | Feb 2026 | Lab model card | |
| 4 | Claude Sonnet 4.5 | 57% | Sep 2025 | Lab model card | |
| 5 | Claude 3.5 Sonnet (October 2024) | 55% | Oct 2024 | Lab model card | |
| 6 | Claude 3.5 Haiku | 42% | Oct 2024 | Lab model card |
Number one over time
Lab claims count from the day the card came out, independent runs from the model’s release.
Source: Labs’ model cards
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Source: Labs’ model cards
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.
6 system cards report it, from 1 lab
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 |
|---|---|---|---|---|
| Feb 5, 2026 | Claude Opus 4.6 system card | 61 | Honesty HF win rate against a Claude Opus 4.5 baseline, 64k thinking, value printed on chart; 54 without thinking (Figure 4.1.A) | Hallucination |
| Sep 29, 2025 | Claude Sonnet 4.5 system card | 57 | Honesty HF win rate against Claude Sonnet 4, Claude Sonnet 4.5 with 16k thinking 57. Without thinking 50 (Figure 3.1.A) | Hallucination |
| Oct 22, 2024 | Claude 3.5 Sonnet (October 2024) model card | 55 | Human feedback win rate vs Claude 3.5 Sonnet baseline, Honesty (adversarial scenario) 55 (Figure 2) | Safety |
| Oct 22, 2024 | Claude 3.5 Haiku model card | 42 | Human feedback win rate vs Claude 3.5 Sonnet baseline, Honesty (adversarial scenario) 42 (Figure 2) | Safety |
| Jun 20, 2024 | Claude 3.5 Sonnet model card | 66 | Honesty, human preference win rate vs Claude 3 Opus baseline, adversarial scenarios (Figure 3) | Safety |
| Mar 4, 2024 | Claude 3 Sonnet model card | 69 | Human preference win rate vs Helpful-only baseline, Honesty (adversarial scenario) 69 (Figure 8) | Safety |
More in Factuality
- SimpleVQA In use Models answer 2,025 short factual questions about images across 9 tasks and 9 topics. An LLM judge grades answers against fixed references.
Qwen3.7-Plus 81.7% - HallusionBench In use Models answer 1,129 yes or no questions about 346 images, built in pairs to expose language hallucination and visual illusion. Scored by accuracy.
Keye-VL-2.0-30B-A3B 73.5% - SimpleQA Verified In use 1,000 human-verified, de-duplicated short-answer factual questions measuring parametric knowledge. Gemini 3.1 Pro 77.5%
- Kimi Hallu Bench In use Models answer hard prompts drawn from practical work tasks. Fact-checking agents score the share of claims that are hallucinated. GPT-6 Astra 4.20%
- Internal adversarially-designed factuality evaluations In use Models answer adversarial questions in nine categories such as history, math and code. Scored on agreement with human ideal responses. GPT-6 Luna 7.60%
- AA-Omniscience v1.1 In use Models answer 3,000 knowledge questions in six domains without tools and may abstain. The index adds correct answers and subtracts wrong ones.
Inkling 2 Small 16.1