{"id":1266,"date":"2026-04-25T05:00:04","date_gmt":"2026-04-25T05:00:04","guid":{"rendered":"https:\/\/aimodels.deepdigitalventures.com\/blog\/?p=1266"},"modified":"2026-04-25T05:00:04","modified_gmt":"2026-04-25T05:00:04","slug":"when-to-use-frontier-models-vs-specialist-ai-models","status":"publish","type":"post","link":"https:\/\/aimodels.deepdigitalventures.com\/blog\/when-to-use-frontier-models-vs-specialist-ai-models\/","title":{"rendered":"When to Use Frontier Models vs Specialist AI Models"},"content":{"rendered":"\n<p>A frontier model is a general-purpose AI system built to handle many kinds of prompts: reasoning, writing, code, multimodal inputs, tool calls, and long-context synthesis. A specialist model is narrower: it is trained, tuned, or wrapped to do one job well, such as extraction, transcription, moderation, ranking, or classification. By the end of this article, you should be able to decide which calls belong on a frontier model, which belong on specialist AI models, and which jobs should move to batch inference.<\/p>\n\n\n\n<p><em>By the Deep Digital Ventures AI Engineering Team, practitioners in AI routing, automation, and evaluation workflows. Substantially updated April 24, 2026.<\/em><\/p>\n\n\n\n<p><strong>Provider pricing, limits, batch windows, and model availability change frequently. The framework below is current as of 2026-04-24, but verify the source links before quoting in a contract, RFP, or cost plan.<\/strong><\/p>\n\n\n\n<p><strong>TL;DR:<\/strong><\/p>\n\n\n\n<ul class='wp-block-list'><li>Use frontier models when the work requires judgment across messy context, uncertain next steps, or tool use.<\/li><li>Use specialist AI models when the task has a stable input, a narrow output contract, and measurable acceptance rules.<\/li><li>Use batch inference when the user is not waiting and the work can finish later at lower operational cost.<\/li><li>The best production system is often hybrid: specialist first, frontier on ambiguity, human review on irreversible decisions.<\/li><\/ul>\n\n\n\n<h2 class='wp-block-heading'>Decision Checklist: Frontier vs Specialist vs Batch<\/h2>\n\n\n\n<ul class='wp-block-list'><li>If the request needs open-ended reasoning, conflicting evidence review, or tool selection, route it to a frontier model.<\/li><li>If the request asks for labels, fields, ranks, transcripts, or policy classes, test a specialist component first.<\/li><li>If the same task repeats thousands of times and the answer is not needed in the UI, test batch inference before scaling synchronous calls.<\/li><li>If a model action changes money, access, legal status, or another durable business record, add deterministic validation and human review.<\/li><\/ul>\n\n\n\n<p><strong>Takeaway:<\/strong> Route by the shape of the work first, then choose the model.<\/p>\n\n\n\n<p>Frontier models are impressive because they are broad. The Claude, GPT, and Gemini families can handle mixed prompts that involve writing, reasoning, code, image understanding, tool calls, and long-context synthesis. That breadth makes them attractive as the default route in a new product, but general ability is not the same thing as production fit.<\/p>\n\n\n\n<p>In production, a small classifier, embedding search layer, OCR or table extractor, speech-to-text system, moderation model, reranker, or deterministic rules layer can beat a frontier call when the input contract is stable and the failure can be measured.<\/p>\n\n\n\n<h2 class='wp-block-heading'>General-Purpose vs Specialized AI: The Core Tradeoff<\/h2>\n\n\n\n<p>A frontier model buys option value. It is the right starting point when the product does not yet know what users will ask, when the answer depends on several documents, or when the model must decide whether to call a search, database, calculator, CRM, or ticketing tool. OpenAI documents this pattern under function calling,<sup><a href='#source-1'>[1]<\/a><\/sup> and Anthropic documents the same application shape in its tool use guide.<sup><a href='#source-2'>[2]<\/a><\/sup><\/p>\n\n\n\n<p>A specialist model buys a tighter contract. It is the right starting point when the task looks like &#8216;return exactly these fields,&#8217; &#8216;rank these documents,&#8217; &#8216;detect this policy class,&#8217; or &#8216;transcribe this audio.&#8217; The test harness is simpler: one input shape, one output schema, one acceptance rule, and a known escalation path.