AbstentionBench - underspecified context - MMLU Math - F1: leaderboard

AbstentionBench evaluates whether a model appropriately declines unanswerable or underspecified questions. This slice reports underspecified context - MMLU Math - F1 as Abstention F1 (%); 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 F1 (%). Source: github.com. Saturation forecast: Estimated already saturated. 20 models tracked.

Top models

#ModelScoreOverall rank
1Llama 3.1 70B Instruct84.48#567
2GPT-4o82.25#357
3O181.94#190
4Llama 3.1 405B Instruct81.78#470
5O1 (High)81.25#190 (O1)
6Qwen 2.5 32B Instruct78.18#509
7Llama 3.1 8B Instruct78.03#1017
8Llama-3.1-Tulu-3-70B-DPO76.15#732
9Mistral 7B Instruct (v0.3)71#1255
10Gemini 1.5 Pro67.66#454
11Llama 3.3 70B Instruct64.97#541
12DeepSeek R1 Distill Llama 70B49.45#614
13Llama-3.1-Tulu-3-8B-DPO46.33#992
14OLMo-7B-0724-Instruct-hf45.9#1441
15Llama-3.1-Tulu-3-8B-SFT43.27#977

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-mmlu-math-f1 · How It Works · Data refreshed daily, snapshot 2026-11-05.