Retell 使用 OpenAI 模型来助力 100M 月调用
GPT‐6 阿斯特拉超过 Retell的配音agent 基准测试，而Codex则帮助它达到每个雇员1.6M的年收入运行率。
100M+
每月接听电话
80%+
客户解决率
1万亿
消耗的 OpenAI 令牌
$1.6M
每名雇员的年收入率
Retell AI(在新窗口打开)是一个用于联系中心操作的语音AI平台，帮助企业建设agents，能够回答问题，采取行动，并在需要时将呼叫者与个人连接。 其平台目前服务于4000多家企业，每月的呼叫量从2026年初的3000万增长到9月的1亿多。
由于雷特尔为更多工作流程复杂的企业服务，它使用OpenAI模型保持调用质量和可靠性高。 今天，OpenAI模型对Retell80%的呼叫产生响应，客户实现解析率超过80%. Retell还超过了OpenAI模型消费的1万亿令牌，反映了这种采用的规模。
Retell的工程师在整个开发过程中都使用Codex，从研究不熟悉的代码到构建特性和调试问题，以及GPT‐6 Astra测试前端接口。
客户选择售货商时，OpenAI不断获得自己的位置。”
企业之声agents需要遵循公司政策，并保持其分配的角色，即使随着对话的改变。 Retell构建了它的vCX 基准测试，以利用从真实生产情景中提取出来的困难的多回合对话来比较数十种模型。
它测试模型遵循指示的好坏程度，坚持以它们得到的信息为基础，调用正确的工具，并使用自然对话语言。 在Retell的2026年10月13日的基准测试中，GPT‐6 Astra以89.7%的成绩排名第一。
Retell还分享了在GPT‐6的发布前进行的评价，该评价显示GPT‐5.5在非卤化率(88.0%)和工具调用正确性(88.3%)两个模型中排名最高，推理能力强，在这两种措施上都表现优于Gemini-3.1/Pro和Claude‐4.6。 也导致了非卤化率和推理。
选择OpenAI模型的Retell 称新客户继续使用。 客户还可以通过实时API访问OpenAI实时模型，OpenAI文本转语音作为语音引擎。 Retell使新的OpenAI模型在发布的当天可用，并在模型改进时重新运行它的基准测试。
联系中心团队使用Retell来定义他们的agents的商业背景，目标和规则。 开发者可以进一步配置工作流程和集成。
在电话中，Retell 的拉带运行音频模型，跟踪对话，OpenAI型号，包括GPT‐6.1 Sol,GPT‐6 Astra，和GPT‐5.5，使用客户的指示和谈话上下文生成响应和请求工具呼叫。 Retell通过与客户系统的整合执行这些电话，并可将结果写回客户记录。
当需要人类判断时，语音agent用上下文向代表作简报，回答他们的后续问题，并在连接客户之前确认他们已经准备好。 在打完电话后，雷特尔使用GPT‐5.6 Terra分析记录稿并提取结果和其他数据。
采用这种方法，雷特尔报告客户分辨率超过80%. 在Anker电子公司，agents在美国和英国处理Tier‐1售后支持，包括订单跟踪、保修检查和故障排除。 Anker报告说，破案率为80.4%，净促进者得分为63。 其团队也以称 agents的基调，节奏，温暖达到他们期望的人类agent的标准。
在Pine Park Health, Retell agents处理预约提醒和主动安排，并将结果记录在病人的医疗图中。 小组恢复了相当于2.1个全职角色的医疗助理时间，并将其转用于病人护理。 它的日程安排agent在到达患者时实现了55.7%的预订率。
Retell 平台的日间流量可达到一夜之间低点的50倍以上。 为了帮助即使在峰值需求时也能保持呼叫响应，雷特尔使用专用服务器与OpenAI，共享于它的客户。 在雷特尔的2026年10月13日的基准测试，GPT‐6 Astra和GPT‐6中。 索尔每个记录的中位数第一判决间隔为883毫秒，其中GPT‐5.6卢纳以497毫秒的成绩最高。
OpenAI的区域API端点允许开发者将交通路线通向特定地理区域，这有助于Retell缩短网络旅行时间，满足客户在保健和金融服务等行业的数据居住要求。
Retell还将冗余建设到服务的每一层。 它运行自己的电话堆，第二堆准备接管，还有语音和模型服务的备份，在服务失败时帮助保持电话运行。
随着雷特尔的企业成长带来对定制工作流程和集成的更多请求，其工程师往往需要在不熟悉的代码库部分工作。 在写入代码之前，他们给Codex一个高级要求。 Codex调查代码，对照实际数据加以检查，并解释需要改变什么。 一位工程师称 Codex所做的这项研究比他做得更好，更快。
在执行过程中，工程师们说，Codex常常捕捉到他们没有考虑过的细节。 对于调试，Codex会与代码交叉引用日志和数据库记录，以调查正在发生的事情。 Retell的工程师在与基于OpenAI模型的客户合作时也使用Codex来加速任务，例如编写指导模型如何在客户应用中响应的指示。
为了测试其网页界面的变化，雷特尔依赖于GPT‐6 Astra的计算机使用。 当一个工程师打开前端拉力请求时，GPT‐6 Astra打开接口，点击通过它，测试流量，试图打破它们，并报告它发现的问题。 Retell 称早期的模型无法可靠地完成这项工作。
Retell继续雇佣员工，Retell报告每名雇员每年收入为160万美元。
“通过使用Codex，工程队有一个虚拟的队友，他们可以在任何困难的任务上与他们配对。”
每个企业都知道它想怎么服务它的客户。 Retell的目标是让最接近该作品的人们直接运用他们的专业知识，使用高级语音AI和一个让他们建立，测试，改进自己的agents的平台。 其次，Retell计划扩大质量保证和模拟，支持更多的语言，并开发知识库自动学习。
