Ant's Ling-3.0-flash-VL Scores 25 on AA Index With Just 5.5B Active Params
ArtificialAnlys · x · 2026-09-12
Ant Group released Ling-3.0-flash-VL, an open-weights (MIT) reasoning model adding image/video understanding to Ling-3.0-flash. Artificial Analysis findings:
- MoE: 124B total, 5.5B active per token, 256K context, text/image/video input
- Scores 25 on the AA Intelligence Index, on the Intelligence vs. Active Params Pareto frontier; Qwen3.5 122B A10B scores 16, Mistral Medium 3.5 15
- Low hallucination: 14% AA-Omniscience accuracy, 22% hallucination rate (Inkling Small: 33% accuracy but 63% hallucination); factual recall is weak
- Weak on hard agentic tasks: 16% AutomationBench-AA, 0% Terminal-Bench v4.0
- Verbose: 50k output tokens per task vs 30k for Inkling Small, with cost implications
Related event: Ant's Ling-3.0-flash-VL Hits Pareto Frontier with Just 5.5B Active Params(2 posts)→
More from Models
- DeepSeek's edge: each model iteration targets the last one's biggest bottleneck — xennygrimmato_ · 2026-09-12
- RL on just 1,700 tasks lifts Kimi K2.7 across five coding benchmarks — echen · 2026-09-12
- User vibes: fable 5.1 'feels alive' while astra feels like a soulless ultrabot — rudrank · 2026-09-12
- Redditor claims bug allows unlimited Astra xhigh usage without touching quotas — Intelligent-Dance361 · 2026-09-12
- DeepSeek v4.1-Flash multimodal model lands on Modal with 16B/8B asymmetric activation — charles_irl · 2026-09-12
- Devin Fusion scores 61.7 on Coding Agent Index, nearly matching Claude Code at 36% less cost — ArtificialAnlys · 2026-09-12