<\/p>\n\n\n\n<figure class='wp-block-table'><table><thead><tr><th>Use frontier models when&#8230;<\/th><th>Use specialist models when&#8230;<\/th><\/tr><\/thead><tbody><tr><td>The task requires reasoning across messy context, such as reconciling a contract clause, a customer email, and a policy excerpt.<\/td><td>The task has a stable input and output format, such as invoice date, purchase order number, supplier name, and total amount.<\/td><\/tr><tr><td>The workflow needs multimodal judgment, such as comparing a screenshot with a spreadsheet or checking whether a chart supports the written claim.<\/td><td>The workflow is a repeated transformation, such as OCR, table extraction, transcription, moderation, tagging, or reranking.<\/td><\/tr><tr><td>The user may ask unpredictable follow-ups, so the router cannot predeclare every branch.<\/td><td>The model does one job repeatedly and can be evaluated with held-out examples from production logs.<\/td><\/tr><tr><td>Tool use and judgment are central, such as deciding whether to call retrieval, a calculator, or a business-system API.<\/td><td>Rules, classifiers, extractors, or rankers can be tested precisely before a frontier model is allowed to see only the hard cases.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>Do not use public benchmark names as a substitute for workflow tests. Broad knowledge, hard reasoning, software repair, code generation, and human preference benchmarks measure different things, and none of them proves that a model is right for invoice extraction, low-latency intent routing, or a batch classification job.<sup><a href='#source-3'>[3]<\/a><\/sup><sup><a href='#source-4'>[4]<\/a><\/sup><sup><a href='#source-5'>[5]<\/a><\/sup><sup><a href='#source-6'>[6]<\/a><\/sup><sup><a href='#source-7'>[7]<\/a><\/sup><\/p>\n\n\n\n<p><strong>Takeaway:<\/strong> Public benchmarks can narrow a candidate list, but production examples decide the route.<\/p>\n\n\n\n<h2 class='wp-block-heading'>When to Use Frontier Models<\/h2>\n\n\n\n<p>Frontier models win when the task looks simple in the UI but requires judgment behind the scenes. A support agent asking &#8216;can we refund this customer?&#8217; may require order history, warranty policy, abuse checks, tone control, and a tool call to issue the refund. A smaller classifier can route the case, but a frontier model is often better at weighing conflicting evidence before action.<\/p>\n\n\n\n<p>They also help when the workflow is still being discovered. In a new agent, copilot, or internal review tool, start with a frontier model to collect real traces: user intent, missing context, tool failures, bad retrieval, and cases where the answer depends on policy interpretation. Once those traces cluster into repeatable tasks, move the stable pieces into specialist components and keep the frontier model for exceptions.<\/p>\n\n\n\n<p>One deployment lesson shows up repeatedly in evaluation logs: the expensive failure is often not that the model cannot reason, but that the prompt received the wrong evidence. Retrieval, citation checks, and validation gates usually improve a frontier route faster than swapping to a larger model. A second common routing mistake is letting a frontier model keep doing routine labels after the product already has thousands of examples; those labels should become a classifier, with the frontier path reserved for low-confidence or policy-sensitive cases.<\/p>\n\n\n\n<p>The practical signal is uncertainty. If the router cannot know in advance whether the next step is search, extraction, code execution, database lookup, or human escalation, keep a frontier model in the path. If the same step repeats with the same schema, extract it from the frontier prompt and test it as its own component.<\/p>\n\n\n\n<p><strong>Takeaway:<\/strong> Use frontier models for ambiguity, not for routine work that has already become measurable.<\/p>\n\n\n\n<h2 class='wp-block-heading'>When to Use Specialist AI Models<\/h2>\n\n\n\n<p>Specialist models win when the problem is bounded and the output can be checked without reading a long natural-language answer. In that world, consistency matters more than broad reasoning. The best system may be a classifier plus rules, a retriever plus reranker, a speech-to-text model, a vision moderation model, or an extraction pipeline that only calls a frontier model on conflicts.