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    Ant Group releases Ling-3.0-flash-Fin, an open-weights AI model for finance

    Artificial Analysis says Ling-3.0-flash-Fin matches MiniMax-M2.7’s Intelligence Index score of 23 with roughly half the active parameters.

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    TLDR

    Artificial Analysis reports that Ant Group’s Ling-3.0-flash-Fin is a text-only, open-weights model built on Ling-3.0-flash.

    Its testing puts Fin at 23 on the Intelligence Index, matching MiniMax-M2.7 while activating 5.1 billion parameters per token versus 10 billion. Fin also matches Ling-3.0-flash-VL at 24 on the Finance & Accounting Index.

    Artificial Analysis found higher business knowledge accuracy for Fin than VL (17% vs. 11%), but also higher business knowledge hallucination (33% vs. 19%).

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    1 Source, first seen 14d ago

    Combined views

    17.5K

    1 Source, first seen 14d ago

    131 likes
    14d ago
    first seen 14d ago
    131 likes
    19 comments
    17 saves
    8 reposts

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    19 comments
    17 saves
    8 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

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    1 Source

    @ArtificialAnlysLing-3.0-flash-Fin, Ant Group’s new finance-focused open weights model, scores 23 on the Artificial Analysis Intelligence Index and 24 on the Finance & Accounting Index, and is on the Intelligence vs. Active Parameter Pareto Frontier @AntLingAGI has released Ling-3.0-flash-Fin, a finance-focused model built on Ling-3.0-flash. Ant Group announced that it developed the model with financial institutions and industry experts to support financial research, including checking sources, building valuation spreadsheets and writing reports. This text-only model comes after their release of their image and video input-capable model Ling-3.0-flash-VL, which scored 25 on the Intelligence Index. Key results: ➤ Ling-3.0-flash-Fin matches MiniMax-M2.7’s Intelligence Index score with roughly half the active parameters. Both score 23, while Flash-Fin activates 5.1B parameters per token compared with MiniMax-M2.7’s 10B. ➤ Ling-3.0-flash-Fin matches Ling-3.0-flash-VL at 24 on the Artificial Analysis Finance & Accounting Index. Fin has higher business knowledge accuracy than VL (17% vs. 11%), but also higher business knowledge hallucination (33% vs. 19%) ➤ Ling-3.0-flash-Fin scores slightly below Ling-3.0-flash-VL on professional knowledge work. It scores 1171 Elo on GDPval-AA v2 and 967 on AA-Briefcase, compared with 1225 and 986 respectively for Ling-3.0-flash-VL. Both benchmarks test agents on professional tasks such as producing documents and spreadsheets. ➤ Difficult agentic tasks remain a challenge for Ling-3.0-flash-Fin. It scores 7% on AutomationBench-AA, which tests workflows across business apps while respecting guardrails, compared with 16% for the flash-VL model. Both models score 0% on Terminal-Bench v4.0, which tests difficult terminal-use tasks. ➤ Ling-3.0-flash-Fin uses more output tokens than the flash-VL model and MiniMax-M2.7. It averages ~67k output tokens per Intelligence Index task, about 34% more than VL (~50k) and 3.2x MiniMax-M2.7 (~21k). Additional model details: ➤ Type: Open weights reasoning model. ➤ Size: 124B total parameters, 5.1B active per token (MoE). ➤ Context window: 256K tokens. ➤ Modalities: Text input and output. ➤ API availability: Available through @OpenRouter, including a rate-limited free endpoint. ➤ License: MIT.

    1 Source

    @ArtificialAnlysLing-3.0-flash-Fin, Ant Group’s new finance-focused open weights model, scores 23 on the Artificial Analysis Intelligence Index and 24 on the Finance & Accounting Index, and is on the Intelligence vs. Active Parameter Pareto Frontier @AntLingAGI has released Ling-3.0-flash-Fin, a finance-focused model built on Ling-3.0-flash. Ant Group announced that it developed the model with financial institutions and industry experts to support financial research, including checking sources, building valuation spreadsheets and writing reports. This text-only model comes after their release of their image and video input-capable model Ling-3.0-flash-VL, which scored 25 on the Intelligence Index. Key results: ➤ Ling-3.0-flash-Fin matches MiniMax-M2.7’s Intelligence Index score with roughly half the active parameters. Both score 23, while Flash-Fin activates 5.1B parameters per token compared with MiniMax-M2.7’s 10B. ➤ Ling-3.0-flash-Fin matches Ling-3.0-flash-VL at 24 on the Artificial Analysis Finance & Accounting Index. Fin has higher business knowledge accuracy than VL (17% vs. 11%), but also higher business knowledge hallucination (33% vs. 19%) ➤ Ling-3.0-flash-Fin scores slightly below Ling-3.0-flash-VL on professional knowledge work. It scores 1171 Elo on GDPval-AA v2 and 967 on AA-Briefcase, compared with 1225 and 986 respectively for Ling-3.0-flash-VL. Both benchmarks test agents on professional tasks such as producing documents and spreadsheets. ➤ Difficult agentic tasks remain a challenge for Ling-3.0-flash-Fin. It scores 7% on AutomationBench-AA, which tests workflows across business apps while respecting guardrails, compared with 16% for the flash-VL model. Both models score 0% on Terminal-Bench v4.0, which tests difficult terminal-use tasks. ➤ Ling-3.0-flash-Fin uses more output tokens than the flash-VL model and MiniMax-M2.7. It averages ~67k output tokens per Intelligence Index task, about 34% more than VL (~50k) and 3.2x MiniMax-M2.7 (~21k). Additional model details: ➤ Type: Open weights reasoning model. ➤ Size: 124B total parameters, 5.1B active per token (MoE). ➤ Context window: 256K tokens. ➤ Modalities: Text input and output. ➤ API availability: Available through @OpenRouter, including a rate-limited free endpoint. ➤ License: MIT.