{"id":59826,"date":"2026-09-17T10:53:54","date_gmt":"2026-09-17T10:53:54","guid":{"rendered":"https:\/\/parashift.ai\/?page_id=59826"},"modified":"2026-09-28T09:52:47","modified_gmt":"2026-09-28T09:52:47","slug":"document-ai-benchmark","status":"publish","type":"page","link":"https:\/\/parashift.ai\/en\/document-ai-benchmark\/","title":{"rendered":"Document AI Benchmark 2026"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"59826\" class=\"elementor elementor-59826 elementor-59820 elementor-bc-flex-widget\" data-elementor-post-type=\"page\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-b10e4da elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"b10e4da\" data-element_type=\"section\" data-e-type=\"section\" data-settings=\"{&quot;pix_scale_in&quot;:&quot;none&quot;}\">\n\t\t\t\t\t\t<div class=\"elementor-container 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var(--line);border-radius:16px;padding:26px 24px;box-shadow:var(--shadow);display:flex;flex-direction:column;gap:16px;}\n.psvr-scope .tmn .tmn-q{color:var(--ink);font-size:14.5px;flex:1;white-space:pre-line;}\n.psvr-scope .tmn .tmn-q::before{content:\"\\201C\";color:var(--brand);font-size:28px;font-family:Georgia,serif;line-height:0;display:block;margin-bottom:6px;}\n.psvr-scope .tmn .tmn-who{display:flex;flex-direction:column;gap:2px;border-top:1px solid var(--line);padding-top:14px;}\n.psvr-scope .tmn .tmn-name{font-weight:700;font-size:13.5px;color:var(--ink);}\n.psvr-scope .tmn .tmn-role{font-size:12.5px;color:var(--ink-faint);}\n\n\/* ---------- Feature grid (long-form) ---------- *\/\n.psvr-scope .featuregrid{display:grid;grid-template-columns:repeat(3,1fr);gap:20px;margin-top:8px;}\n@media (max-width:900px){.psvr-scope .featuregrid{grid-template-columns:1fr 1fr;}}\n@media (max-width:600px){.psvr-scope .featuregrid{grid-template-columns:1fr;}}\n.psvr-scope .feat{background:var(--bg);border:1px solid var(--line);border-radius:14px;padding:24px 22px;box-shadow:var(--shadow);}\n.psvr-scope .feat .feat-ic{width:36px;height:36px;border-radius:10px;background:var(--brand-glow);color:var(--brand-deep);display:flex;align-items:center;justify-content:center;margin-bottom:14px;}\n.psvr-scope .feat .feat-ic svg{width:18px;height:18px;}\n.psvr-scope .feat h4{font-size:15.5px;font-weight:700;margin-bottom:9px;color:var(--ink);}\n.psvr-scope .feat p{font-size:13.5px;color:var(--ink-soft);}\n\n<\/style><style>.psvr-scope .btab{width:100%;border-collapse:collapse;font-size:14px}.psvr-scope .btab th,.psvr-scope .btab td{border-bottom:1px solid var(--line);padding:10px 12px;text-align:left;vertical-align:top}.psvr-scope .btab thead th{font-size:12px;text-transform:uppercase;letter-spacing:.06em;color:var(--ink-soft);white-space:nowrap}.psvr-scope .btab thead th.good,.psvr-scope .btab td.good{color:var(--brand);font-weight:700}.psvr-scope .btab .bnum{text-align:right;font-variant-numeric:tabular-nums;white-space:nowrap}.psvr-scope .btab .bsub{display:block;font-weight:400;font-size:12px;color:var(--ink-soft)}.psvr-scope .btab .bsum th,.psvr-scope .btab .bsum td{font-weight:700;border-top:2px solid var(--line)}.psvr-scope .btabwrap{overflow-x:auto;border:1px solid var(--line);border-radius:10px;background:var(--bg)}.psvr-scope .btabwrap{margin:26px 0 22px}.psvr-scope .btabwrap.narrow{max-width:560px}.psvr-scope .effektband{margin-top:34px}.psvr-scope .btab tbody tr:last-child th,.psvr-scope .btab tbody tr:last-child td{border-bottom:none}.psvr-scope .btab tbody tr.bsum th,.psvr-scope .btab tbody tr.bsum td{border-bottom:none}<\/style><div class=\"psvr-scope\">\n<div class=\"herowrap\"><div class=\"pagehead\"><div class=\"wrap\"><div class=\"pagehead-grid\"><div>\n<div class=\"crumb\">Parashift<span>\/<\/span>Dokumentli<span>\/<\/span>Benchmark<\/div>\n<span class=\"eyebrow\">Benchmark \u00b7 as of 25 September 2026<\/span>\n<h1>Document AI put to the test: Dokumentli against five frontier models.<\/h1>\n<p class=\"lede\">3365 real business documents, 5 datasets, 6 models, one task: extract structured fields with no prior knowledge of the layout. Dokumentli reaches 89.1 percent extraction accuracy, the best result in the field, and wins all five benchmarks against the strongest reference model.<\/p>\n<div class=\"ctas\"><a class=\"btn\" href=\"\/en\/demo\/\">Test your own documents<\/a><a class=\"btn ghost\" href=\"\/en\/dokumentli\/\">See Dokumentli<\/a><\/div>\n<div class=\"microproof\"><span>3365 documents, 6 models<\/span><span>Method and exclusions disclosed<\/span><span>Zones Zurich, Frankfurt, Amsterdam<\/span><\/div>\n<\/div>\n<div class=\"extractcard\"><div class=\"ec-top\"><span class=\"ec-id\">Benchmark 09\/2026<\/span><span class=\"ec-pill\">6 models<\/span><\/div>\n<div class=\"ec-body\"><div class=\"ec-thumb\"><svg width=\"60\" height=\"76\" viewBox=\"0 0 60 76\" fill=\"none\"><path d=\"M6 2h32l14 14v58a2 2 0 0 1-2 2H6a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2z\" fill=\"var(--bg-alt)\" stroke=\"var(--line)\" stroke-width=\"1.5\"><\/path><path d=\"M38 2v12a2 2 0 0 0 2 2h12\" fill=\"none\" stroke=\"var(--line)\" stroke-width=\"1.5\"><\/path><line x1=\"12\" y1=\"34\" x2=\"42\" y2=\"34\" stroke=\"#c7d0e0\" stroke-width=\"2.5\" stroke-linecap=\"round\"><\/line><line x1=\"12\" y1=\"43\" x2=\"42\" y2=\"43\" stroke=\"#c7d0e0\" stroke-width=\"2.5\" stroke-linecap=\"round\"><\/line><line x1=\"12\" y1=\"52\" x2=\"30\" y2=\"52\" stroke=\"#c7d0e0\" stroke-width=\"2.5\" stroke-linecap=\"round\"><\/line><\/svg><\/div>\n<div class=\"ec-fields\">\n<div class=\"ec-row\"><span>Avg accuracy<\/span><b>89.1 %<\/b><\/div>\n<div class=\"ec-row\"><span>Benchmarks won<\/span><b>5 of 5<\/b><\/div>\n<div class=\"ec-row\"><span>Avg speed<\/span><b>1.14 doc\/s<\/b><\/div>\n<div class=\"ec-row\"><span>Context budget<\/span><b>8192 tokens<\/b><\/div>\n<div class=\"ec-row\"><span>Reference models<\/span><b>up to 64000<\/b><\/div>\n<\/div><\/div>\n<div class=\"ec-foot\">cANLS at threshold 0.8, macro average across 5 benchmarks<\/div><\/div>\n<\/div><\/div><\/div><\/div>\n\n<section><div class=\"wrap\"><div class=\"section-head\"><span class=\"eyebrow\">The result<\/span><h2>Specialisation beats model size.<\/h2><\/div>\n<p class=\"prozess-lead\">The field: Claude Opus 5.5 from Anthropic, GPT-6 Astra from OpenAI, Gemini 3.8 Flash from Google, Qwen3.8-27B from Alibaba and the open model Gemma 4 26B-A4B-IT. All five are general purpose models trained at vastly greater expense. Dokumentli is a compact vision language model specialised on European business documents, and it leads all five on accuracy.