{"@context":"https://schema.org","@type":"NewsArticle","generatedAt":"2026-07-28T06:20:51.496Z","headline":"如何评估不同 LLM 提供商在延迟、吞吐量和正常运行时间上的性能","description":"同一模型在不同提供商端点上的表现因基础设施、量化、负载处理和路由默认设置而异。评估需测量延迟、吞吐量、正常运行时间和精度，并将测量结果转化为路由策略。","url":"https://www.aioga.com/news/cms3y2wcy03toro82ozgc5187/","mainEntityOfPage":"https://www.aioga.com/news/cms3y2wcy03toro82ozgc5187/","datePublished":"2026-07-28T00:00:00.000Z","dateModified":"2026-07-28T00:00:00.000Z","inLanguage":"zh-CN","publisher":{"@type":"NewsMediaOrganization","name":"Aioga","url":"https://www.aioga.com"},"citation":["https://openrouter.ai/blog/insights/evaluate-llm-provider-performance","https://aihot.virxact.com/items/cms3y2wcy03toro82ozgc5187"],"canonicalUrl":"https://www.aioga.com/news/cms3y2wcy03toro82ozgc5187/","directAnswer":{"@type":"Answer","text":"同一模型经不同提供商端点交付时，可能因基础设施、路由行为、量化选择及故障模式而呈现不同表现。评估应拆分首个令牌延迟、持续输出速度、正常运行时间和精度，并据此制定路由策略。","url":"https://www.aioga.com/news/cms3y2wcy03toro82ozgc5187/","dateCreated":"2026-07-28T00:00:00.000Z","author":{"@type":"Organization","@id":"https://www.aioga.com/authors/aioga-editorial/#editorial-team","name":"Aioga Editorial Team","url":"https://www.aioga.com/authors/aioga-editorial/"}},"evidence":[{"@type":"CreativeWork","name":"openrouter.ai source article","url":"https://openrouter.ai/blog/insights/evaluate-llm-provider-performance","datePublished":"2026-07-28T00:00:00.000Z","provider":{"@type":"Organization","name":"openrouter.ai","url":"https://openrouter.ai/blog/insights/evaluate-llm-provider-performance"}},{"@type":"CreativeWork","name":"AIHot archive record","url":"https://aihot.virxact.com/items/cms3y2wcy03toro82ozgc5187","datePublished":"2026-07-28T00:00:00.000Z","provider":{"@type":"Organization","name":"AIHot","url":"https://aihot.virxact.com/items/cms3y2wcy03toro82ozgc5187"}}],"aggregationSource":"OpenRouter：Announcements（RSS）","originalPublisher":{"name":"openrouter.ai","url":"https://openrouter.ai/blog/insights/evaluate-llm-provider-performance"},"article":{"id":"cms3y2wcy03toro82ozgc5187","slug":"cms3y2wcy03toro82ozgc5187","url":"https://www.aioga.com/news/cms3y2wcy03toro82ozgc5187/","title":"如何评估不同 LLM 提供商在延迟、吞吐量和正常运行时间上的性能","title_en":"How to Evaluate LLM Provider Performance Across Latency， Throughput， and Uptime","summary":"同一模型在不同提供商端点上的表现因基础设施、量化、负载处理和路由默认设置而异。评估需测量延迟、吞吐量、正常运行时间和精度，并将测量结果转化为路由策略。","source":"OpenRouter：Announcements（RSS）","sourceUrl":"https://openrouter.ai/blog/insights/evaluate-llm-provider-performance","aiHotUrl":"https://aihot.virxact.com/items/cms3y2wcy03toro82ozgc5187","publishedAt":"2026-07-28T00:00:00.000Z","category":"技巧观点","score":65,"selected":true,"articleBody":["Developers often start LLM selection by choosing a model such as Claude, Llama, Gemini, or DeepSeek, then treat the provider as a secondary decision. That’s understandable. But the model choice alone doesn’t tell you how the model performs through a specific provider endpoint.","The same model can run through multiple provider endpoints, and each endpoint can bring different infrastructure, routing behavior, quantization choices, and failure patterns. Your users and systems see those differences in practical ways. One provider may return the first token quickly, but slow down during the rest of the response. Another may serve a lower-precision variant that drifts on harder prompts. Another may perform well under normal traffic but fail under load.","Provider evaluation compares serving environments rather than treating the model as a standalone product. It asks how long users wait for the first token, how quickly the rest of the response arrives, how the provider behaves under load, and what precision level the model runs at.","This guide works through those questions in the order in which you can act on them: latency, throughput, uptime, and quantization. Once you can measure those trade-offs, the next step is to turn the results into routing logic so your application doesn’t hard-code whichever provider looked fastest in a single benchmark run.","A chat product, a document-generation pipeline, and a background summarization job don’t need the same performance profile. The right provider depends on the workload.","A provider can look fast at the start of a response and still feel slow by the time the full answer finishes. Time to first token (TTFT) captures the first part of that experience. Output speed captures the second part because it measures how many tokens the provider generates per second after generation begins. A provider with strong TTFT can still produce a weak long-form experience if its sustained token rate drops, while a provider with strong throughput can still feel poor in a chat interface if the first token arrives late.","Uptime adds the production layer that speed metrics can’t cover. A provider that performs well in benchmark conditions can still hurt the application if it fails during peak usage, rate-limits unexpectedly, or creates error spikes that force retries.","Once those measurements are separated, the next step is to read them at the appropriate confidence level. Median latency can describe the typical request, but production systems also need to know how many requests take much longer than the typical one. That’s where percentile metrics such as p50, p75, p90, and