随着OpenAI型号的不断推进，雷特尔期望其客户以自己的条件向数百万的呼叫者提供世界级服务。
" 没有人比公司本身更了解公司的客户。 我们赋予他们根据这种专门知识采取行动的权力，直接改进AI agents. 他们的目标是建立一个内部部署的工程师团队，这些工程师拥有和经营全部资产，“称 Bing。
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Retell uses OpenAI models to help power 100M monthly calls
GPT‑6 Astra tops Retell’s voice agent benchmark, while Codex helps it reach a $1.6M annual revenue run rate per employee.
100M+
Monthly calls handled
80%+
Customer resolution rates
1 trillion
OpenAI tokens consumed
$1.6M
Annual revenue run rate per employee
Retell AI(opens in a new window) is a voice AI platform for contact center operations, helping businesses build agents that can answer questions, take action, and connect a caller with a person when needed. Its platform now serves more than 4,000 businesses, with monthly call volume growing from 30 million in early 2026 to more than 100 million by September.
As Retell serves more enterprises with complex workflows, it uses OpenAI models to keep call quality and reliability high. Today, OpenAI models generate responses for 80% of calls on Retell, with customers achieving resolution rates above 80%. Retell has also surpassed 1 trillion tokens consumed across OpenAI models, reflecting the scale of that adoption.
Retell’s engineers use Codex throughout development, from researching unfamiliar code to building features and debugging issues, and GPT‑6 Astra to test front-end interfaces.
“We choose models the way our customers choose vendors: on real calls, not demos. OpenAI keeps earning its spot.”
Enterprise voice agents need to follow company policies and stay within their assigned roles, even as conversations change. Retell built its vCX benchmark to compare dozens of models using difficult, multi-turn conversations drawn from real production scenarios.
It tests how well models follow instructions, stay grounded in the information they are given, call the right tools, and use natural conversational language. In Retell’s October 13, 2026 benchmark, GPT‑6 Astra ranked first for instruction following at 89.7%.
Retell also shared evaluations conducted before the launch of the GPT‑6 Astra model that showed GPT‑5.5 ranking highest among models on both non-hallucination rate (88.0%) and tool-call correctness (88.3%) with reasoning enabled, outperforming Gemini‑3.1‑Pro and Claude‑4.6 on both measures. It also led on non-hallucination rate with reasoning off.
