Three bar charts are presented. In the AMIE-25 test, Ring-flash-linear-2.0 scores 37% higher than Qwen3-32B; in LiveBench (2024-08), this gap widens to 42%. The far-right ColabEval chart shows the new model's advantage narrowing to 15%, but it still firmly holds the top spot.

The line graph is even more intriguing. When context length expands from 4k to 64k, Ring-flash-linear-2.0's decoding throughput drops by only 18%, while Qwen3-Next-80BAB3B plummets by 63%. The architecture diagram highlights key components: linear layers, MoE, routers, and grouped query attention—a classic hybrid design.

The Ant Ling team claims this model is 2x faster than MoE models of similar scale and 10x faster than 32B models. Some commenters question the test sample selection, while others mock the chart color schemes as "rainbow vomit." But when SEED-OSS 36B users announce plans to switch, it's clear the performance metrics are compelling.

The model card indicates it's already on HuggingFace but omits specific RL enhancement methods. The architecture suggests computational complexity is reduced via linear attention, paired with dynamic routing to balance inference quality. Comments demanding "when will we get a 1T model?" can rest—the current version has just 16B parameters, emphasizing efficiency over scale.