<\/p>\n\n\n\n<ul class='wp-block-list'><li>High-volume support tagging: route &#8216;billing,&#8217; &#8216;bug,&#8217; &#8216;refund,&#8217; and &#8216;account access&#8217; with a classifier, then send only ambiguous tickets to a frontier model.<\/li><li>Invoice field extraction: use OCR or native PDF parsing first, validate dates and totals with rules, and reserve a frontier model for unreadable scans or conflicting fields.<\/li><li>Search retrieval and reranking: use embeddings and rerankers to select evidence before a frontier model writes an answer.<\/li><li>Transcription: use a speech-to-text system for the transcript, then use a frontier model only for summary, action items, or policy-sensitive interpretation.<\/li><li>Image moderation: use a moderation classifier for the first pass, then escalate borderline images to human review or a stronger multimodal model.<\/li><li>Product matching: use catalog identifiers, embeddings, and rules before asking a frontier model to resolve messy descriptions.<\/li><li>Intent classification: keep the schema small, log low-confidence cases, and retrain or tune the classifier before widening the frontier prompt.<\/li><\/ul>\n\n\n\n<p>These tasks benefit from measurable accuracy, clear latency budgets, and lower operational risk. A specialist system is not weaker if it turns an open-ended generation problem into a contract that can be tested before release.<\/p>\n\n\n\n<p><strong>Takeaway:<\/strong> Specialist AI models are strongest when success can be validated without trusting prose.<\/p>\n\n\n\n<h2 class='wp-block-heading'>When Batch Inference Beats Synchronous Calls<\/h2>\n\n\n\n<p>Model quality is only one production metric. A model can answer well and still be wrong for the workflow if the user is waiting in the UI, if the provider route excludes the feature you need, or if a lower-cost asynchronous endpoint fits the job better. Batch inference is the clearest example: it is often a routing choice, not just a billing trick.<\/p>\n\n\n\n<figure class='wp-block-table'><table><thead><tr><th>Provider path<\/th><th>Evergreen production question<\/th><th>Routing implication<\/th><\/tr><\/thead><tbody><tr><td>OpenAI Batch API<sup><a href='#source-8'>[8]<\/a><\/sup><\/td><td>Can the work wait, and can requests be prepared as a file instead of an interactive call?<\/td><td>Use it for offline evaluations, classification, embeddings, moderation, and bulk generation where the user is not waiting.<\/td><\/tr><tr><td>Anthropic Message Batches API<sup><a href='#source-9'>[9]<\/a><\/sup><\/td><td>Does the Claude-family work belong in a delayed queue rather than the product&#8217;s foreground path?<\/td><td>Use it for high-volume document analysis, moderation, and evaluations that can return later.<\/td><\/tr><tr><td>Google Vertex AI batch inference for Gemini<sup><a href='#source-10'>[10]<\/a><\/sup><\/td><td>Does cost matter more than immediacy, and can the workflow tolerate provider queueing?<\/td><td>Use it for large Gemini-family jobs, but keep interactive user flows on real-time routes.<\/td><\/tr><tr><td>Azure OpenAI batch deployments<sup><a href='#source-11'>[11]<\/a><\/sup><\/td><td>Do Azure deployment, quota, and data-residency requirements decide the route?<\/td><td>Use it when the enterprise platform constraint matters as much as model quality.<\/td><\/tr><tr><td>Amazon Bedrock batch inference<sup><a href='#source-12'>[12]<\/a><\/sup><\/td><td>Does the workload already live in AWS, and should inputs and outputs stay in S3?<\/td><td>Use it when Bedrock model IDs, account limits, Regions, and storage patterns fit the workload.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>The main article intentionally avoids specific discounts, request caps, and file limits because those details age quickly. Keep volatile limits in a maintained comparison page or source appendix, and verify them during capacity planning rather than treating them as architecture constants.<\/p>\n\n\n\n<p>The routing rule is straightforward: synchronous frontier calls are for user-visible judgment; batch paths are for delayed, high-volume work; specialist components are for stable subproblems. If a request can wait for the provider&#8217;s documented batch window, test batch before scaling synchronous traffic.<\/p>\n\n\n\n<p><strong>Takeaway:<\/strong> Batch inference belongs wherever throughput matters more than immediate interaction.