<\/p>\n<div class=\"effektband\">\n<div class=\"effekt\"><div class=\"num\">89.1 %<\/div><p>average extraction accuracy, best of all six models<\/p><span class=\"src\">cANLS@0.8, 5 benchmarks<\/span><\/div>\n<div class=\"effekt\"><div class=\"num\">5 of 5<\/div><p>benchmarks won against Claude Opus 5.5, the strongest reference model<\/p><span class=\"src\">lead of 0.8 to 7.4 percentage points<\/span><\/div>\n<div class=\"effekt\"><div class=\"num\">1.9 \u00d7<\/div><p>faster than the most accurate reference model<\/p><span class=\"src\">1.14 against 0.60 documents per second<\/span><\/div>\n<div class=\"effekt\"><div class=\"num\">1\/8<\/div><p>of the context budget of the largest reference models<\/p><span class=\"src\">8192 against up to 64000 tokens<\/span><\/div>\n<\/div><\/div><\/section>\n\n<section class=\"alt\"><div class=\"wrap\"><div class=\"section-head\"><span class=\"eyebrow\">Every figure<\/span><h2>Accuracy by benchmark and model.<\/h2><\/div>\n<div class=\"btabwrap\"><table class=\"btab\"><thead><tr><th>Benchmark<\/th><th>Documents<\/th><th class=\"good\">Dokumentli 5.3<\/th><th>Claude Opus 5.5<\/th><th>GPT-6 Astra<\/th><th>Gemini 3.8<\/th><th>Qwen3.8-27B<\/th><th>Gemma 4 26B<\/th><\/tr><\/thead><tbody><tr><th>Invoice processing<span class=\"bsub\">Mixed accounts payable invoices<\/span><\/th><td class=\"bnum\">413<\/td><td class=\"bnum good\">89.1 %<\/td><td class=\"bnum\">86.1 %<\/td><td class=\"bnum\">86.0 %<\/td><td class=\"bnum\">85.5 %<\/td><td class=\"bnum\">83.7 %<\/td><td class=\"bnum\">71.1 %<\/td><\/tr><tr><th>E-commerce<span class=\"bsub\">Order and shipping documents<\/span><\/th><td class=\"bnum\">1474<\/td><td class=\"bnum good\">84.5 %<\/td><td class=\"bnum\">77.1 %<\/td><td class=\"bnum\">75.8 %<\/td><td class=\"bnum\">76.4 %<\/td><td class=\"bnum\">79.1 %<\/td><td class=\"bnum\">63.1 %<\/td><\/tr><tr><th>Logistics<span class=\"bsub\">Freight and contract documents<\/span><\/th><td class=\"bnum\">927<\/td><td class=\"bnum good\">82.2 %<\/td><td class=\"bnum\">79.7 %<\/td><td class=\"bnum\">79.8 %<\/td><td class=\"bnum\">78.3 %<\/td><td class=\"bnum\">75.4 %<\/td><td class=\"bnum\">62.9 %<\/td><\/tr><tr><th>Automotive<span class=\"bsub\">Vehicle workshop invoices<\/span><\/th><td class=\"bnum\">126<\/td><td class=\"bnum good\">96.8 %<\/td><td class=\"bnum\">96.0 %<\/td><td class=\"bnum\">95.8 %<\/td><td class=\"bnum\">94.6 %<\/td><td class=\"bnum\">93.2 %<\/td><td class=\"bnum\">\u2013<\/td><\/tr><tr><th>Real estate<span class=\"bsub\">Rental and utility statements<\/span><\/th><td class=\"bnum\">425<\/td><td class=\"bnum good\">93.4 %<\/td><td class=\"bnum\">87.5 %<\/td><td class=\"bnum\">88.7 %<\/td><td class=\"bnum\">85.8 %<\/td><td class=\"bnum\">78.2 %<\/td><td class=\"bnum\">70.3 %<\/td><\/tr><tr class=\"bsum\"><th>Average<\/th><td class=\"bnum\">3365<\/td><td class=\"bnum good\">89.1 %<\/td><td class=\"bnum\">85.3 %<\/td><td class=\"bnum\">85.2 %<\/td><td class=\"bnum\">84.1 %<\/td><td class=\"bnum\">81.9 %<\/td><td class=\"bnum\">66.9 %<\/td><\/tr><\/tbody><\/table><\/div>\n<p class=\"prozess-lead\">cANLS at threshold 0.8, macro average per benchmark. Reference values come from human verified fields of real business documents. The largest lead is on order and shipping documents at 7.4 percentage points, the narrowest on vehicle workshop invoices at 0.8.<\/p>\n<\/div><\/section>\n\n<section><div class=\"wrap\"><div class=\"section-head\"><span class=\"eyebrow\">Speed<\/span><h2>The fastest model in the field on average.<\/h2><\/div>\n<p class=\"prozess-lead\">In production document processes throughput decides cycle time and infrastructure cost. Claude Opus 5.5 is the fastest reference model but stays clearly behind. GPT-6 Astra, Gemini 3.8 Flash and above all Qwen3.8-27B fall much further behind.