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Moonshot AI releases Kimi K3.1 Pro, which ranks 8th on Code Arena WebDev with a score of 1692. According to Arena, its score ties with Claude Opus-5 (Max) at 70% lower cost, and it rises 9 places from Kimi K3 (Max)'s 17th place. The model is priced at $2/$10 per million input/output tokens, with a median cost per task of $1.23, 29% lower than Kimi K3, and its weights will be opened on November 4. Kimi officially calls it a 3T-class open-weight model, featuring a 1M token context and a 2.8T total parameter/104B activated parameter MoE multimodal architecture, with hallucination rate reduced by 65%. Testing Catalog reports that Kimi thus returns to the top 4 in the Code Arena WebDev lab rankings, and says that after the open-weight release it will become the top-ranked open-source model on that leaderboard.
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Kimi K3.1 Pro rises to 8th on WebDev Arena; Kimi returns to 4th in the lab rankingsDevelopments
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Oct 24
Testing Catalog
Kimi K3.1 Pro rises to 8th on WebDev Arena; Kimi returns to 4th in the lab rankings
Kimi K3.1 Pro rises to 8th on Code Arena: WebDev with a score of 1692, tying Claude Opus-5 (Max) at 70% lower cost; compared with Kimi K3 (Max)'s 17th place and 1655 points, it improves 9 places and 37 points. Kimi thus returns to the top 4 in the Code Arena: WebDev lab rankings. The model is priced at $2/$10 per million input/output tokens, with a median cost per task of $1.23, 29% lower than Kimi K3; the official announcement says open weights will be released soon, at which point it will become the top-ranked open-source model on the leaderboard, surpassing DeepSeek-v4.1-Flash.
ArenaSelected
Kimi K3.1 Pro released, rises to 8th on Code Arena WebDev
Moonshot AI released Kimi K3.1 Pro, which ranks 8th on Code Arena WebDev with 1,692 points, matching Claude Opus-5 (Max) while costing 70% less, and rising 9 places from Kimi K3 (Max)'s 17th. The model is priced at $2/$10 per million input/output tokens, with a median cost of $1.23 per task, 29% lower than Kimi K3, and its weights will be opened on November 4. Kimi officially calls it a 3T-class open-weight model, featuring a 1M-token context, a 2.8T total parameter/104B active parameter MoE multimodal architecture, and a 65% reduction in hallucination rate.