HalBench v2.3 tests 29 OSS models on sycophancy resistance
A custom sycophancy and hallucination benchmark (HalBench v2.3) tested 33 models on a 3,076-item corpus of false premises, finding that only Sonnet 4.6 and Grok 4.3 clear 50% pushback, while qwen3.6 (~27B) tops all open models at 36.6% — beating models up to 402B in size.
Score breakdown
HalBench v2.3 shows that sycophancy resistance is largely decoupled from model size and architecture, with a ~27B model outperforming models up to 402B and several closed frontier models on false-premise pushback.
- 01HalBench v2.3 tests 33 models (29 open-source) on a 3,076-item corpus of false premises, scoring pushback vs. compliance.
- 02Only Sonnet 4.6 (65.1% pushback) and Grok 4.3 (50.9%) cleared the 50% pushback threshold.
- 03qwen3.6 (~27B dense) is the top open-source model at 36.6% pushback, beating all larger open models tested.
HalBench v2.3 is an open benchmark designed to measure LLM sycophancy and hallucination resistance by presenting models with false premises and scoring whether they push back (score of 1) or comply and elaborate (score of 0). The benchmark was posted to r/LocalLLaMA by u/Saraozte01, who credits u/jipok_ for identifying weak questions and scoring issues that prompted a corpus audit. After dropping 124 items across multiple audit passes — including mislabeled domain items and prompts that conflated instruction-following with sycophancy — the final v2.3 corpus stands at 3,076 items. Scores for the four original closed frontier models were recalibrated accordingly.
The top open-source model is qwen3.6 (~27B dense) at a mean of 0.421 and 36.6% pushback, followed by gemma-4-26b-a4b (26B MoE, 29.2%) and gemma-4-31b (31B dense, 25.5%).
Across 33 models tested, only Sonnet 4.6 (mean 0.558, 65.1% pushback) and Grok 4.3 (mean 0.488, 50.9%) cleared the 50% pushback threshold. The top open-source model is qwen3.6 (~27B dense) at a mean of 0.421 and 36.6% pushback, followed by gemma-4-26b-a4b (26B MoE, 29.2%) and gemma-4-31b (31B dense, 25.5%). These results place qwen3.6 ahead of closed models GPT-5.4 (27.5%) and Gemini 3.1 Pro (20.6%), and ahead of every larger open model in the set, including Llama-4-Maverick at 402B (8.2%), MiMo v2 flash at 310B (16.9%), and Qwen3-235B at 235B (14.8%). phi-4 (14B dense) finished last at a mean of 0.157 and 2.3% pushback.
The post argues that model size is a weak predictor of sycophancy resistance, noting a rank correlation of approximately 0.42 between parameter count and pushback, with the four largest models all landing mid-to-bottom. MoE versus dense architecture also shows no clean separation — the top two open models are dense and MoE respectively, and both architectures are scattered across the rankings. The post characterizes this as a training-recipe property rather than a scale or architectural one. The dataset, benchmark space, and code are described as open and linked in the original post.
Key facts
- 01HalBench v2.3 tests 33 models (29 open-source) on a 3,076-item corpus of false premises, scoring pushback vs. compliance.
- 02Only Sonnet 4.6 (65.1% pushback) and Grok 4.3 (50.9%) cleared the 50% pushback threshold.
- 03qwen3.6 (~27B dense) is the top open-source model at 36.6% pushback, beating all larger open models tested.
- 04Llama-4-Maverick (402B MoE) scored only 8.2% pushback, placing 23rd out of 33 models.
- 05phi-4 (14B dense) finished last at 2.3% pushback, complying with false premises on roughly 98% of items.
- 06Rank correlation between parameter count and pushback is approximately 0.42 — the post characterizes size as a weak predictor.
- 07The corpus was reduced from a prior version by dropping 124 items after community-flagged issues with mislabeled domains and instruction-following conflation.
Topics
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