p99 become more useful than averages.","Averages hide the requests that create the worst user experience. If a provider serves most requests quickly but occasionally stalls badly, the average may still look acceptable, but your users will remember the stalled request.","We track latency and throughput metrics：https://openrouter.ai/docs/guides/routing/provider-selection for each model and provider using percentile statistics over a rolling 5-minute window. The available percentiles are p50, p75, p90, and p99.","A p50 latency value describes the median request, but it can hide the requests that sit much farther out in the distribution. A provider can have a strong p50 and a weak p99, meaning the typical request feels fine while a small but meaningful share of requests take long enough to create visible delays.","The right percentile depends on where the delay actually damages the workflow. In a chat interface, delay appears before the first token arrives, so p90 and p99 latency indicate whether the experience remains consistent beyond the typical request. In a batch job, the user isn’t waiting for each response to start, so sustained output speed provides a better view of how much work the system can complete over time.","As a concrete example, on June 24, 2026 UTC, two providers serving anthropic/claude-sonnet-4.5 showed different latency curves over a 1-day window, and both had a much wider p99 tail than their p50 values suggest.","Percentiles show how consistently a provider responds, but they don’t explain every difference between two providers serving the same model. Once latency and throughput are clear, the next question is whether both providers are serving the model in the same form.","Quantization is one of the easiest provider differences to miss because the model name doesn’t always reveal how the model is being served. Two providers can expose the same model slug while running it at different precision levels. One route may preserve more of the model’s original weight precision, while another may reduce precision to lower memory use, improve serving efficiency, or make the endpoint cheaper to operate.","Reducing precision changes how the model behaves under different types of prompts. Lower precision can make a model faster and cheaper to serve, but it can also change how the model handles prompts that require careful reasoning, code correctness, long context, or strict instruction-following.","We expose quantization as a routing decision because provider selection shouldn’t treat every endpoint with the same model name as equivalent. The quantizations：https://openrouter.ai/docs/guides/routing/provider-selection field lets you choose which precision levels your application can use, so quality risk becomes part of the routing policy instead of a hidden provider detail.","Read the table as a way to decide how much precision your workload can afford to trade for lower serving cost or higher speed. A summarization pipeline may tolerate a lower-precision endpoint if the outputs stay stable on your own test set. A code-editing workflow, tool-calling agent, or reasoning-heavy task gives you less room for silent quality loss because a small drift can change the answer, break a call, or produce code that looks plausible but fails.","When your evaluation shows that a workload needs higher precision, use the quantizations field to limit the provider endpoints your application can use:","You can also exclude a provider if your evaluation shows that its endpoint performs poorly for your prompts:","The model name starts the evaluation, but the provider endpoint completes it. Quantization is one reason public benchmarks and production behavior can diverge.","A public leaderboard：https://openrouter.ai/rankings helps you narrow the candidate set, but it can’t describe your production path. Benchmarks measure selected endpoints at a point in time, using their own prompts, parameters, regions, and scoring methods. Your application may use a different endpoint, prompt shape, or routing path than the one the benchmark measured.","A useful provider benchmark review should check the benchmark’s published methodology before treating the result as production evidence:","A benchmark result becomes useful when it leads to a second round of testing under your own workload. If a leaderboard points to a fast provider, the next question is whether that endpoint stays fast with your prompt length, routing path, and quality requirements. If it does, convert that result into a routing rule.","Hard-coding a single provider from a static ranking creates a fragile setup. Provider behavior varies with traffic, outages, routing paths, and serving configurations. That’s what our routing layer is designed to handle.","Evaluating one provider in isolation only tells you how that provider behaved under the conditions you measured. It doesn’t tell you what happens when the provider hits a rate limit, slows down under load, or becomes temporarily unavailable. If your application depends on one provider endpoint, every provider-side failure becomes part of your application’s behavior.","A routing layer changes the evaluation from a one-time provider choice into an ongoing selection problem. Instead of treating every provider outage, rate limit, or slowdown as an application failure, you can automatically route around those conditions.","We monitor response times, error rates, and availability：https://openrouter.ai/docs/guides/best-practices/uptime-optimization across providers in real time. Our default routing deprioritizes providers that saw significant outages in the last 30 seconds, load-balances across the stable ones weighted toward lower prices, and keeps the rest available as fallbacks.","Multi-provider routing doesn’t guarantee perfect uptime. It reduces dependence on a single endpoint by providing an alternative path when a provider fails or becomes unavailable.","Model fallbacks：https://openrouter.ai/docs/guides/routing/model-fallbacks apply the same principle when provider-level routing is insufficient. If the primary model can’t complete the request because its providers are down, rate-limited, or unable to answer, OpenRouter can try the next model in the fallback list.","A practical routing strategy should answer three questions:","The main routing controls：https://openrouter.ai/docs/guides/routing/provider-selection sit inside the provider object. They let you sort providers, prefer performance thresholds, choose or avoid provider endpoints, filter by quantization, and decide whether fallbacks can run. Model fallbacks use the models array.","The simplest controls are the model suffixes：https://openrouter.ai/docs/guides/routing/provider-selection. Add :nitro when the request should prioritize providers with higher throughput:","Add :floor when the request should prioritize lower price:","Those shortcuts work when one preference should dominate the request. Production routing often needs a narrower rule. For example, a support workflow may need the cheapest endpoint that still sustains enough throughput for longer summaries. In that case, combine price sorting with a throughput preference:","This request asks OpenRouter to look across the listed models and prefer the cheapest model and provider endpoint that reaches 50 tokens per second at p90. Endpoints below that threshold don’t disappear from the route. They move behind preferred options, which keep them available if all preferred endpoints fail.","A chat workflow can use the same pattern with latency. If the first-token delay creates the user-visible bottleneck, combine price sorting with a latency preference:","This request keeps price in the decision, but it prefers endpoints that keep p90 latency under 3 seconds. The preference comes from the provider evaluation rather than from a fixed provider choice.","The same pattern works for quality and reliability decisions. If a workload performs better on higher-precision endpoints, add a quantization filter:","If a provider failed your test set or doesn’t meet your operational requirements, exclude it from the route:","If completion reliability matters more than staying with a single model, use a fallback list：https://openrouter.ai/docs/guides/routing/model-fallbacks:","The routing policy should come after measurement, not before it. Before you set thresholds, you need a simple evaluation process that tells you which thresholds matter.","Define the workload first. Don’t start with the provider. Start with the product behavior you need. A streaming chat app should measure time to first token and p90 latency. A batch-generation workflow should measure throughput and total completion time. A reasoning-heavy workflow should add quality checks and quantization review.","Shortlist with external benchmarks and live provider data. Use public leaderboards：https://openrouter.ai/rankings to identify candidates, then check current provider data on the model’s page：https://openrouter.ai/models in our catalog.","Test with your own prompts. Run prompts that represent your product’s real workload. Include short prompts, long prompts, easy prompts, and the prompts that usually break weak providers. Measure speed, reliability, and output quality together.","Check quantization and provider behavior. If two providers serve the same model but one gives worse answers on hard prompts, check the serving path before blaming the model. Pin higher precision or exclude the weaker endpoint when your evaluation supports that decision.","Enforce the result through routing. Turn the evaluation into request settings：https://openrouter.ai/docs/guides/routing/provider-selection. Use sort , :nitro , :floor , preferred_min_throughput , preferred_max_latency , quantizations , ignore , and fallbacks to keep the application aligned with the measurements. That keeps provider choice tied to your application rather than to a generic market ranking, and it makes performance review repeatable as provider behavior changes over time.","Latency measures how long the user waits before the first token arrives. Throughput measures the number of output tokens the provider generates per second after generation starts. A chat UI usually prioritizes latency, while a long-output workflow prioritizes throughput.","p50 is the median request experience. p90 means 90% of requests complete at or below that latency value. p99 shows the far tail of the distribution, which you need when your application must respond predictably for nearly all users.","The provider endpoint can change the serving environment. Quantization, infrastructure, context support, load, and provider defaults can all affect output behavior. The model slug identifies the model family, but the provider path affects the production result.","After you confirm the problem varies by provider, test it against your own prompts first. If the problem is repeatable, use provider.ignore to exclude that provider. If the issue appears tied to lower precision, use provider.quantizations to prefer higher-precision endpoints.","Multi-provider routing can improve reliability by adding fallback paths when a provider fails, rate-limits, or goes down. It reduces single-provider dependency, but it still depends on the quality of the routing policy and the available providers.","No. The request is priced using the model and provider endpoint that ultimately serve the completion. OpenRouter doesn’t mark up provider pricing, and failed requests aren’t billed, whether you route to one provider or ten.","Use provider.sort: \"latency\" when you care about time to first token. Use provider.sort: \"throughput\" or the :nitro suffix when you care about long-form generation speed. Add percentile thresholds when you need predictable performance rather than a simple speed preference."],"articleImages":[{"sourceUrl":"https://openrouter.ai/blog/images/evaluate-llm-provider-performance.png","alt":"How to Evaluate LLM Provider Performance Across Latency, Throughput, and Uptime","afterParagraph":0,"url":"/media/articles/cms3y2wcy03toro82ozgc5187/6a2a834ee943c18d.png"},{"sourceUrl":"https://openrouter.ai/blog/images/evaluate-provider-latency-percentiles.png","alt":"Line chart of latency percentiles for Claude Sonnet 4.5 on Amazon Bedrock and Google Vertex, showing p50 values of 6.3s and 7.8s widening to p99 values of 77.4s and 92.1s","afterParagraph":12,"url":"/media/articles/cms3y2wcy03toro82ozgc5187/5d2fa07d9b7c9413.png"}],"mediaStatus":"ok","articleBodyZh":["开发者通常在选择大语言模型（LLM）时先选择一个模型，例如 Claude、Llama、Gemini 或 DeepSeek，然后再将提供商作为次要决定。这是可以理解的。但仅凭模型选择并不能告诉你该模型通过特定提供商端点的实际表现。","同一个模型可以通过多个提供商端点运行，每个端点可能带来不同的基础设施、路由行为、量化选择和故障模式。你的用户和系统会以实际的方式看到这些差异。一个提供商可能会快速返回第一个 token，但在后续响应过程中速度变慢。另一个可能提供低精度的变体，在处理更复杂的提示时容易偏离。还有一个可能在正常流量下表现良好，但在高负载下会失败。","提供商评估侧重比较服务环境，而不是将模型视为独立产品。它关注用户等待第一个 token 的时间、剩余响应到达的速度、提供商在负载下的表现以及模型运行的精度水平。","本指南按你可以采取行动的顺序来处理这些问题：延迟、吞吐量、运行时间和量化。一旦你能衡量这些权衡，下一步就是将结果转化为路由逻辑，这样你的应用程序就不会在一次基准测试中硬编码看起来最快的提供商。","聊天产品、文档生成管道和后台摘要任务不需要相同的性能配置。合适的提供商取决于工作负载。","一个提供商在响应开始时看起来很快，但到完整答案完成时可能仍然感觉很慢。首个 token 时间（TTFT）捕捉了体验的第一部分。输出速度捕捉了第二部分，因为它测量生成开始后提供商每秒生成的 token 数量。即使一个提供商的 TTFT 很强，如果其持续 token 速率下降，长文本体验仍可能较差；同样，一个吞吐量强的提供商，如果首个 token 到达时间晚，在聊天界面中仍可能感觉很差。","正常运行时间增加了生产层面，速度指标无法涵盖的内容。一个在基准测试条件下表现良好的供应商，如果在高峰使用期间发生故障、意外限流或产生迫使重试的错误峰值，仍可能对应用程序造成损害。","一旦这些测量被区分开来，下一步就是以适当的置信水平读取它们。中位延迟可以描述典型请求，但生产系统还需要知道有多少请求比典型请求耗时更长。此时，百分位指标如 p50、p75、p90 和 p99 就比平均值更有用。","平均值会掩盖那些带来最差用户体验的请求。如果一个供应商大多数请求响应迅速，但偶尔严重阻塞，平均值可能仍然看起来可以接受，但你的用户会记住那些阻塞的请求。","我们使用滚动 5 分钟窗口的百分位统计来跟踪每个模型和供应商的延迟和吞吐率指标：https://openrouter.ai/docs/guides/routing/provider-selection。可用的百分位数是 p50、p75、p90 和 p99。","p50 延迟值描述中位请求，但它可能掩盖了分布中远端的请求。一个供应商可以有很好的 p50，但 p99 较弱，这意味着典型请求的体验还好，但一小部分有意义的请求耗时足够长，导致出现明显延迟。","适当的百分位取决于延迟在何处实际损害了工作流程。在聊天界面中，延迟出现在第一个 token 到达之前，因此 p90 和 p99 延迟可以表明在典型请求之外，体验是否保持一致。对于批处理作业，用户不必等待每个响应开始，因此持续输出速度可以更好地反映系统随时间完成工作的能力。","举个具体例子，在 2026 年 6 月 24 日 UTC，两个提供 anthropic/claude-sonnet-4.5 的供应商在 1 天窗口内显示了不同的延迟曲线，并且它们的 p99 尾部分布远比 p50 值显示的要宽。","百分位数显示了供应商响应的一致性，但它们无法解释两个供应商在提供相同模型时的所有差异。一旦延迟和吞吐率明确，接下来的问题是两个供应商是否以相同的形式提供模型。","量化是最容易被忽略的供应商差异之一，因为模型名称并不总能揭示模型的实际服务方式。两个供应商可能提供相同的模型标识符，但运行时的精度水平不同。一种方式可能保留更多模型原始权重精度，而另一种方式可能降低精度以减少内存使用、提高服务效率或降低端点运营成本。","降低精度会改变模型在不同类型提示下的行为。较低的精度可以使模型服务更快、更便宜，但也可能改变模型处理需要仔细推理、代码正确性、长上下文或严格遵循指令的提示的方式。","我们将量化作为路由决策公开，因为选择供应商不应将每个具有相同模型名称的端点视为等同。量化字段（https://openrouter.ai/docs/guides/routing/provider-selection）让您可以选择应用可以使用的精度等级，因此质量风险成为路由策略的一部分，而非隐藏的供应商细节。","请将表格作为决定工作负载可以以多大程度用较低精度来换取较低服务成本或更高速度的参考。如果摘要流水线输出在您自己的测试集上保持稳定，则可以容忍较低精度的端点。代码编辑工作流、工具调用代理或重推理任务给您的静默质量损失留下较少空间，因为小的偏差可能改变答案、破坏调用或生成看似合理但实际失败的代码。","当评估显示工作负载需要更高精度时，使用量化字段限制应用可以使用的供应商端点：","如果评估显示某供应商端点在您的提示下表现不佳，您也可以排除此供应商：","模型名称启动评估，但供应商端点完成评估。量化是公开基准测试与生产行为可能出现差异的一个原因。","公开排行榜：https://openrouter.ai/rankings 可以帮助你缩小候选集，但它无法描述你的生产路径。基准测试在某一时点测量选定的端点，使用它们自己的提示、参数、区域和评分方法。你的应用可能使用不同的端点、提示形式或路由路径，这与基准测试测量的可能不同。","有用的提供商基准评测应该在将结果视为生产证据之前，检查基准测试公布的方法论：","当基准结果引导你在自己的工作负载下进行第二轮测试时，它才变得有用。如果排行榜显示某个提供商速度快，下一个问题是该端点在你的提示长度、路由路径和质量要求下是否仍然快速。如果是，将该结果转换为路由规则。","从静态排名中硬编码单一提供商会创建脆弱的设置。提供商行为会随流量、中断、路由路径和服务配置而变化。这正是我们的路由层设计用来处理的问题。","孤立地评估一个提供商只会告诉你该提供商在你测量条件下的行为。它不会告诉你当提供商达到速率限制、在负载下变慢或暂时不可用时会发生什么。如果你的应用依赖于单一提供商端点，那么每一次提供商侧的失败都会成为你应用行为的一部分。","路由层将评估从一次性的提供商选择变为持续的选择问题。你可以自动绕过这些状况，而不是将每一次提供商中断、速率限制或变慢都视为应用失败。","我们实时监控各提供商的响应时间、错误率和可用性：https://openrouter.ai/docs/guides/best-practices/uptime-optimization。我们的默认路由会将过去30秒内出现重大中断的提供商优先级降低，在稳定的提供商之间进行负载均衡，并按价格权重偏向较低价格，同时保留其余提供商作为备用。","多提供商路由不能保证完美的正常运行时间。它通过在一个提供商失败或不可用时提供替代路径，降低对单一端点的依赖。","模型回退：https://openrouter.ai/docs/guides/routing/model-fallbacks 当提供者级路由不足时也适用相同的原则。如果主要模型因其提供者不可用、受限或无法回答而无法完成请求，OpenRouter 可以尝试回退列表中的下一个模型。","一个实用的路由策略应该回答三个问题：","主要路由控制：https://openrouter.ai/docs/guides/routing/provider-selection 位于提供者对象内部。它们让你可以排序提供者、偏好性能阈值、选择或避免提供者端点、按量化过滤，并决定回退是否可以运行。模型回退使用 models 数组。","最简单的控制是模型后缀：https://openrouter.ai/docs/guides/routing/provider-selection。当请求应优先选择高吞吐量提供者时，添加 :nitro：","当请求应优先选择价格较低时，添加 :floor：","当一个偏好应主导请求时，这些快捷方式有效。生产路由通常需要更精细的规则。例如，一个支持工作流程可能需要最便宜的端点，同时仍能维持足够的吞吐量以生成较长的摘要。在这种情况下，将价格排序与吞吐量偏好结合：","此请求要求 OpenRouter 在列出的模型中查找，并优先选择在 p90 下达到每秒 50 个 token 的最便宜模型和提供者端点。低于该阈值的端点不会从路由中消失。它们会排在首选选项之后，如果所有首选端点失败，它们仍然可用。","聊天工作流程可以使用相同模式处理延迟。如果首个 token 延迟形成用户可见的瓶颈，则将价格排序与延迟偏好结合：","此请求在决策中保留价格，但偏好延迟 p90 低于 3 秒的端点。偏好来自提供者评估，而非固定提供者选择。","同样的模式适用于质量和可靠性决策。如果工作负载在高精度端点上表现更好，请添加量化过滤器：","如果某个提供者未通过你的测试集或未满足你的运营要求，将其从路由中排除：","如果完成可靠性比坚持使用单一模型更重要，请使用备用列表：https://openrouter.ai/docs/guides/routing/model-fallbacks","路由策略应该在测量之后制定，而不是之前。在设置阈值之前，你需要一个简单的评估流程来告诉你哪些阈值是重要的。","首先定义工作负载。不要从提供商开始，要从你需要的产品行为开始。一个流式聊天应用应该测量首个标记到达时间和 p90 延迟。一个批量生成工作流应该测量吞吐量和总完成时间。重推理工作流应该增加质量检查和量化评审。","结合外部基准和实时提供商数据进行初步筛选。使用公开排行榜：https://openrouter.ai/rankings 来确定候选模型，然后在我们的目录中查看模型页面上的当前提供商数据：https://openrouter.ai/models。","使用你自己的提示进行测试。运行能代表你产品实际工作负载的提示。包括短提示、长提示、简单提示，以及通常会让弱提供商出错的提示。综合测量速度、可靠性和输出质量。","检查量化和提供商行为。如果两个提供商提供相同的模型，但在困难提示上一个回答更差，请在责怪模型之前检查服务路径。在评估支持的情况下，提高精度或排除较弱的端点。","通过路由强制执行结果。将评估结果转化为请求设置：https://openrouter.ai/docs/guides/routing/provider-selection。使用 sort、:nitro、:floor、preferred_min_throughput、preferred_max_latency、quantizations、ignore 和 fallbacks 来保持应用与测量结果一致。这样可以使提供商选择绑定到你的应用而不是通用市场排名，并且在提供商行为随时间变化时使性能评审可重复。","延迟衡量用户在收到第一个标记之前的等待时间。吞吐量衡量生成开始后提供商每秒生成的输出标记数量。聊天界面通常优先考虑延迟，而长输出工作流优先考虑吞吐量。","p50 是请求体验的中位数。p90 表示 90% 的请求完成时延在该数值或以下。p99 显示分布的远尾，这在你的应用需要对几乎所有用户都能做出可预测响应时非常重要。","提供商端点可以改变服务环境。量化、基础设施、上下文支持、负载和提供商默认设置都可能影响输出行为。模型标识（slug）确定模型系列，但提供商路径会影响生产结果。","在确认问题因提供商而异后，先用你自己的提示进行测试。如果问题可重复出现，可使用 provider.ignore 排除该提供商。如果问题似乎与低精度相关，可使用 provider.quantizations 优先选择高精度端点。","多提供商路由可以通过在提供商失败、达到速率限制或宕机时增加备用路径来提高可靠性。它减少了对单一提供商的依赖，但仍依赖于路由策略的质量和可用提供商的数量。","不。请求的计费基于最终提供完成服务的模型和提供商端点。OpenRouter 不会加价，失败的请求不收费，无论你路由到一个提供商还是十个提供商。","当你关心第一个标记的时间时，使用 provider.sort: \"latency\"。当你关心长文本生成速度时，使用 provider.sort: \"throughput\" 或 :nitro 后缀。当你需要可预测的性能而不仅仅是简单的速度偏好时，添加百分位阈值。"],"translationStatus":"translated","bodyOrigin":"source-page","editorial":{"summary":"同一模型经不同提供商端点交付时，可能因基础设施、路由行为、量化选择及故障模式而呈现不同表现。评估应拆分首个令牌延迟、持续输出速度、正常运行时间和精度，并据此制定路由策略。","background":"开发者常先选择 Claude、Llama、Gemini 或 DeepSeek 等模型，再把提供商视为次要决策。但材料指出，模型本身不能代表具体端点体验；聊天、文档生成和后台摘要任务所需的性能特征也并不相同。","viewpoint":"Aioga 判断，供应商评估不宜依赖单次基准或平均值。首个令牌快不等于完整回答快，高吞吐也不保证聊天体验良好；同时观察 p50、p75、p90 和 p99，可能更接近生产环境中的典型与长尾表现。","implications":"对应用团队而言，只按模型名称固定端点可能忽略负载下失败、意外限流、错误峰值及重试成本。值得关注的是，速度、稳定性与精度需要分开衡量，再按照不同工作负载确定优先级，而非寻找统一最优提供商。","nextStep":"可先按实际工作负载建立测试，分别记录首个令牌时间、生成开始后的令牌速度、正常运行情况、错误与量化差异，并比较中位数及多个百分位。随后将结果转化为可调整的路由逻辑，避免依据一次最快结果硬编码端点。","evidenceRefs":["title","summary","articleBody","source"],"status":"published","aiGenerated":true,"autoApproved":true,"generatedBy":"aioga-editorial:gpt-5.6-sol","reviewedBy":"aioga-editorial-review:gpt-5.6-sol","generatedAt":"2026-07-28T02:49:33.819Z","sourceHash":"92c82cd1df2ab48d","review":{"approved":true,"groundedness":95,"clarity":92,"duplicationRisk":12,"blockingIssues":[],"notes":["“精度”可进一步明确为“量化精度或模型输出质量”，以避免与准确率概念混淆。","“Aioga 判断”属于编辑观点，当前已通过 viewpoint 字段明确标注，不构成观点冒充事实。"]},"validation":{"passed":true,"mode":"ai-auto","revisions":0,"checks":["schema","length","source-attribution","low-source-overlap","no-html","independent-ai-review"]}},"tags":["技巧观点","OpenRouter：Announcements（RSS）"],"translations":{"zh-CN":{"title":"如何评估不同 LLM 提供商在延迟、吞吐量和正常运行时间上的性能","summary":"同一模型在不同提供商端点上的表现因基础设施、量化、负载处理和路由默认设置而异。评估需测量延迟、吞吐量、正常运行时间和精度，并将测量结果转化为路由策略。","category":"技巧观点","source":"openrouter.ai","aggregationSource":"OpenRouter：Announcements（RSS）","pageTitle":"如何评估不同 LLM 提供商在延迟、吞吐量和正常运行时间上的性能 - Aioga AI资讯","description":"同一模型在不同提供商端点上的表现因基础设施、量化、负载处理和路由默认设置而异。评估需测量延迟、吞吐量、正常运行时间和精度，并将测量结果转化为路由策略。","url":"https://www.aioga.com/news/cms3y2wcy03toro82ozgc5187/"},"en":{"title":"How to evaluate the performance of different LLM providers in terms of latency, throughput, and uptime","summary":"The performance of the same model varies across different provider endpoints due to infrastructure, quantization, load handling, and default routing settings. Evaluation requires measuring latency, throughput, uptime, and accuracy, and translating the measurements into routing strategies.","category":"Insights","source":"OpenRouter：Announcements（RSS）","aggregationSource":"OpenRouter：Announcements（RSS）","pageTitle":"How to evaluate the performance of different LLM providers in terms of latency, throughput, and uptime - Aioga AI News","description":"The performance of the same model varies across different provider endpoints due to infrastructure, quantization, load handling, and default routing settings. Evaluation requires m...","url":"https://www.aioga.com/en/news/cms3y2wcy03toro82ozgc5187/","contentTranslated":true,"sourceHash":"889b58a7a02f36ee","translatedAt":"2026-07-28T01:03:23.086Z"},"ja":{"title":"異なる LLM 提供業者の遅延、スループット、および稼働時間における性能をどのように評価するか","summary":"同じモデルでも、異なるプロバイダーのエンドポイントでは、インフラ、量子化、負荷処理、ルーティングのデフォルト設定によりパフォーマンスが異なります。評価では、遅延、スループット、稼働時間、精度を測定し、測定結果をルーティング戦略に変換する必要があります。","category":"ヒントと視点","source":"OpenRouter：Announcements（RSS）","aggregationSource":"OpenRouter：Announcements（RSS）","pageTitle":"異なる LLM 提供業者の遅延、スループット、および稼働時間における性能をどのように評価するか - Aioga AIニュース","description":"同じモデルでも、異なるプロバイダーのエンドポイントでは、インフラ、量子化、負荷処理、ルーティングのデフォルト設定によりパフォーマンスが異なります。