CLEM MatchIt ASCII: leaderboard
CLEM text release v3.0: models compare ASCII depictions through conversation. Scores combine protocol-valid play and conditional task quality, higher is better. Headline preserves the native published product of macro-average played percentage and macro-average conditional quality; per-game scores multiply source-published played and quality components without extra rounding. Source temperature, player configuration and exact evidenced model/checkpoint settings are retained. Undefined conditional quality remains missing rather than imputed zero.
Metric: Game Clemscore (%), text v3.0; published played × quality / 100. Source: huggingface.co. Saturation forecast: Around January 2027. 30 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | GPT-5.2 (Medium) | 100 | #110 (GPT-5.2) |
| 2 | GPT-5.2 (High) | 95 | #110 (GPT-5.2) |
| 3 | Mistral Large 3 | 92.5 | #401 |
| 4 | aya-expanse-32B | 92.5 | #692 |
| 5 | DeepSeek V3.2 | 90 | #205 |
| 6 | Qwen 2.5 72B Instruct | 87.5 | #454 |
| 7 | Qwen 3 Max | 85 | #208 |
| 8 | DeepSeek V3.2 (Non-reasoning) | 85 | #205 (DeepSeek V3.2) |
| 9 | GLM-4.7 | 85 | #192 |
| 10 | Kimi K2.5 (Non-reasoning) | 80 | #144 (Kimi K2.5) |
| 11 | Gemini 3 Flash (Preview) | 80 | #82 |
| 12 | Qwen 3 Next 80B A3B Instruct | 80 | #368 |
| 13 | EuroLLM-22B-Instruct-2512 | 80 | #701 |
| 14 | MiniMax-M2.5 | 77.5 | #303 |
| 15 | Llama 3.1 8B Instruct | 72.5 | #1035 |
No result here: #3 Claude Opus 5.5, #6 GPT-6 Astra, #7 GPT-6.1 Sol.
Interactive version: theaggregate.ai/benchmark?slug=clem-matchit-ascii · How It Works · Data refreshed daily, snapshot 2026-10-20.