Can AI actually get the joke, or just recite the punchline?
We gave 15 frontier models raw XKCD strips and hover text with zero spoilers, then graded their explanations against human ExplainXKCD contributors using TypeSafe System One (Noul). Compare how Gemini 4 Argon, Claude Opus 5.5, Claude Fable 5.1, Gemma 4 31B, and GPT-6 Luna break down the humor, or test reverse comic search across 891 strips.
| Rank | Model | Modality | Composite Score | Humor & Punchline | Domain Depth | Visual Grounding | Alt-Text Synthesis | Concept Recall | Avg Latency |
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| Model | #327Bobby Tables | #2347Dependency | #2630Shuttle Skel | #927Standards | #386Duty Calls | #1838Machine Lrn | #882Significant | #1597Git | MeanComposite |
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| Rank | Retrieval Engine | Architecture | Hit@1 (Top-1) | Hit@3 | Hit@5 | MRR@10 | Calibration ECE | P50 Latency |
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Blind Multimodal Prompting & Hybrid Rubric
Every model is evaluated blind: vision models receive only the raw comic .png bytes and official hover alt text (max_tokens=4096, temperature=0.2), with zero access to ExplainXKCD ground truth.
Noul) + 60% Deterministic Reference (Stem Concept Recall + Lexical F1 @ 115 tokens)BM25F + 64-Bit dHash + TypeSafe Jev Rerank
Reverse search resolves vague human memory queries ("Bobby Tables SQL injection", "whale bones inside space shuttle") and cropped comic panels across all 891 strips in three fused stages:
- 1. BM25F Index: Field-weighted lexical retrieval (
title ×4.0,concepts ×3.5,alt ×2.5,transcript ×2.0,explanation ×1.2). - 2. 64-Bit dHash: 9×8 LANCZOS grayscale luminance gradient hashing with normalized Hamming similarity (
1 − dH / 64). - 3. TypeSafe Jev + RRF: Parallel System One binary judgment reranking fused via Reciprocal Rank Fusion (
k = 60), achieving 100.0% Hit@1 across all 30 queries.
Cross-Joined Hugging Face Datasets
Built by cross-joining olivierdehaene/xkcd (2,630 ExplainXKCD crowd-annotated strips) with devparagiri/dataset-xkcd (curated topic tags and hover titles) into 891 verified local comics partitioned across 6 technical domains: