Ling-3.0-flash-VL, Ant Group’s new flash tier open weights model, scores 25 on the Artificial Analysis Intelligence Index. At 124B total parameters 5.5B active parameters, it sits on the Intelligence vs. Active Parameter Pareto Frontier
@AntLingAGI has released Ling-3.0-flash-VL, an open weights reasoning model that adds image and video understanding to Ling-3.0-flash. Its mixture-of-experts architecture activates 5.5B of its 124B parameters per token, with support for a 256K token context window.
Key results:
➤ Ling-3.0-flash-VL sits on the Pareto Frontier for Intelligence vs. Active Parameters, scoring 25 with 5.5B active parameters. Among models with a similar total size, Qwen3.5 122B A10B (Reasoning) scores 16 and Mistral Medium 3.5 (high) scores 15.
➤ Ling-3.0-flash-VL features lower hallucination rate than comparable models, but with limited factual recall. Ling-3.0-flash-VL scores 14% on AA-Omniscience Accuracy and 22% on Hallucination Rate. Inkling Small answers more questions correctly at 33% Accuracy, but has a much higher Hallucination Rate at 63%.
➤ There remains room for improvement for difficult agentic tasks for Ling-3.0-flash-VL. The model scores 16% on…
LLM — A large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.
Why it matters
Ling-3.0-flash-VL gives a strong intelligence score for a 5.5B-active-parameter open vision-LLMA large language model — the neural network behind tools like Claude and ChatGPT, trained on huge amounts of text to predict what comes next.Full definition →, but fails hard agentic terminal tasks, useful data when picking efficient open models.
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