Cohere 发布 Parse 5：2.3B 视觉语言模型
Cohere Releases Parse 5 (parse-v5.0): A 2.3B Vision Language Model That Turns Enterprise Documents Into Markdown
Cohere has released Parse (parse-v5.0), a document parsing model aimed at high-volume enterprise ingestion. It is a 2.3B-parameter vision language model with an 8,192-token context window and a ~4.6GB footprint, built on Cohere Labs’ North-Micro-Vision-Instruct architecture. Parse takes a PDF, PPT or JPEG page as a base64-encoded data URI and returns Markdown containing text in reading order, tables rendered as HTML, lists, form key-value pairs, image descriptions and bounding box coordinates. There is no separate OCR stage in front of it. Cohere prices the Parse API at $1.50 per 1,000 pages and positions the model on price-performance rather than peak accuracy — a claim the company supports with a self-reported ParseBench score of 79.2 that, as we detail below, measures three of that benchmark’s five dimensions.
Cohere 发布了 Parse（parse-v5.0），这是一款面向高容量企业数据摄取而设计的文档解析模型。它是一个拥有 23 亿参数的视觉语言模型，具有 8,192 个令牌的上下文窗口和约 4.6GB 的模型大小，基于 Cohere Labs 的 North-Micro-Vision-Instruct 架构构建。Parse 将 PDF、PPT 或 JPEG 页面作为 base64 编码的数据 URI 输入，并返回包含按阅读顺序排列的文本、以 HTML 形式呈现的表格、列表、表单键值对、图像描述和边界框坐标的 Markdown 格式输出。它没有前置的独立 OCR 阶段。Cohere 将 Parse API 定价为每 1,000 页 1.50 美元，并将该模型定位为注重性价比而非峰值精度——这一说法得到了公司自报的 ParseBench 分数 79.2 的支持，正如我们下面详述的，该分数衡量了该基准测试五个维度中的三个。
Is it deployable?
它可以部署吗？
Yes, in production. Parse is generally available through the Cohere Parse API, Microsoft Foundry, AWS SageMaker, and single-tenant Model Vault. There is no waitlist and no research license.
可以，并且已投入生产。Parse 通过 Cohere Parse API、Microsoft Foundry、AWS SageMaker 和单租户 Model Vault 全面可用。无需等待名单，也没有研究许可限制。
- Which companies: Mid-market teams that already run a RAG stack can start on metered API calls with a free trial key. Large enterprises with residency or air-gap requirements go straight to Model Vault or private deployment. Seed-stage startups can use it, but the economics only start to matter above roughly 100K pages a month.
- Which industries: Cohere targets financial services, insurance, healthcare and life sciences, public sector, telecom, energy and manufacturing — the document-heavy verticals where scanned forms and dense tables are the norm.
- Applications: RAG ingestion, intelligent document processing, claims and invoice pipelines, contract and filing search, and giving document context to agents.
- 哪些公司适用：已经运行 RAG 技术栈的中端市场团队可以通过免费试用密钥开始使用按量计费的 API 调用。有数据驻留或气隙要求的大型企业可直接使用 Model Vault 或私有部署。种子期初创公司也可以使用，但只有在每月处理量超过约 10 万页时，其经济效益才会显现。
- 哪些行业适用：Cohere 的目标行业包括金融服务、保险、医疗保健和生命科学、公共部门、电信、能源和制造业——这些文档密集型垂直领域，扫描表单和密集表格是常态。
- 应用场景：RAG 数据摄取、智能文档处理、理赔和发票处理流程、合同和文件搜索，以及为智能体提供文档上下文。
What is Parse?
什么是 Parse？
Parse is a 2.3B-parameter vision language model built on Cohere Labs’ North-Micro-Vision-Instruct architecture, with an 8,192-token context window and a ~4.6GB footprint. It accepts PDF, PPT and JPEG pages as base64-encoded data URIs and returns Markdown containing document text, lists, tables rendered as HTML, bounding box coordinates and image descriptions.
Parse 是一个拥有 23 亿参数的视觉语言模型，基于 Cohere Labs 的 North-Micro-Vision-Instruct 架构构建，具有 8,192 个令牌的上下文窗口和约 4.6GB 的模型大小。它接受 PDF、PPT 和 JPEG 页面作为 base64 编码的数据 URI，并返回包含文档文本、列表、以 HTML 形式呈现的表格、边界框坐标和图像描述的 Markdown 格式输出。
There is no separate OCR stage in front of it. The model recovers text and reading order, tables, lists, forms and key-value pairs, images and captions, and the locations of page boundaries and visual elements in one pass. Nine languages are listed as stable — Arabic, English, French, German, Italian, Japanese, Korean, Portuguese and Spanish — with zero-shot support elsewhere at lower accuracy.
