Traders see near-even odds across frontrunners for the top LiveBench coding score by end of October because Anthropic’s Claude Fable 5 and OpenAI’s GPT-5.6 variants currently post the strongest verified results on the benchmark’s refreshed coding tasks, with DeepSeek, Moonshot’s Kimi, and Google Gemini models close behind on agentic and SWE-bench-style evaluations. Recent frontier releases have narrowed gaps, while open-weight and Chinese labs continue closing performance deltas through scale and specialization. Key swing factors include any new model drops, fine-tunes, or context-window improvements before the October snapshot, plus LiveBench’s contamination-free refresh cycle that can reorder rankings quickly. Market-implied probabilities around 20-27% for the named leaders capture this fluid, multi-player contest where small capability edges or timing surprises could shift the outcome.
基于Polymarket数据的AI实验性摘要。这不是交易建议,也不影响该市场的结算方式。 · 更新于亚马逊 31%
Z.ai 30%
Anthropic 27%
OpenAI 26%

亚马逊
26%

Z.ai
27%

Anthropic
22%

OpenAI
26%

StepFun
13%

字节跳动
20%

SpaceXAI
25%

美团
20%

Meta
13%

Moonshot
20%

谷歌
17%

DeepSeek
16%

Nvidia
14%

腾讯
14%

百度
12%

Mistral
9%

小米
9%

Thinky
8%

阿里巴巴
7%

MiniMax
5%

微软
4%
亚马逊 31%
Z.ai 30%
Anthropic 27%
OpenAI 26%

亚马逊
26%

Z.ai
27%

Anthropic
22%

OpenAI
26%

StepFun
13%

字节跳动
20%

SpaceXAI
25%

美团
20%

Meta
13%

Moonshot
20%

谷歌
17%

DeepSeek
16%

Nvidia
14%

腾讯
14%

百度
12%

Mistral
9%

小米
9%

Thinky
8%

阿里巴巴
7%

MiniMax
5%

微软
4%
Results from the “Coding” column of the leaderboard at https://livebench.ai/#/?cats=Coding, with the latest available LiveBench release selected and the category set to “Coding,” will be used to resolve this market.
Models will be ranked according to the specified score, with higher scores ranked ahead of lower scores. If two or more models have exactly the same score as displayed on the leaderboard, the model with the lower listed "cost per successful task" will be ranked ahead. If a tie still remains, alphabetical order of company names as listed in this market group will be used as a final tiebreaker (e.g., if the two models are tied by exact score and cost per successful task, “Google” would be ranked ahead of “SpaceXAI”). This market will resolve based on the company that occupies first place under this ranking.
The resolution source for this market is the LiveBench leaderboard. If this resolution source is unavailable at check time, this market will remain open until the leaderboard comes back online and will resolve based on the first check after it becomes available. If it becomes permanently unavailable, this market will resolve to “Other.”
市场开放时间: Aug 12, 2026, 8:05 PM ET
Resolver
0x69c47De9D...Results from the “Coding” column of the leaderboard at https://livebench.ai/#/?cats=Coding, with the latest available LiveBench release selected and the category set to “Coding,” will be used to resolve this market.
Models will be ranked according to the specified score, with higher scores ranked ahead of lower scores. If two or more models have exactly the same score as displayed on the leaderboard, the model with the lower listed "cost per successful task" will be ranked ahead. If a tie still remains, alphabetical order of company names as listed in this market group will be used as a final tiebreaker (e.g., if the two models are tied by exact score and cost per successful task, “Google” would be ranked ahead of “SpaceXAI”). This market will resolve based on the company that occupies first place under this ranking.
The resolution source for this market is the LiveBench leaderboard. If this resolution source is unavailable at check time, this market will remain open until the leaderboard comes back online and will resolve based on the first check after it becomes available. If it becomes permanently unavailable, this market will resolve to “Other.”
Resolver
0x69c47De9D...Traders see near-even odds across frontrunners for the top LiveBench coding score by end of October because Anthropic’s Claude Fable 5 and OpenAI’s GPT-5.6 variants currently post the strongest verified results on the benchmark’s refreshed coding tasks, with DeepSeek, Moonshot’s Kimi, and Google Gemini models close behind on agentic and SWE-bench-style evaluations. Recent frontier releases have narrowed gaps, while open-weight and Chinese labs continue closing performance deltas through scale and specialization. Key swing factors include any new model drops, fine-tunes, or context-window improvements before the October snapshot, plus LiveBench’s contamination-free refresh cycle that can reorder rankings quickly. Market-implied probabilities around 20-27% for the named leaders capture this fluid, multi-player contest where small capability edges or timing surprises could shift the outcome.
基于Polymarket数据的AI实验性摘要。这不是交易建议,也不影响该市场的结算方式。 · 更新于
警惕外部链接哦。
警惕外部链接哦。
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