評価では、遅延、スループット、稼働時間、精度を測定し、測定結果をルーティング戦略に変換する必要があります。","url":"https://www.aioga.com/ja/news/cms3y2wcy03toro82ozgc5187/","contentTranslated":true,"sourceHash":"889b58a7a02f36ee","translatedAt":"2026-07-28T01:03:24.371Z"},"ko":{"title":"다양한 LLM 제공업체의 지연 시간, 처리량 및 가동 시간 성능을 어떻게 평가할 것인가","summary":"같은 모델도 서로 다른 제공업체 엔드포인트에서는 인프라, 양자화, 부하 처리 및 라우팅 기본 설정에 따라 성능이 달라집니다. 평가 시에는 지연, 처리량, 정상 가동 시간 및 정확도를 측정하고, 측정 결과를 라우팅 전략으로 전환해야 합니다.","category":"인사이트","source":"OpenRouter：Announcements（RSS）","aggregationSource":"OpenRouter：Announcements（RSS）","pageTitle":"다양한 LLM 제공업체의 지연 시간, 처리량 및 가동 시간 성능을 어떻게 평가할 것인가 - Aioga AI 뉴스","description":"같은 모델도 서로 다른 제공업체 엔드포인트에서는 인프라, 양자화, 부하 처리 및 라우팅 기본 설정에 따라 성능이 달라집니다. 평가 시에는 지연, 처리량, 정상 가동 시간 및 정확도를 측정하고, 측정 결과를 라우팅 전략으로 전환해야 합니다.","url":"https://www.aioga.com/ko/news/cms3y2wcy03toro82ozgc5187/","contentTranslated":true,"sourceHash":"889b58a7a02f36ee","translatedAt":"2026-07-28T01:04:05.660Z"},"es":{"title":"Cómo evaluar el rendimiento de diferentes proveedores de LLM en cuanto a latencia, rendimiento y tiempo de actividad","summary":"El rendimiento del mismo modelo en diferentes puntos finales de proveedores varía según la infraestructura, la cuantificación, el manejo de la carga y la configuración predeterminada de enrutamiento. La evaluación debe medir la latencia, el rendimiento, el tiempo de actividad y la precisión, y convertir los resultados de la medición en estrategias de enrutamiento.","category":"Ideas","source":"OpenRouter：Announcements（RSS）","aggregationSource":"OpenRouter：Announcements（RSS）","pageTitle":"Cómo evaluar el rendimiento de diferentes proveedores de LLM en cuanto a latencia, rendimiento y tiempo de actividad - Aioga Noticias de IA","description":"El rendimiento del mismo modelo en diferentes puntos finales de proveedores varía según la infraestructura, la cuantificación, el manejo de la carga y la configuración predetermina...","url":"https://www.aioga.com/es/news/cms3y2wcy03toro82ozgc5187/","contentTranslated":true,"sourceHash":"889b58a7a02f36ee","translatedAt":"2026-07-28T01:04:00.762Z"},"fr":{"title":"Comment évaluer les performances de différents fournisseurs de LLM en termes de latence, de débit et de disponibilité","summary":"Les performances du même modèle sur différents points de terminaison de fournisseurs varient en fonction de l'infrastructure, de la quantification, du traitement de la charge et des paramètres par défaut de routage. L'évaluation doit mesurer la latence, le débit, le temps de fonctionnement et la précision, et convertir les résultats des mesures en stratégies de routage.","category":"Analyses","source":"OpenRouter：Announcements（RSS）","aggregationSource":"OpenRouter：Announcements（RSS）","pageTitle":"Comment évaluer les performances de différents fournisseurs de LLM en termes de latence, de débit et de disponibilité - Aioga Actualités IA","description":"Les performances du même modèle sur différents points de terminaison de fournisseurs varient en fonction de l'infrastructure, de la quantification, du traitement de la charge et de...","url":"https://www.aioga.com/fr/news/cms3y2wcy03toro82ozgc5187/","contentTranslated":true,"sourceHash":"889b58a7a02f36ee","translatedAt":"2026-07-28T01:04:38.982Z"},"de":{"title":"Wie man die Leistung verschiedener LLM-Anbieter in Bezug auf Latenz, Durchsatz und Betriebszeit bewertet","summary":"Die Leistung desselben Modells an unterschiedlichen Endpunkten von Anbietern variiert je nach Infrastruktur, Quantisierung, Lastverarbeitung und Standard-Routing-Einstellungen. Die Bewertung muss Latenz, Durchsatz, Betriebszeit und Genauigkeit messen und die Messergebnisse in Routing-Strategien umsetzen.","category":"技巧观点","source":"OpenRouter：Announcements（RSS）","aggregationSource":"OpenRouter：Announcements（RSS）","pageTitle":"Wie man die Leistung verschiedener LLM-Anbieter in Bezug auf Latenz, Durchsatz und Betriebszeit bewertet - Aioga KI-News","description":"Die Leistung desselben Modells an unterschiedlichen Endpunkten von Anbietern variiert je nach Infrastruktur, Quantisierung, Lastverarbeitung und Standard-Routing-Einstellungen. Die...","url":"https://www.aioga.com/de/news/cms3y2wcy03toro82ozgc5187/","contentTranslated":true,"sourceHash":"889b58a7a02f36ee","translatedAt":"2026-07-28T01:04:42.338Z"},"pt-BR":{"title":"Como avaliar o desempenho de diferentes fornecedores de LLM em termos de latência, taxa de transferência e tempo de atividade","summary":"O desempenho do mesmo modelo em diferentes endpoints de provedores varia devido à infraestrutura, quantização, gerenciamento de carga e configurações padrão de roteamento. A avaliação precisa medir latência, taxa de transferência, tempo de atividade e precisão, e transformar os resultados das medições em estratégias de roteamento.","category":"技巧观点","source":"OpenRouter：Announcements（RSS）","aggregationSource":"OpenRouter：Announcements（RSS）","pageTitle":"Como avaliar o desempenho de diferentes fornecedores de LLM em termos de latência, taxa de transferência e tempo de atividade - Aioga Notícias de IA","description":"O desempenho do mesmo modelo em diferentes endpoints de provedores varia devido à infraestrutura, quantização, gerenciamento de carga e configurações padrão de roteamento. A avalia...","url":"https://www.aioga.com/pt-BR/news/cms3y2wcy03toro82ozgc5187/","contentTranslated":true,"sourceHash":"889b58a7a02f36ee","translatedAt":"2026-07-28T01:05:15.318Z"},"ru":{"title":"Как