Accept Markdown for AI Agents: The August 2026 Workflow Developers Should Add Now

On August 27, 2026, the “Accept Markdown” workflow moved from clever optimization to practical infrastructure pattern. Developers, founders, and content teams are starting to serve clean text/markdown or Markdown-like variants of web pages when an AI agent requests them through the HTTP Accept header. The result is simple: the same URL can serve polished HTML to humans and compact, structured Markdown to agents.

The market cares because AI agents are no longer just scraping pages for summaries. They are comparing vendors, reading docs, generating implementation plans, answering support questions, creating procurement briefs, and operating browser tools. If your site only exposes dense HTML, cookie banners, navigation, scripts, and repeated layout text, agents spend extra tokens just extracting the page. If your site can return the article, product page, pricing table, documentation, or FAQ in Markdown, retrieval gets cleaner and agent runs get cheaper.

This post shows what changed, why Accept Markdown matters now, what teams can build with it, which agent stacks can use it today, and how to estimate the model cost impact. You will get copyable implementation outlines, model recommendations, cheaper fallbacks, and a practical rollout plan for content sites, SaaS docs, ecommerce catalogs, and internal knowledge bases.

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What changed on August 27, 2026

The technical idea behind content negotiation is old: clients send an Accept header, servers choose the best response format, and everyone avoids format-specific URLs when the resource is conceptually the same. What changed in August 2026 is demand. AI agents now behave like automated readers, not traditional crawlers. They need concise, high-signal content that preserves headings, links, tables, code blocks, citations, and product facts.

The Accept Markdown workflow gained traction because three pressures converged:

1. Agent traffic is becoming commercially valuable. AI assistants are recommending products, summarizing pages, and feeding buying workflows.
2. HTML is expensive for LLMs to read. Navigation, menus, footers, CSS class noise, inline scripts, consent banners, and hidden UI text add tokens that do not improve reasoning.
3. Retrieval quality determines agent usefulness. A clean Markdown version gives retrieval pipelines better chunks, fewer duplicates, and more predictable metadata.

The core mechanism is straightforward. A browser might request:

Accept: text/html,application/xhtml+xml

An agent, crawler, or retrieval service can request:

Accept: text/markdown,text/plain;q=0.9,text/html;q=0.8

Your server can then return:

Content-Type: text/markdown; charset=utf-8
Vary: Accept

That lets the same URL expose two representations: HTML for visual browsing and Markdown for machine reading. The canonical URL stays stable, links remain natural, and your analytics can identify agent-readable requests without building an entirely separate “AI site.”

Why AI agents prefer Markdown over HTML

Markdown is not magical. Its advantage is signal density. It keeps the semantic pieces agents need and removes the parts that usually pollute retrieval.

A well-prepared Markdown page preserves:

• The H1 title and heading hierarchy
• Paragraphs in reading order
• Tables as structured Markdown tables
• Code examples in fenced code blocks
• Links with visible anchor text
• Image alt text and captions
• Product specs, prices, and availability
• FAQ questions as headings
• Source metadata at the top or bottom

It removes or reduces:

• Header navigation repeated on every page
• Footer link farms
• Cookie banners and modals
• Tracking scripts
• CSS class names
• Responsive layout wrappers
• Hidden mobile menus
• Duplicate sidebar content
• SVG/icon markup
• Boilerplate legal text unless relevant

For human visitors, HTML is the product surface. For agents, Markdown is the reading surface. The best implementation keeps both aligned and generated from the same content source so the AI-readable variant never drifts from the public page.

[stat] 40-70% fewer input tokens Typical reduction when an agent reads clean Markdown instead of full production HTML with navigation, scripts, repeated layout, and boilerplate.

That token reduction directly affects model cost. More importantly, it reduces context distraction. An agent asked to compare your pricing, explain an API, or summarize a policy should spend its context window on the actual page, not on JavaScript bundles and navigation text.

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7 practical things teams can build with Accept Markdown

Accept Markdown is useful because it turns your site into agent-friendly infrastructure. Here are seven concrete workflows developers, founders, and content teams can ship now.