<\/p>\n<div class=\"btabwrap narrow\"><table class=\"btab\"><thead><tr><th>Model<\/th><th>Documents per second<\/th><\/tr><\/thead><tbody><tr><th>Dokumentli 5.3<\/th><td class=\"bnum good\">1.14<\/td><\/tr><tr><th>Claude Opus 5.5<\/th><td class=\"bnum\">0.60<\/td><\/tr><tr><th>Gemma 4 26B<\/th><td class=\"bnum\">0.34<\/td><\/tr><tr><th>GPT-6 Astra<\/th><td class=\"bnum\">0.21<\/td><\/tr><tr><th>Gemini 3.8 Flash<\/th><td class=\"bnum\">0.20<\/td><\/tr><tr><th>Qwen3.8-27B<\/th><td class=\"bnum\">0.08<\/td><\/tr><\/tbody><\/table><\/div>\n<p class=\"prozess-lead\">Averaged over all 5 benchmarks under identical conditions. Reference models via OpenRouter, Dokumentli through its own dedicated endpoint.<\/p>\n<\/div><\/section>\n\n<section class=\"alt\"><div class=\"wrap\"><div class=\"section-head\"><span class=\"eyebrow\">What follows<\/span><h2>Accuracy, speed and operations are connected.<\/h2><\/div>\n<div class=\"featuregrid\">\n<div class=\"feat\"><div class=\"feat-ic\"><svg viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.6\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><rect x=\"3\" y=\"5\" width=\"18\" height=\"12\" rx=\"1.5\"><\/rect><path d=\"M8 21h8M12 17v4M7 9h4M7 12h7\"><\/path><\/svg><\/div><h4>Runs on a single GPU<\/h4><p>Thanks to its small context budget Dokumentli runs through the Dokumentli Node component on standard PC hardware with one GPU. No dedicated server infrastructure, no cloud connection, no document leaving the building.<\/p><\/div>\n<div class=\"feat\"><div class=\"feat-ic\"><svg viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.6\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M12 3v18M7 7h7a3 3 0 0 1 0 6H7m0 0h8a3 3 0 0 1 0 6H6\"><\/path><\/svg><\/div><h4>Cost per document<\/h4><p>Higher throughput per compute unit at leading accuracy means lower cost per processed document. That shows at volume, as in the e-commerce benchmark with 1474 and logistics with 927 documents.<\/p><\/div>\n<div class=\"feat\"><div class=\"feat-ic\"><svg viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"1.6\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M12 3l8 3v6c0 4.5-3.2 7.8-8 9c-4.8-1.2-8-4.5-8-9V6z\"><\/path><path d=\"M9 12l2 2l4-4\"><\/path><\/svg><\/div><h4>European data sovereignty<\/h4><p>Processing in the zones Zurich, Frankfurt and Amsterdam, on request entirely inside your own data centre. ISO 27001, SOC 2 Type 2, BSI C5. The model is built and operated in Europe, not merely hosted there.<\/p><\/div>\n<\/div><\/div><\/section>\n\n<section><div class=\"wrap\"><div class=\"section-head\"><span class=\"eyebrow\">Questions about the benchmark<\/span><h2>Method, limits and pricing.<\/h2><\/div>\n<div class=\"faqlist\"><details class=\"qa\"><summary>What does cANLS at a threshold of 0.8 measure?<\/summary><div class=\"qa-a\"><p>cANLS stands for case-insensitive Average Normalized Levenshtein Similarity. Each extracted field is compared against a human verified reference value. A field counts as correct from a similarity of 0.8 upwards. This is a common variant of the ANLS industry standard for document extraction. Precision and recall on cell level and 95 percent confidence intervals were calculated in addition.<\/p><\/div><\/details><details class=\"qa\"><summary>Was Dokumentli trained on this test data?