English(EN) 10 Models Tested: From 81.6% to 10%. The Free Tier is a Full-On Gamble.

AI模型测试：Grok 4.3领先，免费版差异巨大

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-26 22:42

最近对十个AI模型进行的编码任务测试揭示了显著的性能差异，尤其是在免费版本中。Grok 4.3以81.6%的成功率成为表现最佳的模型，而Perceptron Mk1以极低的成本提供了近80%的卓越价值。在免费模型中，Owl Alpha以76.7%的得分脱颖而出，且没有出现硬性失败，尽管延迟是一个问题。其他模型如GPT Chat Latest和Mistral Medium 3.5则表现不一，前者价格最高，后者则出现超时。 AI

影响突显了AI模型之间显著的成本和性能差异，尤其是在免费版本中，这影响了开发者的选择和工具的选型。

排序理由文章展示了对多个AI模型在编码任务上的基准测试结果，比较了它们的性能和成本。[lever_c_demoted from research: ic=1 ai=1.0]

在 dev.to — LLM tag 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 文章展示了对多个AI模型在编码任务上的基准测试结果，比较了它们的性能和成本。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: model release, product; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 132 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

报道来源 [1]

dev.to — LLM tag TIER_1 English(EN) · Vilius · 2026-05-26 22:42

10款模型测试：从81.6%到10%。免费层是一场豪赌。

By Vilius Vystartas | May 2026 I tested another 10 models across the same 10 agent coding tasks. Four of them were free-tier models — and the range was absurd: Owl Alpha scored 76.7% with zero hard fails, Laguna M.1 scored 10% and produced garbage on 9 out of 1…

本文如何被排名

报道来源 [1]

10款模型测试：从81.6%到10%。免费层是一场豪赌。

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