<\/p>\n\n\n\n<h2 class='wp-block-heading'>A Practical Evaluation Framework<\/h2>\n\n\n\n<ul class='wp-block-list'><li>Define the task with one primary verb: generate, extract, classify, retrieve, rerank, transcribe, moderate, reason, or call a tool.<\/li><li>Write the expected output contract before choosing the model: JSON fields for extraction, labels for classification, ranked document IDs for retrieval, or a cited answer for reasoning.<\/li><li>Measure cost per approved output, not cost per API call: include retries, empty outputs, human review, failed schema validation, and any second model used for repair.<\/li><li>Use real examples from the workflow, including bad scans, missing fields, long prompts, conflicting policy excerpts, and adversarial user phrasing.<\/li><li>Choose public benchmarks only when they resemble the job; otherwise they are useful background, not acceptance criteria.<\/li><li>Compare latency under the actual route: synchronous API call, prompt-cache hit, retrieval plus generation, or provider batch job.<\/li><li>Check whether failures are detectable: schema validation, confidence score, rule violation, missing citation, unsupported language, or unresolved tool call.<\/li><li>Keep a fallback path: specialist first, frontier on uncertainty, human review on policy-sensitive or irreversible actions.<\/li><\/ul>\n\n\n\n<p>A useful router can be simple. Start with deterministic gates: file type, modality, required latency, expected output format, and whether the answer changes user-visible state. Then add model choice. That order prevents an expensive frontier model from compensating for a routing problem.<\/p>\n\n\n\n<p>For provider comparison, avoid copying one public leaderboard into a spreadsheet and calling it done. A model table should include price, modalities, context window, public benchmarks, endpoint mode, batch availability, data controls, and the shape of tool calling. That is why the internal comparison step belongs before the final architecture review, not after procurement has already picked a brand.<\/p>\n\n\n\n<p>To compare current model prices, context windows, modalities, benchmark fields, and cost estimates while designing a router, use the <a href='https:\/\/aimodels.deepdigitalventures.com\/'>AI model comparison and cost estimator<\/a>.<\/p>\n\n\n\n<p><strong>Takeaway:<\/strong> Evaluate the route as a system, including retries, validation, fallback, and review cost.<\/p>\n\n\n\n<h2 class='wp-block-heading'>Hybrid Architecture Example<\/h2>\n\n\n\n<p>Consider a document review workflow for supplier onboarding. The naive version sends every uploaded PDF directly to the largest frontier model and asks for a risk summary. It works in demos, but it hides parsing failures, repeats the same extraction work, and spends frontier tokens on pages that only need field validation.<\/p>\n\n\n\n<p>A stronger version routes the work in stages. First, parse the PDF and tables outside the model. Second, use retrieval to pull the relevant onboarding policy, sanctions policy, and payment terms. Third, run specialist extraction for supplier name, tax ID, bank account fields, addresses, dates, and invoice terms. Fourth, use a classifier to label the document type and missing-field status. Fifth, send only the selected excerpts, extracted fields, and conflict notes to a frontier model for reasoning. Sixth, run deterministic validation on the final output before the procurement system is updated.<\/p>\n\n\n\n<figure class='wp-block-table'><table><thead><tr><th>Workflow step<\/th><th>Default component<\/th><th>Escalate to frontier model when&#8230;<\/th><\/tr><\/thead><tbody><tr><td>Document parsing<\/td><td>OCR, native PDF parsing, and table extraction<\/td><td>The extracted text conflicts with the visual layout or key fields are unreadable.<\/td><\/tr><tr><td>Evidence selection<\/td><td>Embeddings, keyword search, and reranking<\/td><td>The answer depends on several policy excerpts that disagree or require judgment.<\/td><\/tr><tr><td>Field extraction<\/td><td>Specialist extractor plus schema validation<\/td><td>The same field appears in multiple places with different values.<\/td><\/tr><tr><td>Risk assessment<\/td><td>Frontier model over selected evidence<\/td><td>This is already the judgment step; keep the prompt narrow and cite the evidence.<\/td><\/tr><tr><td>Final approval<\/td><td>Rules layer and human review for exceptions<\/td><td>The action changes payment details, account status, or another irreversible business record.