<\/summary><div class=\"qa-a\"><p>No. Dokumentli is specialised on business documents from the same industries and document types, but it was not trained on the benchmark datasets themselves. All 3365 test documents were unseen by the model.<\/p><\/div><\/details><details class=\"qa\"><summary>Is this an independent test?<\/summary><div class=\"qa-a\"><p>No, this is an internal benchmark run by Parashift. That is why the datasets, the metric, the model versions, the context budgets and every exclusion are disclosed. The five reference models were queried through standard APIs via OpenRouter with zero shot prompting, Dokumentli through its own dedicated endpoint.<\/p><\/div><\/details><details class=\"qa\"><summary>Why is the Gemma 4 figure missing for the automotive benchmark?<\/summary><div class=\"qa-a\"><p>Gemma 4 26B-A4B-IT was not re-tested against Dokumentli 5.3. The figures shown come from an earlier test run under the same conditions, and no measurement exists for the automotive benchmark.<\/p><\/div><\/details><details class=\"qa\"><summary>How much compute does Dokumentli need?<\/summary><div class=\"qa-a\"><p>Dokumentli works with a budget of 8192 tokens per request. The reference models were queried with up to 64000 tokens, roughly eight times the context per document, at lower or comparable accuracy. Through the Dokumentli Node component the model runs on standard PC hardware with a single GPU, without dedicated server infrastructure and without a cloud connection.<\/p><\/div><\/details><details class=\"qa\"><summary>Where are documents processed?<\/summary><div class=\"qa-a\"><p>In the zones Zurich, Frankfurt and Amsterdam, chosen by the customer. On request entirely inside your own data centre or on your own hardware. Parashift is certified to ISO 27001 and SOC 2 Type 2 and meets BSI C5.<\/p><\/div><\/details><details class=\"qa\"><summary>How high is the automation rate in practice?<\/summary><div class=\"qa-a\"><p>In productive Parashift processes the automation rate exceeds 90 percent. It measures the share of documents that pass through without human intervention and depends on confidence thresholds, validation rules and master data matching. It is measured per process and document type.<\/p><\/div><\/details><details class=\"qa\"><summary>What does Dokumentli cost?<\/summary><div class=\"qa-a\"><p>Dokumentli starts at 990 euros per month. The full Parashift platform with classification, separation, validation and evidence chain starts at 1590 euros per month.<\/p><\/div><\/details><\/div>\n<\/div><\/section>\n\n<section class=\"finalcta\"><div class=\"wrap\"><h2>The honest test is your own document stack.<\/h2>\n<p>Send us an anonymised set of your documents. You get back the extraction, the confidence values and the results field by field on your own paperwork, not on ours.<\/p>\n<div class=\"ctas\"><a class=\"btn\" href=\"\/en\/demo\/\">Test your own documents<\/a><a class=\"btn ghost\" href=\"\/en\/platform\/\">See the platform<\/a><\/div>\n<p class=\"note\">Dokumentli from 990 euros per month, platform from 1590 euros per month. Answer within one working day.<\/p>\n<\/div><\/section>\n<\/div>\n<script type=\"application\/ld+json\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"FAQPage\",\"@id\":\"https:\/\/parashift.ai\/en\/document-ai-benchmark\/#faq\",\"mainEntity\":[{\"@type\":\"Question\",\"name\":\"What does cANLS at a threshold of 0.8 measure?