Endpoint Accuracy Index v1.1发布
Artificial Analysis发布Endpoint Accuracy Index v1.1，将覆盖范围扩展至六个开放权重模型，包括GLM-5.3 Flash和Muse Spark Open。该指数使用工具调用、科学推理和长上下文召回，将服务商端点与自托管参考部署进行比较；更新后的AA-LCR v1.1采用25次重复测试，将平均95%置信区间从±4.7收窄至±2.6个百分点。
这些测量覆盖100多个无服务器端点，为开发者比较服务商部署的准确率提供依据，并显示输出上限、推理减少和工具参数处理会影响实际表现。
Launching Artificial Analysis Endpoint Accuracy Index v1.1: a minor version upgrade alongside expanded coverage for GLM-5.3 Flash and Muse Spark Open. Across the 100+ serverless endpoints we benchmarked, nearly 1 in 4 score below the reference
Providers trade off accuracy to optimize for speed and cost. They quantize weights, write custom kernels, tune their inference stacks and cap output lengths, and sometimes they simply ship bugs. The Endpoint Accuracy Index measures how much of a model’s accuracy each endpoint preserves, so developers can pick providers on accuracy, not just price and speed.
We score each endpoint as a percentage of our self-hosted reference deployment of the official weights, across tool calling (BFCL-500), scientific reasoning (HLE-250) and long context recall (AA-LCR-25), equally weighted.
What’s changed:
➤ Upgraded AA-LCR-25 to AA-LCR v1.1 and increased repeats from 10 to 25: AA-LCR v1.1 corrects errors and ambiguities in answer keys and moves grading to GPT-5.6 Luna (medium). Running each question 25 times, up from 10, reduces the average 95% confidence interval from ±4.7 to ±2.6 points
➤ Expanded coverage to six open weights models: We add GLM-5.3 Flash (max) and Meta’s Muse Spark Open (xhigh), alongside DeepSeek V4.1 Flash 1017 (max), GLM-5.3 (max), Kimi K3 (max) and gpt-oss-120b (high)
#Artificial Analysis#Endpoint Accuracy Index v1.1#AA-LCR v1.1#GLM-5.3 Flash#Muse Spark Open#DeepSeek V4.1 Flash 1017#GLM-5.3#Kimi K3#gpt-oss-120b#GPT-5.6 Luna