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>This design is better than &#8216;always use the biggest model&#8217; because each component has a reason to exist. The specialist stages reduce noise. The frontier stage handles ambiguity. The rules layer catches format and policy failures. Human review is reserved for cases where automation should not make the final call.<\/p>\n\n\n\n<p><strong>Takeaway:<\/strong> The best architecture is usually not frontier versus specialist; it is frontier plus specialist, with routing rules between them.<\/p>\n\n\n\n<h2 class='wp-block-heading'>FAQ<\/h2>\n\n\n\n<h3 class='wp-block-heading'>Is a frontier model the same as an LLM?<\/h3>\n\n\n\n<p>Not exactly. Most frontier models are large language or multimodal models, but the useful production distinction is not size alone. The question is whether the model is being used as a broad reasoning system or as one component inside a narrower contract.<\/p>\n\n\n\n<h3 class='wp-block-heading'>Can a specialist model be cheaper and more accurate?<\/h3>\n\n\n\n<p>Yes. For invoice fields, support labels, search ranking, transcription, and moderation, a specialist path can be cheaper, faster, and easier to validate because the output is constrained and the failure mode is easier to measure.<\/p>\n\n\n\n<h3 class='wp-block-heading'>What should teams log before changing models?<\/h3>\n\n\n\n<p>Log the route chosen, prompt size, selected evidence, schema failures, confidence signals, retries, latency, cost, fallback use, and human review outcome. Without those fields, a model replacement can hide a routing problem instead of fixing it.<\/p>\n\n\n\n<p><strong>Methodology and production note:<\/strong> This article was produced from provider documentation, public benchmark references, Google guidance on helpful content, AI features, Article structured data, and DDV deployment-review patterns. It does not add FAQ schema because the FAQ is included for readers, not search decoration.<sup><a href='#source-13'>[13]<\/a><\/sup><sup><a href='#source-14'>[14]<\/a><\/sup><sup><a href='#source-15'>[15]<\/a><\/sup><sup><a href='#source-16'>[16]<\/a><\/sup><\/p>\n\n\n\n<script type='application\/ld+json'>{\"@context\":\"https:\/\/schema.org\",\"@type\":\"BlogPosting\",\"headline\":\"When to Use Frontier Models vs Specialist AI Models\",\"description\":\"A practical routing framework for choosing general-purpose frontier models, specialist AI models, or batch inference in production AI systems.\",\"author\":{\"@type\":\"Organization\",\"name\":\"Deep Digital Ventures\",\"url\":\"https:\/\/deepdigitalventures.com\/\"},\"publisher\":{\"@type\":\"Organization\",\"name\":\"Deep Digital Ventures\",\"url\":\"https:\/\/deepdigitalventures.com\/\"},\"dateModified\":\"2026-04-24\",\"about\":[\"frontier models\",\"specialist AI models\",\"batch inference\",\"AI routing\"]}<\/script>\n\n\n\n<h2 class='wp-block-heading'>Sources<\/h2>\n\n\n\n<ol class='wp-block-list'><li id='source-1'>OpenAI function calling guide &#8211; <a href='https:\/\/platform.openai.com\/docs\/guides\/function-calling'>https:\/\/platform.openai.com\/docs\/guides\/function-calling<\/a><\/li><li id='source-2'>Anthropic tool use guide &#8211; <a href='https:\/\/docs.anthropic.com\/en\/docs\/build-with-claude\/tool-use'>https:\/\/docs.anthropic.com\/en\/docs\/build-with-claude\/tool-use<\/a><\/li><li id='source-3'>MMLU paper &#8211; <a href='https:\/\/arxiv.org\/abs\/2009.03300'>https:\/\/arxiv.org\/abs\/2009.03300<\/a><\/li><li id='source-4'>GPQA paper &#8211; <a href='https:\/\/arxiv.org\/abs\/2311.12022'>https:\/\/arxiv.org\/abs\/2311.12022<\/a><\/li><li id='source-5'>SWE-bench benchmark &#8211; <a href='https:\/\/www.swebench.com\/'>https:\/\/www.swebench.com\/<\/a><\/li><li id='source-6'>HumanEval paper &#8211; <a href='https:\/\/arxiv.org\/abs\/2107.03374'>https:\/\/arxiv.org\/abs\/2107.03374<\/a><\/li><li id='source-7'>LMArena model comparison arena &#8211; <a href='https:\/\/lmarena.ai\/'>https:\/\/lmarena.ai\/<\/a><\/li><li id='source-8'>OpenAI Batch API guide for current batch behavior, pricing notes, and limits &#8211; <a href='https:\/\/platform.openai.com\/docs\/guides\/batch'>https:\/\/platform.openai.com\/docs\/guides\/batch<\/a><\/li><li id='source-9'>Anthropic Message Batches API documentation for current batch behavior, pricing notes, and limits &#8211; <a