Writer 推出新 AI 模型和升级版 Harness，以控制 Token 成本

在整个 AI 行业中，用户正越来越清楚地意识到，其部署成本究竟有多高——并产生了降低成本的新紧迫感。但尽管开源模型的单 Token 成本显著更低，为特定任务找到合适的模型仍然并不容易。

周四，面向营销人员提供 AI 工具和 Agent 的 Writer 发布了一款名为 Palmyra X6 的新旗舰模型，旨在为用户解决这一问题。该模型是在 Z.ai 开源模型 GLM-5.2 的基础上进行后训练改造而成。Writer 表示，这一新系统应能以更低的价格提供可直接部署的能力。该公司估计，新模型结合其 Harness 基础设施的调整后，可将客户处理基础任务的成本最高降低 50%。

除新模型外，该公司还对其标准 Agentic Harness 发布了重要升级。这两项功能将从周四起向 Writer 客户开放。

“我认为，企业已经彻底厌倦了追逐下一个基准测试成绩，”CEO May Habib 对 TechCrunch 表示。“他们希望成本趋于平稳，而似乎没有人能够做到这一点。”

这一新方法尤其强调更快速地执行复杂、多步骤任务，并减少 Token 消耗。Writer 认为，Harness 优化是实现这一目标的关键杠杆。

Writer 研究人员最近发表的一篇论文 为这一方法提供了支持。该研究测试了多种不同模型中 Harness 效率的微小调整。研究发现，在许多情况下，相比模型选择，调整 Harness 是更可靠的降本方法；在其测试中，成本平均下降了 40%。

“Harness 是唯一一个其效率能够乘数式影响组织当前及未来所运行的每一个模型的组件，”研究人员写道。

对于 Writer 的客户而言，使用体验仍然与具体模型无关：Palmyra X6 将与其他 Writer 模型并列，也可与通过 Azure 或 Amazon Bedrock 导入的外部模型共同使用。但 Habib 也认为，降本压力正在推动市场对大型 AI 实验室产生更广泛的不信任，因为这些实验室在财务上有动力推动 Token 使用量上升。

“对客户而言，这里的成本爆炸前所未有；CIO 放弃这些实验室的程度也是如此，”Habib 对 TechCrunch 表示，并补充说，AI 实验室“并不真正深入理解如何帮助企业从 AI 中获益”。

Across the AI industry, users are becoming more conscious of just how expensive their deployments can be —and feeling a new urgency to cut costs. But while open source models offer significantly lower per-token costs, it can be difficult to find the right model for a given job.

On Thursday, Writer, which offers AI tools and agents for marketers, launched a new flagship model called Palmyra X6, aimed at solving that problem for its users. Built as a post-training variation on Z.ai’s open source model GLM-5.2, Writer says the new system should provide deployment-ready capabilities at a much lower price. The company estimates the new model, combined with changes to the companies harness infrastructure, will cut costs for its customers by as much as 50% for basic tasks.

Together with the new model, the company also released significant upgrades to its standard agentic harness. Both features will be available to Writer clients starting Thursday.

“I think the enterprise is absolutely sick of chasing the next benchmark,” CEO May Habib told TechCrunch. “They want flattening cost, and it seems like nobody can deliver that.”

The new approach puts particular emphasis on complex, multi-step tasks, executed faster and with fewer tokens. And Writer sees harness optimization as a crucial lever toward making that happen.

A recent paper from Writer researchers lends credence to this approach, testing small changes in harness efficiency across multiple different models. The research found that, in many cases, changes in the harness were a more reliable way to reduce costs than model choice, with costs falling an average of 40% across their testing.

“The harness is the one component whose efficiency multiplies across every model an organization runs—present and future,” the researchers wrote.

For Writer’s clients, the experience is still model-agnostic: Palmyra X6 will sit alongside other Writer models or outside models imported through Azure or Amazon Bedrock. But Habib also sees the push to cut costs as driving a broader distrust toward major AI labs, which have a financial incentive to drive up token use.

“The cost explosion here is just unprecedented for customers, and so is the degree to which CIOs are giving up on the labs,” Habib told TechCrunch, adding that the AI labs “don’t deeply understand right how to help an enterprise get benefit from AI.”