оценивать производительность разных поставщиков LLM по задержке, пропускной способности и времени безотказной работы","summary":"Производительность одной и той же модели на разных конечных точках поставщиков зависит от инфраструктуры, квантизации, обработки нагрузки и настроек маршрутизации по умолчанию. Оценка должна включать измерение задержки, пропускной способности, времени безотказной работы и точности, а результаты измерений должны использоваться для разработки стратегии маршрутизации.","category":"技巧观点","source":"OpenRouter：Announcements（RSS）","aggregationSource":"OpenRouter：Announcements（RSS）","pageTitle":"Как оценивать производительность разных поставщиков LLM по задержке, пропускной способности и времени безотказной работы - Aioga Новости ИИ","description":"Производительность одной и той же модели на разных конечных точках поставщиков зависит от инфраструктуры, квантизации, обработки нагрузки и настроек маршрутизации по умолчанию. Оце...","url":"https://www.aioga.com/ru/news/cms3y2wcy03toro82ozgc5187/","contentTranslated":true,"sourceHash":"889b58a7a02f36ee","translatedAt":"2026-07-28T01:05:27.136Z"},"ar":{"title":"كيفية تقييم أداء مزودي LLM المختلفين من حيث التأخير ومعدل النقل ووقت التشغيل","summary":"يختلف أداء نفس النموذج على نقاط النهاية لمزودين مختلفين بسبب البنية التحتية والتكميم ومعالجة الأحمال والإعدادات الافتراضية للتوجيه. يجب تقييم الأداء من خلال قياس التأخير ومعدل النقل ووقت التشغيل والدقة، وتحويل نتائج القياس إلى استراتيجية توجيه.","category":"技巧观点","source":"OpenRouter：Announcements（RSS）","aggregationSource":"OpenRouter：Announcements（RSS）","pageTitle":"كيفية تقييم أداء مزودي LLM المختلفين من حيث التأخير ومعدل النقل ووقت التشغيل - Aioga أخبار الذكاء الاصطناعي","description":"يختلف أداء نفس النموذج على نقاط النهاية لمزودين مختلفين بسبب البنية التحتية والتكميم ومعالجة الأحمال والإعدادات الافتراضية للتوجيه. يجب تقييم الأداء من خلال قياس التأخير ومعدل النق...","url":"https://www.aioga.com/ar/news/cms3y2wcy03toro82ozgc5187/","contentTranslated":true,"sourceHash":"889b58a7a02f36ee","translatedAt":"2026-07-28T01:06:12.353Z"},"hi":{"title":"विभिन्न LLM प्रदाताओं के विलंब, थ्रूपुट और अपटाइम पर प्रदर्शन का मूल्यांकन कैसे करें","summary":"एक ही मॉडल विभिन्न प्रदाता एंडपॉइंट पर आधारभूत संरचना, क्वांटाइजेशन, लोड हैंडलिंग और रूटिंग डिफ़ॉल्ट सेटिंग्स के कारण अलग-अलग प्रदर्शन करता है। मूल्यांकन में विलंबता, थ्रूपुट, अपटाइम और सटीकता को मापना आवश्यक है, और माप परिणामों को रूटिंग रणनीति में परिवर्तित किया जाना चाहिए।","category":"技巧观点","source":"OpenRouter：Announcements（RSS）","aggregationSource":"OpenRouter：Announcements（RSS）","pageTitle":"विभिन्न LLM प्रदाताओं के विलंब, थ्रूपुट और अपटाइम पर प्रदर्शन का मूल्यांकन कैसे करें - Aioga AI समाचार","description":"एक ही मॉडल विभिन्न प्रदाता एंडपॉइंट पर आधारभूत संरचना, क्वांटाइजेशन, लोड हैंडलिंग और रूटिंग डिफ़ॉल्ट सेटिंग्स के कारण अलग-अलग प्रदर्शन करता है। मूल्यांकन में विलंबता, थ्रूपुट, अपटा...","url":"https://www.aioga.com/hi/news/cms3y2wcy03toro82ozgc5187/","contentTranslated":true,"sourceHash":"889b58a7a02f36ee","translatedAt":"2026-07-28T01:06:06.595Z"},"it":{"title":"Come valutare le prestazioni dei diversi fornitori di LLM in termini di latenza, throughput e uptime","summary":"Le prestazioni dello stesso modello su endpoint di fornitori diversi variano a seconda dell'infrastruttura, della quantizzazione, della gestione del carico e delle impostazioni predefinite di instradamento. La valutazione richiede la misurazione della latenza, della capacità di elaborazione, del tempo di attività e della precisione, e la conversione dei risultati delle misurazioni in strategie di instradamento.","category":"技巧观点","source":"OpenRouter：Announcements（RSS）","aggregationSource":"OpenRouter：Announcements（RSS）","pageTitle":"Come valutare le prestazioni dei diversi fornitori di LLM in termini di latenza, throughput e uptime - Aioga Notizie IA","description":"Le prestazioni dello stesso modello su endpoint di fornitori diversi variano a seconda dell'infrastruttura, della quantizzazione, della gestione del carico e delle impostazioni pre...","url":"https://www.aioga.com/it/news/cms3y2wcy03toro82ozgc5187/","contentTranslated":true,"sourceHash":"889b58a7a02f36ee","translatedAt":"2026-07-28T01:06:48.111Z"},"nl":{"title":"Hoe de prestaties van verschillende LLM-aanbieders te beoordelen op het gebied van latentie, doorvoer en uptime","summary":"De prestaties van hetzelfde model op verschillende aanbiedersendpunten verschillen door infrastructuur, kwantisatie, belastingverwerking en standaardrouteringsinstellingen. Evaluatie moet vertraging, doorvoer, beschikbaarheid en nauwkeurigheid meten, en de meetresultaten omzetten in routeringsstrategieën.","category":"技巧观点","source":"OpenRouter：Announcements（RSS）","aggregationSource":"OpenRouter：Announcements（RSS）","pageTitle":"Hoe de prestaties van verschillende LLM-aanbieders te beoordelen op het gebied van latentie, doorvoer en uptime - Aioga AI-nieuws","description":"De prestaties van hetzelfde model op verschillende aanbiedersendpunten verschillen door infrastructuur, kwantisatie, belastingverwerking en standaardrouteringsinstellingen. Evaluat...","url":"https://www.aioga.com/nl/news/cms3y2wcy03toro82ozgc5187/","contentTranslated":true,"sourceHash":"889b58a7a02f36ee","translatedAt":"2026-07-28T01:06:51.389Z"},"tr":{"title":"Farklı