在其前端没有单独的OCR阶段。模型在一次处理中恢复文本和阅读顺序、表格、列表、表单和键值对、图像和标题，以及页面边界和视觉元素的位置。九种语言被列为稳定支持——阿拉伯语、英语、法语、德语、意大利语、日语、韩语、葡萄牙语和西班牙语——其他语言以较低准确率提供零样本支持。
Two output modes matter in practice. The default returns a Markdown string per page. Setting output_format="blocks" returns typed blocks, where a table block carries its HTML, its bounding box and a description. That second mode is what makes citation-level traceability possible.
两种输出模式在实践中很重要。默认模式每页返回一个Markdown字符串。设置output_format="blocks"返回类型化块，其中表格块携带其HTML、边界框和描述。第二种模式使得引用级别的可追溯性成为可能。
The Benchmark
基准测试
Cohere reports a ParseBench score of 79.2 for Parse, averaged across tables, content faithfulness and semantic formatting, ahead of Mistral OCR 4 (74.5), Azure Document Intelligence (74.3) and Databricks AI Parse (72.4).
Cohere报告Parse在ParseBench上的得分为79.2，平均涵盖表格、内容忠实度和语义格式，领先于Mistral OCR 4（74.5）、Azure Document Intelligence（74.3）和Databricks AI Parse（72.4）。
ParseBench is a LlamaIndex benchmark of ~2,078 human-verified enterprise pages scored on five dimensions: tables, charts, content faithfulness, semantic formatting and visual grounding. Cohere’s figure averages three of them and drops charts and visual grounding — the two dimensions where most parsers collapse.
ParseBench是LlamaIndex的一个基准测试，包含约2,078个经过人工验证的企业页面，从五个维度评分：表格、图表、内容忠实度、语义格式和视觉基础。Cohere的得分平均了其中三个维度，并省略了图表和视觉基础——这是大多数解析器失败的两个维度。
Against the public leaderboard, the same vendors score far lower on the full five-dimension overall: Mistral OCR 4 at 60.68, Databricks AI Parse at 60.68, Azure Document Intelligence (Layout) at 59.64. Azure’s three-dimension average works out to 74.3, which matches Cohere’s figure exactly and confirms the methodology. Cohere Parse is not currently listed on that leaderboard, where LlamaParse Agentic leads at 84.88.
与公开排行榜相比，同一供应商在完整五维总分上得分低得多：Mistral OCR 4为60.68，Databricks AI Parse为60.68，Azure Document Intelligence（布局）为59.64。Azure的三维平均分计算为74.3，与Cohere的数字完全一致，证实了方法论。Cohere Parse目前未在该排行榜上列出，其中LlamaParse Agentic以84.88领先。
So 79.2 is a vendor-reported subset score, not a leaderboard position. It is a reasonable claim to test on your own documents.
因此，79.2是供应商报告的子集得分，而非排行榜位置。这是一个合理的声明，可以在您自己的文档上进行测试。
What it Costs
成本
Cohere prices the Parse API at $1.50 per 1,000 pages. On Model Vault, Parse 5 runs $4.00/hour or $2,500/month for a Medium instance, and $7.00/hour or $4,300/month for XL.
Cohere将Parse API定价为每1,000页$1.50。在Model Vault上，Parse 5的Medium实例运行费用为每小时$4.00或每月$2,500，XL实例为每小时$7.00或每月$4,300。
The crossover nobody publishes: at $0.0015 per page, a single Medium instance breaks even at roughly 1.67M pages per month, and XL at roughly 2.87M. Below that, metered API calls are cheaper. Above it, dedicated capacity wins on price alone — before any argument about data residency, which is usually the real reason enterprises move to Vault.
没有人公布的交叉点：按每页$0.0015计算，单个Medium实例每月约167万页达到盈亏平衡，XL约287万页。低于此，按量计费的API调用更便宜。高于此，专用容量仅在价格上胜出——在考虑数据驻留问题之前，这通常是企业迁移到Vault的真正原因。
Key Takeaways
关键要点
- Cohere shipped parse-v5.0, a 2.3B VLM that converts PDFs, slides and images into Markdown with HTML tables and bounding boxes.
- API pricing is $1.50 per 1,000 pages; Model Vault runs $2,500/month (Medium) or $4,300/month (XL).
- Dedicated capacity only beats metered pricing above roughly 1.67M pages per month.
- The 79.2 ParseBench figure is vendor-reported across three of five dimensions and omits charts and visual grounding.
- Cohere发布了parse-v5.0，这是一个2.3B的VLM，可将PDF、幻灯片和图像转换为带有HTML表格和边界框的Markdown。
- API 定价为每 1,000 页 1.50 美元；Model Vault 费用为每月 2,500 美元（中型）或每月 4,300 美元（大型）。
- 专用容量仅在每月超过约 167 万页时才优于按量计费定价。
- 79.2 的 ParseBench 分数是供应商在五个维度中的三个维度上报告的，且未包含图表和视觉基础。
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