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"cANLS stands for case-insensitive Average Normalized Levenshtein Similarity. Each extracted field is compared against a human verified reference value. A field counts as correct from a similarity of 0.8 upwards. This is a common variant of the ANLS industry standard for document extraction. 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Through the Dokumentli Node component the model runs on standard PC hardware with a single GPU, without dedicated server infrastructure and without a cloud connection.\"}},{\"@type\":\"Question\",\"name\":\"Where are documents processed?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"In the zones Zurich, Frankfurt and Amsterdam, chosen by the customer. On request entirely inside your own data centre or on your own hardware. Parashift is certified to ISO 27001 and SOC 2 Type 2 and meets BSI C5.\"}},{\"@type\":\"Question\",\"name\":\"How high is the automation rate in practice?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"In productive Parashift processes the automation rate exceeds 90 percent. It measures the share of documents that pass through without human intervention and depends on confidence thresholds, validation rules and master data matching. It is measured per process and document type.\"}},{\"@type\":\"Question\",\"name\":\"What does Dokumentli cost?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"Dokumentli starts at 990 euros per month. The full Parashift platform with classification, separation, validation and evidence chain starts at 1590 euros per month.\"}}]},{\"@type\":\"Report\",\"@id\":\"https:\/\/parashift.ai\/en\/document-ai-benchmark\/#report\",\"name\":\"Document AI Benchmark 2026: Dokumentli against six frontier models\",\"datePublished\":\"2026-08-14\",\"inLanguage\":\"en\",\"author\":{\"@id\":\"https:\/\/parashift.ai\/en\/#organization\/\"},\"publisher\":{\"@id\":\"https:\/\/parashift.ai\/en\/#organization\/\"},\"about\":[\"Intelligent document processing\",\"Vision language model\",\"Document extraction\"],\"measurementTechnique\":\"case-insensitive Average Normalized Levenshtein Similarity (cANLS), threshold 0.8\",\"variableMeasured\":[\"Extraction accuracy cANLS@0.8\",\"Processing speed in documents per second\",\"Context budget in tokens per request\"],\"url\":\"https:\/\/parashift.ai\/en\/document-ai-benchmark\/\"}]}<\/script>\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Dokumentli reaches 88.4 percent against Claude, GPT-5.6 Sol, Gemini, Qwen and Gemma. 3368 documents, 5 datasets, method and exclusions disclosed.<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"content-type":"","footnotes":""},"class_list":["post-59826","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/parashift.ai\/en\/wp-json\/wp\/v2\/pages\/59826","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/parashift.ai\/en\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/parashift.ai\/en\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/parashift.ai\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/parashift.ai\/en\/wp-json\/wp\/v2\/comments?post=59826"}],"version-history":[{"count":2,"href":"https:\/\/parashift.ai\/en\/wp-json\/wp\/v2\/pages\/59826\/revisions"}],"predecessor-version":[{"id":59829,"href":"https:\/\/parashift.ai\/en\/wp-json\/wp\/v2\/pages\/59826\/revisions\/59829"}],"wp:attachment":[{"href":"https:\/\/parashift.ai\/en\/wp-json\/wp\/v2\/media?parent=59826"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}