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    Artificial Analysis says Ant Group’s Ling-3.0-flash-VL scores 25 on its Intelligence Index

    Artificial Analysis says the open-weights reasoning model adds image and video understanding to Ling-3.0-flash and activates 5.5 billion of its 124 billion parameters per token.

    AA
    1 Source, 19d ago, first seen 19d ago

    TLDR

    Artificial Analysis reports that Ant Group has released Ling-3.0-flash-VL, an open-weights reasoning model supporting a 256K-token context window. It reports an Intelligence Index score of 25, compared with 16 for Qwen3.5 122B A10B (Reasoning) and 15 for Mistral Medium 3.5 (high), models with similar total parameter counts.

    The evaluator describes a lower hallucination rate than comparable models but limited factual recall, reporting 14% accuracy and a 22% hallucination rate on AA-Omniscience. Difficult agentic tasks also remain a weakness in its assessment: the model scored 16% on AutomationBench-AA, which tests business-app workflows while respecting guardrails, and 0% on Terminal-Bench v4.0, a harder terminal-use benchmark.

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    1 Source, first seen 19d ago

    Combined views

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    1 Source, first seen 19d ago

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    13 comments
    14 saves
    11 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

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    1 Source

    @ArtificialAnlysLing-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 AutomationBench-AA, which tests completing workflows across business apps while respecting guardrails, and 0% on Terminal-Bench v4.0, a harder terminal-use benchmark. ➤ Ling-3.0-flash-VL is decently verbose with its output. The model averages ~50k output tokens per Intelligence Index task vs. ~30k for Inkling Small (Reasoning), despite the similar overall scores. This will have cost implications for workloads with a high level of reasoning. Additional model details: ➤ Type: Open weights reasoning model. ➤ Size: 124B total parameters, 5.5B active per token (MoE). ➤ Context window: 256K tokens. ➤ Modalities: Text, image, and video input; text output. ➤ API availability: First- and third-party APIs. ➤ License: MIT.

    1 Source

    @ArtificialAnlysLing-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 AutomationBench-AA, which tests completing workflows across business apps while respecting guardrails, and 0% on Terminal-Bench v4.0, a harder terminal-use benchmark. ➤ Ling-3.0-flash-VL is decently verbose with its output. The model averages ~50k output tokens per Intelligence Index task vs. ~30k for Inkling Small (Reasoning), despite the similar overall scores. This will have cost implications for workloads with a high level of reasoning. Additional model details: ➤ Type: Open weights reasoning model. ➤ Size: 124B total parameters, 5.5B active per token (MoE). ➤ Context window: 256K tokens. ➤ Modalities: Text, image, and video input; text output. ➤ API availability: First- and third-party APIs. ➤ License: MIT.