想知道开源模型在金融领域到底行不行？这份基准测试用数据说话，Kimi K2.6和GLM 5的表现可能会让你意外。
更新后的Financial Touchstone基准包含2967个问答对，覆盖495份国际年报。测试了20个模型，包括GLM 4.7、GLM 5、Kimi K2.6、DeepSeek V3.2等开源模型及阿里Qwen3-Max。Claude Opus 4.6准确率最高达88.4%，Gemini 2.5 Pro幻觉率最低为0.08%。Kimi K2.6准确率排名第三，GLM 5和Mistral 3分列第四、第五，挑战了推理架构或专有权重的必要性。信息检索是主要瓶颈，占失败原因的48.9%。
Can Open-Weight Models Compete on Financial Text Comprehension?
Open-weight language models from Chinese AI labs caught up on benchmarks relative to proprietary frontier models in recent months. Yet their reliability on real-world financial tasks remains largely untested. We updated the Financial Touchstone benchmark, which now has 2,967 question context-answer triplets across 495 international annual reports. We also apply a new set of models on the benchmark, expanding coverage from eleven to twenty models across ten providers, including recent open-weight models such as GLM 4.7, GLM 5, Kimi K2.6, and DeepSeek V3.2, as well as Alibaba's proprietary flagship Qwen3-Max. Anthropic's Claude Opus 4.6 achieves the highest accuracy (88.4%), while Google's Gemini 2.5 Pro maintains the lowest hallucination rate (0.08%). Notably, the open-weight Kimi K2.6 ranks third in accuracy, and the non-reasoning models GLM 5 and Mistral 3 rank fourth and fifth, challenging the assumption that reasoning architectures or proprietary weights are a prerequisite for strong financial comprehension. Information retrieval remains the primary bottleneck, accounting for 48.9% of all failures. We also document a new finding: geopolitical content filters in Chinese models refuse legitimate financial questions (0.08% of attempts), sometimes without clear reason, and the refusal behavior depends on the access route as much as on the model. The complete dataset and evaluation framework are publicly available.