LLM sağlayıcılarının gecikme, verim ve çalışma süresi performansını nasıl değerlendirebilirsiniz","summary":"Aynı modelin farklı sağlayıcı uç noktalarındaki performansı altyapı, ölçümlendirme, yük işleme ve yönlendirme varsayılan ayarlarına bağlı olarak değişir. Değerlendirme, gecikme, veri akışı hızı, çalışma süresi ve doğruluğu ölçmeyi ve ölçüm sonuçlarını yönlendirme stratejilerine dönüştürmeyi gerektirir.","category":"技巧观点","source":"OpenRouter：Announcements（RSS）","aggregationSource":"OpenRouter：Announcements（RSS）","pageTitle":"Farklı LLM sağlayıcılarının gecikme, verim ve çalışma süresi performansını nasıl değerlendirebilirsiniz - Aioga AI Haberleri","description":"Aynı modelin farklı sağlayıcı uç noktalarındaki performansı altyapı, ölçümlendirme, yük işleme ve yönlendirme varsayılan ayarlarına bağlı olarak değişir. Değerlendirme, gecikme, ve...","url":"https://www.aioga.com/tr/news/cms3y2wcy03toro82ozgc5187/","contentTranslated":true,"sourceHash":"889b58a7a02f36ee","translatedAt":"2026-07-28T01:07:34.237Z"},"vi":{"title":"Làm thế nào để đánh giá hiệu suất của các nhà cung cấp LLM khác nhau về độ trễ, thông lượng và thời gian hoạt động","summary":"Hiệu suất của cùng một mô hình trên các điểm cuối của các nhà cung cấp khác nhau sẽ khác nhau do cơ sở hạ tầng, lượng tử hóa, xử lý tải và cài đặt mặc định về định tuyến. Việc đánh giá cần đo lường độ trễ, thông lượng, thời gian hoạt động và độ chính xác, đồng thời chuyển đổi kết quả đo lường thành chiến lược định tuyến.","category":"技巧观点","source":"OpenRouter：Announcements（RSS）","aggregationSource":"OpenRouter：Announcements（RSS）","pageTitle":"Làm thế nào để đánh giá hiệu suất của các nhà cung cấp LLM khác nhau về độ trễ, thông lượng và thời gian hoạt động - Tin tức AI Aioga","description":"Hiệu suất của cùng một mô hình trên các điểm cuối của các nhà cung cấp khác nhau sẽ khác nhau do cơ sở hạ tầng, lượng tử hóa, xử lý tải và cài đặt mặc định về định tuyến. Việc đánh...","url":"https://www.aioga.com/vi/news/cms3y2wcy03toro82ozgc5187/","contentTranslated":true,"sourceHash":"889b58a7a02f36ee","translatedAt":"2026-07-28T01:07:30.677Z"},"id":{"title":"Bagaimana mengevaluasi kinerja berbagai penyedia LLM dalam hal latensi, throughput, dan waktu operasi","summary":"Kinerja model yang sama pada titik akhir dari penyedia yang berbeda bervariasi karena infrastruktur, kuantisasi, penanganan beban, dan pengaturan default routing. Evaluasi perlu mengukur latensi, throughput, waktu operasi, dan akurasi, serta mengubah hasil pengukuran menjadi strategi routing.","category":"技巧观点","source":"OpenRouter：Announcements（RSS）","aggregationSource":"OpenRouter：Announcements（RSS）","pageTitle":"Bagaimana mengevaluasi kinerja berbagai penyedia LLM dalam hal latensi, throughput, dan waktu operasi - Berita AI Aioga","description":"Kinerja model yang sama pada titik akhir dari penyedia yang berbeda bervariasi karena infrastruktur, kuantisasi, penanganan beban, dan pengaturan default routing. Evaluasi perlu me...","url":"https://www.aioga.com/id/news/cms3y2wcy03toro82ozgc5187/","contentTranslated":true,"sourceHash":"889b58a7a02f36ee","translatedAt":"2026-07-28T01:08:11.279Z"},"th":{"title":"วิธีประเมินประสิทธิภาพของผู้ให้บริการ LLM ต่าง ๆ ในด้านความล่าช้า, อัตราการประมวลผล และเวลาทำงาน","summary":"ประสิทธิภาพของโมเดลเดียวกันบนจุดสิ้นสุดของผู้ให้บริการต่างกันขึ้นอยู่กับโครงสร้างพื้นฐาน การคำนวณเชิงปริมาณ การจัดการโหลด และการตั้งค่าเริ่มต้นของการกำหนดเส้นทาง การประเมินจำเป็นต้องวัดความหน่วง ทราฟฟิกที่สามารถรองรับ เวลาการทำงานและความแม่นยำ แล้วแปลงผลการวัดเหล่านี้เป็นกลยุทธ์การกำหนดเส้นทาง","category":"技巧观点","source":"OpenRouter：Announcements（RSS）","aggregationSource":"OpenRouter：Announcements（RSS）","pageTitle":"วิธีประเมินประสิทธิภาพของผู้ให้บริการ LLM ต่าง ๆ ในด้านความล่าช้า, อัตราการประมวลผล และเวลาทำงาน - ข่าว AI Aioga","description":"ประสิทธิภาพของโมเดลเดียวกันบนจุดสิ้นสุดของผู้ให้บริการต่างกันขึ้นอยู่กับโครงสร้างพื้นฐาน การคำนวณเชิงปริมาณ การจัดการโหลด และการตั้งค่าเริ่มต้นของการกำหนดเส้นทาง การประเมินจำเป็นต้...","url":"https://www.aioga.com/th/news/cms3y2wcy03toro82ozgc5187/","contentTranslated":true,"sourceHash":"889b58a7a02f36ee","translatedAt":"2026-07-28T01:08:17.530Z"},"pl":{"title":"Jak ocenić wydajność różnych dostawców LLM pod względem opóźnień, przepustowości i czasu pracy","summary":"Wydajność tego samego modelu na różnych punktach końcowych dostawców różni się w zależności od infrastruktury, kwantyzacji, obsługi obciążenia i domyślnych ustawień routingu. Ocena wymaga pomiaru opóźnienia, przepustowości, czasu pracy i dokładności, a wyniki pomiarów należy przekształcić w strategię routingu.","category":"技巧观点","source":"OpenRouter：Announcements（RSS）","aggregationSource":"OpenRouter：Announcements（RSS）","pageTitle":"Jak ocenić wydajność różnych dostawców LLM pod względem opóźnień, przepustowości i czasu pracy - Aioga Wiadomości AI","description":"Wydajność tego samego modelu na różnych punktach końcowych dostawców różni się w zależności od infrastruktury, kwantyzacji, obsługi obciążenia i domyślnych ustawień routingu. Ocena...","url":"https://www.aioga.com/pl/news/cms3y2wcy03toro82ozgc5187/","contentTranslated":true,"sourceHash":"889b58a7a02f36ee","translatedAt":"2026-07-28T01:08:56.253Z"}}}}