内容来源：https://aibusiness.com/agentic-ai/ai2-releases-open-coding-agents

内容总结：

开源AI模型新动向：成本与性能平衡成企业AI应用关键

在生成式AI浪潮中，企业正积极探索既能控制成本又能保障性能的落地场景。近日，知名开源AI模型机构艾伦人工智能研究所（Ai2）发布了一组名为SERA的新型开源编码智能体，旨在帮助企业开发团队基于自身代码库训练更轻量、更经济的专用模型。

SERA智能体可协助完成代码生成、审查、调试及维护等任务，并能与Anthropic的Claude Code等流行模型集成使用。据Ai2介绍，其完整的训练与微调方案成本低于基于Mistral公司Devstral Small 2等开源权重模型的方案。与此同时，Mistral同日也推出了其编码智能体Mistral Vibe 2.0的升级版本。

与IBM的Granite、英伟达的Nemotron等开源模型厂商类似，Ai2一贯公开模型权重与训练数据。开源倡导者认为，这种做法相比OpenAI、谷歌等公司的闭源模型更具透明度，有助于企业更好地理解和控制AI系统。

当前，企业在AI项目中普遍面临成本与性能的平衡难题——尤其是在数据中心建设与运营成本不断攀升的背景下。Futurum Group分析师布拉德利·希明指出，许多公司正采用“任务路由”模式，将不同工作分配给更小型、更专业的模型，以优化资源使用。

Omdia分析师苏建哲进一步解释，SERA采用传统的监督微调方法，而非更复杂的强化学习，这有助于减少计算资源与令牌消耗，对IT预算有限的组织具有重要意义。

除了SERA，Ai2还发布了与之配套的8B和32B参数模型、训练方案及合成数据生成方法，延续了开源厂商公开训练流程的趋势。希明认为，这一趋势既源于成本优化需求，也体现了企业对数据主权与控制权的重视。

Ai2长期建立的伦理与透明声誉，为其编码智能体赢得了公共部门及部分非政府组织的关注。苏建哲表示：“对于将透明度视为AI部署前提的组织而言，Ai2的品牌背书具有重要价值。”

尽管在成本敏感的企业开发者与研究机构中具有吸引力，Ai2仍需面对市场采纳的挑战——预算更充裕的客户可能选择其他服务商。如何在开源生态中持续提供差异化价值，将成为其发展的关键。

中文翻译：

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此次发布揭示了企业如何在成本与性能间寻求平衡，并凸显开源模型市场的上升趋势。

开源AI模型提供商艾伦人工智能研究所于周二推出全新开源编程智能体系列，支持企业开发团队基于自有代码库训练更轻量的开源模型。作为开源生成式AI模型领域最知名的开发者之一，该研究所将首批编程智能体统称为SERA（软验证高效存储库智能体）。SERA可协助开发者完成代码生成、审查、调试、维护及解释等工作。研究所表示，小型开发团队可直接在Anthropic公司广受欢迎的Claude Code模型中微调并运行这些智能体，用于调试、重构与维护任务。

据研究所介绍，完整训练与微调方案的成本低于基于法国AI供应商Mistral开源权重模型Devstral Small 2的方案。同日，Mistral也发布了由其Devstral 2驱动的升级版编程智能体Mistral Vibe 2.0。与IBM（Granite模型系列）和英伟达（Nemotron模型系列）等开源模型供应商类似，研究所通常公开模型权重与训练数据。开源倡导者认为，相较于OpenAI和谷歌的专有模型，这种方式为生成式AI提供了更高透明度。

此次发布似乎回应了企业在AI项目中平衡成本与性能的普遍困境——尤其是在AI数据中心建设与运营成本持续攀升的背景下。
"若能找到各项要素完美契合的平衡点，便能占据优势，"Futurum Group分析师布拉德利·希明指出，"但即便在单一项目中实现这一点也极为困难。"他补充说，在智能体流程中，某些任务复杂度更高，可能需要更轻量的工具和较低的专业门槛，因此许多企业正采用任务分流模式，将工作分配给更小型的模型。

Omdia（Informa TechTarget旗下机构）分析师苏连杰表示，研究所帮助企业降低成本的方法之一，是SERA采用传统监督式微调而非更复杂的强化学习，这对部分供应商而言意义重大。"这意味着用更少的令牌量、消耗更少资源仍能达成相同效果，这对IT预算有限的组织至关重要。"

