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    Ling 3.1 Flash scores 41 on the Artificial Analysis Intelligence Index, up from 20

    Artificial Analysis reports gains on agentic benchmarks, though the larger model costs more per token than Ling 3.0 Flash.

    Artificial AnalysisAA
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    TLDR

    Artificial Analysis reports that Ant Group released Ling 3.1 Flash, a reasoning model with a one-million-token context window. On the firm's Intelligence Index v4.3, it scored 41, up from its predecessor's 20. Its Terminal-Bench v4.0 score rose from 0% to 33%, while its AutomationBench-AA score rose from 3% to 62%. Artificial Analysis expects its weights to be released soon.

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    Combined views

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    3 Sources, first seen 1h ago

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    Today's Rank

    #9

    Today's Rank

    #9

    3 Sources

    Artificial Analysis@ArtificialAnlysLing 3.1 Flash makes large gains in intelligence over its predecessor, scoring 41 on the Artificial Analysis Intelligence Index with particular improvement in agentic capabilities @AntLingAGI has released Ling 3.1 Flash, a reasoning model with 560B total parameters, 25B active parameters, and a 1M token context window. It scores 41 on the Artificial Analysis Intelligence Index v4.3, up from 20 from Ling 3.0 Flash, which launched in August. Ling 3.1 Flash is expected to be open weights, with Ant Group releasing the weights soon. Key results: ➤ Ling 3.1 Flash improves agentic capabilities over its predecessor. With a GDPval-AA v2 Elo of 1,622 and an AA-Briefcase Elo of 1,400, the model rivals peer models such as GLM-5.3-Flash, Gemini 3.8 Flash (high) and DeepSeek V4.1 Flash (Max). Ling 3.1 Flash also shows notable gains on AutomationBench-AA (62%) and Terminal-Bench v4.0 (33%). ➤ Ling 3.1 Flash scores +2 on AA-Omniscience, a 22 point improvements from Ling 3.0 Flash (-18). Compared to its predecessor, its accuracy rate rose from 18% to 29% while the hallucination rate fell from 44% to 38% at a similar attempt rate (56% to 58%), demonstrating the gain comes primarily from knowing more, not abstaining more. For comparison, DeepSeek V4.1 Flash (Max) has a hallucination rate of 97%. ➤ Ling 3.1 Flash is a larger model than its predecessor, priced accordingly, but more token efficient. It has 560B total and 25B active parameters, up from 124B and 5.1B for Ling 3.0 Flash, and is priced at $0.30 / $0.90 per 1M input / output tokens, up from $0.075 / $0.22. It used 218M output tokens to run the Intelligence Index, 16% fewer than Ling 3.0 Flash (261M). ➤ Ling 3.1 Flash has a higher cost per task than some peers, but remains in the most attractive quadrant. Ling 3.1 Flash costs $0.99 per task, well above GLM-5.3-Flash ($0.42) and DeepSeek V4.1 Flash (Max, $0.32) but below Gemini 3.8 Flash (High) at $1.24. Additional model details: ➤ Size: 560B total parameters, 25B active ➤ Context window: 1M tokens ➤ Pricing: $0.30 per 1M input tokens and $0.90 per 1M output tokens, with cached input at $0.06 per 1M (80% discount) ➤ Availability: Accessible through Novita AI, with weights coming soon1h

    3 Sources

    Artificial Analysis@ArtificialAnlysLing 3.1 Flash makes large gains in intelligence over its predecessor, scoring 41 on the Artificial Analysis Intelligence Index with particular improvement in agentic capabilities @AntLingAGI has released Ling 3.1 Flash, a reasoning model with 560B total parameters, 25B active parameters, and a 1M token context window. It scores 41 on the Artificial Analysis Intelligence Index v4.3, up from 20 from Ling 3.0 Flash, which launched in August. Ling 3.1 Flash is expected to be open weights, with Ant Group releasing the weights soon. Key results: ➤ Ling 3.1 Flash improves agentic capabilities over its predecessor. With a GDPval-AA v2 Elo of 1,622 and an AA-Briefcase Elo of 1,400, the model rivals peer models such as GLM-5.3-Flash, Gemini 3.8 Flash (high) and DeepSeek V4.1 Flash (Max). Ling 3.1 Flash also shows notable gains on AutomationBench-AA (62%) and Terminal-Bench v4.0 (33%). ➤ Ling 3.1 Flash scores +2 on AA-Omniscience, a 22 point improvements from Ling 3.0 Flash (-18). Compared to its predecessor, its accuracy rate rose from 18% to 29% while the hallucination rate fell from 44% to 38% at a similar attempt rate (56% to 58%), demonstrating the gain comes primarily from knowing more, not abstaining more. For comparison, DeepSeek V4.1 Flash (Max) has a hallucination rate of 97%. ➤ Ling 3.1 Flash is a larger model than its predecessor, priced accordingly, but more token efficient. It has 560B total and 25B active parameters, up from 124B and 5.1B for Ling 3.0 Flash, and is priced at $0.30 / $0.90 per 1M input / output tokens, up from $0.075 / $0.22. It used 218M output tokens to run the Intelligence Index, 16% fewer than Ling 3.0 Flash (261M). ➤ Ling 3.1 Flash has a higher cost per task than some peers, but remains in the most attractive quadrant. Ling 3.1 Flash costs $0.99 per task, well above GLM-5.3-Flash ($0.42) and DeepSeek V4.1 Flash (Max, $0.32) but below Gemini 3.8 Flash (High) at $1.24. Additional model details: ➤ Size: 560B total parameters, 25B active ➤ Context window: 1M tokens ➤ Pricing: $0.30 per 1M input tokens and $0.90 per 1M output tokens, with cached input at $0.06 per 1M (80% discount) ➤ Availability: Accessible through Novita AI, with weights coming soon1h