AbstentionBench - underspecified context - GPQA-Diamond - Recall: leaderboard

AbstentionBench evaluates whether a model appropriately declines unanswerable or underspecified questions. This slice reports underspecified context - GPQA-Diamond - Recall as Abstention Recall (%); values converted from 0-1 to percent. Precision and recall must be read together: always abstaining is not necessarily useful. Scenario/dataset pairs and SFT/DPO/RL checkpoints remain separate; no synthetic overall average. Source: facebookresearch/AbstentionBench analysis/abstention_performance.csv.

Metric: Abstention Recall (%). Source: github.com. Saturation forecast: Estimated already saturated. 21 models tracked.

Top models

#ModelScoreOverall rank
1O177.5#190
2GPT-4o75#357
3Llama 3.1 405B Instruct65#470
4Qwen 2.5 32B Instruct65#509
5Llama-3.1-Tulu-3-70B65#499
6Llama-3.1-Tulu-3-70B-DPO65#732
7O1 (High)60#190 (O1)
8Llama 3.1 8B Instruct52.5#1017
9Mistral 7B Instruct (v0.3)52.5#1255
10Gemini 1.5 Pro50#454
11Llama 3.1 70B Instruct45#567
12Llama 3.3 70B Instruct37.5#541
13Llama-3.1-Tulu-3-8B-SFT32.5#977
14DeepSeek R1 Distill Llama 70B25#614
15Llama 3.1 70B22.5#588

No result here: #3 Claude Opus 5.5, #6 GPT-6 Astra, #7 GPT-6.1 Sol.

Interactive version: theaggregate.ai/benchmark?slug=abstentionbench-underspecified-context-gpqa-diamond-recall · How It Works · Data refreshed daily, snapshot 2026-11-05.