6G-Bench - Distributed Intelligence and Emerging Use Cases: leaderboard

Metric: Accuracy (%) on the distributed intelligence and emerging 6G use-case tasks (T21-T25, T28) of 6G-Bench's 3,722 expert-validated four-option multiple-choice questions on network-level semantic reasoning for AI-native 6G networks, deterministic single-shot answers (temperature 0, one letter in a JSON object), group score is the unweighted mean of its task accuracies; higher is better. Source: arxiv.org. Saturation forecast: Estimated already saturated. 28 models tracked.

Top models

#ModelScoreOverall rank
1Llama 4 Maverick80.6#451
2Qwen3 Coder Next79.9#321
3Qwen 3 235B A22B 2507 Instruct77.7#291
4Ministral 3 14B77#636
5GPT-5.2 Codex76.4#89
6GPT-5.2 Instant76.2#205
7DeepSeek V3.275.4#198
8Olmo 3.1 32B Instruct74.6#754
9DeepSeek V3.2 Exp74.6#227
10Ministral 3 8B74.4#676
11Qwen 3 VL 32B Instruct73.1#276
12GPT-4o Mini72#588
13Ministral 3 3B71.7#908
14Claude Haiku 4.570.8#271
15GPT-5 Mini70.6#176

No result here: #3 Claude Opus 5.5, #5 GPT-6 Astra, #8 Claude Fable 5.1.

Interactive version: theaggregate.ai/benchmark?slug=6g-bench-distributed-intelligence-and-emerging-use-cases · How It Works · Data refreshed daily, snapshot 2026-10-11.