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General Language Model Evaluation on Aggregated 11-benchmark suite Math, Code, IF

74.9Average AccuracyQwen3-30B-A3B
34.96445.33255.766.068May 18, 2026May 19, 2026

Evaluation results

MethodLinks
Qwen3-30B-A3BBase Model=Qwen3-30B-A3B2026.0574.90———
ZEDABase Model=Qwen3-30B-A3B2026.0574.251.2———
ZEDASFTBase Model=Qwen3-30B-A3B2026.0573.351.5———
NETSFT→OPDBase Model=Qwen3-30B-A3B2026.057350———
GLM-4.7-FlashBase Model=GLM-4.7-Flash2026.0572.50———
NETSFTBase Model=Qwen3-30B-A3B2026.0572.350———
ZEDABase Model=GLM-4.7-Flash2026.0571.853———
NETSFT→OPDBase Model=GLM-4.7-Flash2026.0570.950———
ZEDASFTBase Model=GLM-4.7-Flash2026.0570.952.8———
NETSFTBase Model=GLM-4.7-Flash2026.0570.650———
Dynamic SkippingBase Model=Qwen3-30B-A3B2026.0568.143.8———
Dynamic SkippingBase Model=GLM-4.7-Flash2026.0567.837.5———
AdaMoEBase Model=GLM-4.7-Flash2026.0557.147———
AdaMoEBase Model=Qwen3-30B-A3B2026.0554.851.9———
IXTTraining Dataset=Openthinker2026.0550—55.543.141.8
NTPTraining Dataset=Openthinker2026.0549.2—544441.6
IXTTraining Dataset=SFT > 4k, Annotation Process=pairwise ann.2026.0546.1—52.938.834.7
IXTTraining Dataset=SFT > 4k2026.0545.4—52.937.633.4
NTPTraining Dataset=SFT > 4k2026.0543.7—50.637.231.8
IXTTraining Dataset=General SFT2026.0537.7—34.937.244.6
NTPTraining Dataset=General SFT2026.0536.5—34.136.342