除SERA外，研究所还发布了基于该框架开发的80亿与320亿参数模型、训练方案及新型合成数据生成方法，企业可借此为自有代码库定制智能体。开源供应商发布训练方案已成为行业趋势。
希明认为这一趋势的增强源于"优化支出的需求，以及对数据主权与控制的追求——企业不希望依赖可能违反内部或外部规定的托管服务"。

研究所的过往声誉也使其成为企业考虑此类开源编程智能体的可靠选择。
"研究所始终以高度道德标准和操作透明度著称，"苏连杰强调，"将这样的品牌信誉赋予编程智能体至关重要，特别是对那些将透明度视为AI部署前提的组织。"他补充说，这套新型编程智能体应能吸引公共部门或部分非政府组织，这些机构因其社会使命而格外关注AI模型的可解释性。

研究所面临的挑战在于市场接受度。尽管其编程智能体适合预算有限的企业开发者或研究机构，但资金更充裕的组织可能会选择其他供应商。

英文来源：

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The release shows how enterprises need to balance cost with performance and highlights a rising trend in the open source model market.
Open source AI model provider Allen Institute for AI on Tuesday launched a new family of Open Coding Agents that enable enterprise developer teams to train smaller, open models on their organization's codebase.
The Allen Institute (Ai2) is one of the best-known developers of open source generative AI models. Ai2's first set of coding agents, grouped under the new agent family, is SERA (Soft-Verified Efficient Repository Agents). SERA agents help developers with code generation, code review, debugging, maintenance and code explanation. Small developer teams can fine-tune agents and run them directly in the popular Claude Code model from Anthropic for debugging, refactoring, and maintenance, Ai2 said.
According to Ai2, a complete training and fine-tuning recipe costs less to reproduce than one based on Devstral Small 2, an open-weight model from French AI vendor Mistral. Also on Tuesday, Mistral released Mistral Vibe 2.0, an upgrade of its coding agent powered by Devstral 2.
Along with other open model vendors such as IBM (with its Granite models) and Nvidia (with its Nemotron models), Ai2 generally releases model weights and training data, an approach open source advocates say provides more transparency into generative AI than proprietary models such as those from OpenAI and Google.
The release appears to address the balance enterprises struggle with in optimizing for cost and performance in their AI projects, particularly as other areas of AI technology, such as the cost of building and powering AI data centers, continue to rise.
"If you can find that sweet spot where everything is aligned, then you're golden," said Bradley Shimmin, an analyst at Futurum Group. "But getting that is very difficult even within a single project." He added that, with agentic processes, some tasks are more complex than others and could require smaller tools and less expertise. Therefore, many companies are adopting a routing model that delegates tasks to smaller models.
One method Ai2 uses to help enterprises cut costs is that SERA employs traditional supervised fine-tuning compared to the more complex reinforcement learning, which could make a difference for some vendors, said Lian Jye Su, an analyst at Omdia, a division of Informa TechTarget.
"That's a huge component of using lesser tokens, consuming lesser resources and still being able to achieve the same result," he said. "That is something that does matter a lot to organizations that have a smaller IT budget."
In addition to SERA, Ai2 released 8B and 32B-parameter models developed with SERA, training recipes, and new synthetic data generation methods that enterprises can use to customize agents for their own codebases. The release of training recipes follows a trend among open source vendors.
The trend is growing because of "the need to optimize spend, but also with the need or desire to have some sort of data sovereignty and control and not relying on hosted services that might run afoul of either internal or external requirements or mandates," Shimmin said.
Ai2's history also makes it a trusted source for enterprises considering open coding agents like these.
"Ai2 has the reputation of being very ethical, being very transparent with what they do," Su said. "Having that brand name attached to this coding agent matters a lot, especially for organizations that really pursue transparency as the prerequisite for all their AI deployments."
He added that this set of new coding agents should appeal to organizations in the public sector or certain NGOs that are concerned about visibility into AI models because of their social missions
One challenge for Ai2 is adoption. While its coding agent might serve enterprise developers or research organizations with cost constraints, those without bigger budgets might opt for a different provider.