FAB-Bench (32K Context): leaderboard
Retrieval-augmented question answering in semiconductor manufacturing (FAB-Bench, Qian et al., FutureFab.AI, 2026): 200 questions generated from device-conference papers, patents and SEMI standards, answered through a fixed AnythingLLM pipeline with a 32K-token context window and 4K output tokens. GPT-4.1-mini scores each answer with G-Eval on factuality, technical depth, completeness, relevance, context utilization and support quality; the score is their mean on a 0-1 scale, and higher is better.
Metric: Mean of six GPT-4.1-mini G-Eval scores (0-1: factuality, technical depth, completeness, relevance, context utilization, support quality) over 200 questions, AnythingLLM retrieval with a 32K-token context window and 4K output tokens. Source: arxiv.org. Saturation forecast: Estimated already saturated. 4 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | DeepSeek V3.2 Exp | 0.88 | #436 |
| 2 | Gemini 2.5 Flash | 0.84 | #452 |
| 3 | Qwen 2.5 72B Instruct | 0.8 | #1093 |
No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.
Interactive version: theaggregate.ai/benchmark?slug=fab-bench-32k-context · How It Works · Data refreshed daily, snapshot 2026-11-12.