Retell says new customers who choose OpenAI models continue using them. Customers also have access to OpenAI Realtime models through the Realtime API and OpenAI text-to-speech as a voice engine. Retell makes new OpenAI models available the day they launch and reruns its benchmark as models improve.
Contact center teams use Retell to define their agents’ business context, goals, and rules. Developers can further configure workflows and integrations.
During a call, Retell’s harness runs the audio models, tracks the conversation, and manages when the agent speaks, listens, or responds to interruptions. OpenAI models, including GPT‑6.1 Sol, GPT‑6 Astra, and GPT‑5.5, use the customer’s instructions and conversation context to generate responses and request tool calls. Retell executes those calls through integrations with the customer’s systems and can write outcomes back to a customer record.
When human judgment is needed, the voice agent briefs the representative with context, answers their follow-up questions, and confirms they are ready before connecting the customer. After a call, Retell uses GPT‑5.6 Terra to analyze the transcript and extract outcomes and other data.
With this approach, Retell reports customer resolution rates above 80%. At electronics company Anker, agents handle Tier‑1 post-sales support in the US and UK, including order tracking, warranty checks, and troubleshooting. Anker reported an 80.4% case resolution rate and a Net Promoter Score (NPS) of 63. Its team also said the agents’ tone, pacing, and warmth met the standard they expected of a human agent.
At Pine Park Health, Retell agents handle appointment reminders and proactive scheduling and record the outcomes in patients’ medical charts. The team recovered medical-assistant time equivalent to 2.1 full-time roles and redirected it to patient care. Its scheduling agent achieved a 55.7% booking rate when it reached a patient.
Daytime traffic on Retell’s platform can reach more than 50 times its overnight low. To help keep calls responsive even at peak demand, Retell uses dedicated servers with OpenAI, shared across its customers. In Retell’s October 13, 2026 benchmark, GPT‑6 Astra and GPT‑6 Sol each recorded median first-sentence latency of 883 milliseconds, with GPT‑5.6 Luna ranking highest at 497 milliseconds.
OpenAI’s regional API endpoints allow developers to route traffic to specific geographic regions, which helps Retell reduce network travel time and meet customers’ data residency requirements in industries such as healthcare and financial services.
Retell also builds redundancy into each layer of the service. It runs its own telephony stack with a second stack ready to take over, along with backups for voice and model services, to help keep calls running when a service fails.
As Retell’s enterprise growth brings more requests for custom workflows and integrations, its engineers often need to work in unfamiliar parts of the codebase. Before writing code, they give Codex a high-level requirement. Codex surveys the code, checks it against actual data, and explains what needs to change. One engineer said Codex did this research better and much faster than he could.
During implementation, engineers say Codex often catches details they had not considered. For debugging, Codex cross-references logs and database records with the code to investigate what is happening. Retell’s engineers also use Codex in their work with customers building on OpenAI models to speed up tasks such as writing the instructions that guide how a model responds in a customer’s application.
To test changes to its web interface, Retell relies on GPT‑6 Astra’s computer use. When an engineer opens a front-end pull request, GPT‑6 Astra opens the interface, clicks through it, tests flows, tries to break them, and reports the issues it finds. Retell says earlier models could not perform this work reliably.
Retell continues to hire, but its engineering team hasn’t needed to grow at the same pace as the business. Retell reports $1.6 million in annual revenue run rate per employee.
“By using Codex, the engineering team has a virtual teammate that can pair with them on any kind of difficult task.”
Every business knows how it wants to serve its customers. Retell’s goal is to let the people closest to that work apply their expertise directly, using advanced voice AI and a platform that lets them build, test, and improve their own agents. Next, Retell plans to expand quality assurance and simulation, support more languages, and develop knowledge base auto-learning.
As OpenAI models continue to advance, Retell expects its customers to deliver world-class service to millions of callers, on their own terms.
“No one knows a company’s customers better than the company itself. We give them the power to act on that expertise and improve AI agents directly. The goal is for them to build an in-house team of internally deployed engineers who own and run it all,” says Bing.
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