href='https:\/\/docs.anthropic.com\/en\/docs\/build-with-claude\/batch-processing'>https:\/\/docs.anthropic.com\/en\/docs\/build-with-claude\/batch-processing<\/a><\/li><li id='source-10'>Google Vertex AI Gemini batch prediction documentation for current batch behavior, pricing notes, and limits &#8211; <a href='https:\/\/cloud.google.com\/vertex-ai\/generative-ai\/docs\/multimodal\/batch-prediction-gemini'>https:\/\/cloud.google.com\/vertex-ai\/generative-ai\/docs\/multimodal\/batch-prediction-gemini<\/a><\/li><li id='source-11'>Microsoft Azure OpenAI batch deployment documentation for current batch behavior, pricing notes, and limits &#8211; <a href='https:\/\/learn.microsoft.com\/en-us\/azure\/ai-services\/openai\/how-to\/batch'>https:\/\/learn.microsoft.com\/en-us\/azure\/ai-services\/openai\/how-to\/batch<\/a><\/li><li id='source-12'>Amazon Bedrock batch inference documentation for current batch behavior and constraints &#8211; <a href='https:\/\/docs.aws.amazon.com\/bedrock\/latest\/userguide\/batch-inference.html'>https:\/\/docs.aws.amazon.com\/bedrock\/latest\/userguide\/batch-inference.html<\/a><\/li><li id='source-13'>Google guidance on creating helpful content &#8211; <a href='https:\/\/developers.google.com\/search\/docs\/fundamentals\/creating-helpful-content'>https:\/\/developers.google.com\/search\/docs\/fundamentals\/creating-helpful-content<\/a><\/li><li id='source-14'>Google guidance on AI features in Search &#8211; <a href='https:\/\/developers.google.com\/search\/docs\/appearance\/ai-features'>https:\/\/developers.google.com\/search\/docs\/appearance\/ai-features<\/a><\/li><li id='source-15'>Google Article structured data guidance &#8211; <a href='https:\/\/developers.google.com\/search\/docs\/appearance\/structured-data\/article'>https:\/\/developers.google.com\/search\/docs\/appearance\/structured-data\/article<\/a><\/li><li id='source-16'>Google FAQ structured data limitations &#8211; <a href='https:\/\/developers.google.com\/search\/docs\/appearance\/structured-data\/faqpage'>https:\/\/developers.google.com\/search\/docs\/appearance\/structured-data\/faqpage<\/a><\/li><\/ol>\n","protected":false},"excerpt":{"rendered":"<p>A frontier model is a general-purpose AI system built to handle many kinds of prompts: reasoning, writing, code, multimodal inputs, tool calls, and long-context synthesis. A specialist model is narrower: it is trained, tuned, or wrapped to do one job well, such as extraction, transcription, moderation, ranking, or classification. By the end of this article, [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":1885,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_seopress_robots_primary_cat":"","_seopress_titles_title":"When to Use Frontier Models vs Specialist AI Models","_seopress_titles_desc":"A practical AI routing framework for choosing frontier models, specialist AI models, or batch inference based on task shape, latency, cost, and risk.","_seopress_robots_index":"","footnotes":""},"categories":[12],"tags":[],"class_list":["post-1266","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-comparisons"],"_links":{"self":[{"href":"https:\/\/aimodels.deepdigitalventures.com\/blog\/wp-json\/wp\/v2\/posts\/1266","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/aimodels.deepdigitalventures.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/aimodels.deepdigitalventures.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/aimodels.deepdigitalventures.com\/blog\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/aimodels.deepdigitalventures.com\/blog\/wp-json\/wp\/v2\/comments?post=1266"}],"version-history":[{"count":5,"href":"https:\/\/aimodels.deepdigitalventures.com\/blog\/wp-json\/wp\/v2\/posts\/1266\/revisions"}],"predecessor-version":[{"id":2104,"href":"https:\/\/aimodels.deepdigitalventures.com\/blog\/wp-json\/wp\/v2\/posts\/1266\/revisions\/2104"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/aimodels.deepdigitalventures.com\/blog\/wp-json\/wp\/v2\/media\/1885"}],"wp:attachment":[{"href":"https:\/\/aimodels.deepdigitalventures.com\/blog\/wp-json\/wp\/v2\/media?parent=1266"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/aimodels.deepdigitalventures.com\/blog\/wp-json\/wp\/v2\/categories?post=1266"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/aimodels.deepdigitalventures.com\/blog\/wp-json\/wp\/v2\/tags?post=1266"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}