1. Agent-readable documentation

Developer docs are the clearest use case. Serve Markdown versions of API references, SDK guides, changelogs, and tutorials. Coding agents can retrieve exact snippets, parameters, and examples without stripping docs layout.

Use this for:

• API docs
• SDK migration guides
• CLI references
• Integration tutorials
• Error code catalogs
• Release notes

If your product depends on developers implementing correctly, Markdown docs reduce support load and improve agent-assisted onboarding.

2. AI-ready product and pricing pages

Founders and growth teams should expose Markdown versions of pricing, feature comparisons, limits, SLAs, and procurement content. Agents increasingly answer questions like “Which product should I choose?” or “Write a vendor comparison for this feature set.”

Markdown variants make it easier for agents to extract:

• Plan names
• Price points
• Feature availability
• Usage limits
• Contract requirements
• Security claims
• Support tiers

For cost-sensitive buyers, accurate AI-readable pricing pages can determine whether your product appears in automated shortlist workflows.

3. Better retrieval for your own RAG system

Many companies crawl their public site into their own support bot or sales assistant. If your crawler ingests HTML, you are paying twice: once in preprocessing complexity and again in noisy embeddings.

Serving Markdown lets your RAG pipeline crawl the same canonical URLs with an Accept: text/markdown header. Chunks become cleaner, embeddings become more representative, and answers cite the right source.

4. Content syndication without duplicate pages

Content teams often create separate .md, .txt, or “llms” pages. That works, but it creates another URL layer to maintain. Accept Markdown keeps the human page and machine page attached to the same resource.

This is especially useful for:

• Technical blogs
• Research libraries
• Help centers
• Product changelogs
• Policy hubs
• Knowledge bases

You can still provide index files or feed manifests, but the page-level content negotiation keeps source-of-truth management cleaner.

5. Browser agent acceleration

Browser agents can interact with rendered pages, but visual browsing is slower and more failure-prone than direct text retrieval. If an agent is trying to understand the page before clicking, a Markdown representation can act as a fast “read mode.”

A strong browser-agent stack can:

1. Fetch Markdown for understanding.
2. Use extracted links and action descriptions.
3. Switch to browser automation only when interaction is required.
4. Use screenshots or DOM inspection for final confirmation.

That separation lowers cost and reduces brittle automation.

6. Compliance and policy review workflows

Legal, privacy, and compliance pages are often bloated with layout and repeated definitions. Markdown variants make policy review agents more reliable because they receive stable text with headings and clauses intact.

Build workflows for:

• Terms-of-service diffing
• Privacy policy monitoring
• Vendor security review
• Accessibility statement extraction
• Data retention policy comparison
• Regulatory evidence packs

Markdown also makes redline-style comparison easier because the source is text-first.

7. Sales and support enablement

Internal sales agents can retrieve public docs, pricing, case studies, and FAQ pages from Markdown endpoints, then generate responses grounded in current website content. Support teams can do the same for help articles and troubleshooting trees.

The value is operational: fewer stale enablement docs, fewer incorrect agent answers, and lower token cost per customer interaction.

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Step-by-step workflow 1: Add Markdown content negotiation to a docs site

This workflow is for developer documentation, SaaS help centers, and technical blogs. The goal is to serve HTML to browsers and Markdown to agents from the same route.

Step 1: Choose the canonical content source

Start with one source of truth. The easiest stack is Markdown or MDX as the source format, rendered to HTML for humans and returned directly or transformed for agents.

Recommended source patterns:

Source system	Good fit	Markdown serving approach
MDX docs	Developer docs and blogs	Strip components or render component fallbacks
Headless CMS	Marketing and help centers	Convert rich text blocks to Markdown
Database pages	Product catalogs and directories	Template structured fields into Markdown
Static site generator	Blogs and docs	Use original Markdown source where possible
HTML-only legacy site	Existing marketing site	Use extraction pipeline, then cache Markdown

If you already have MDX, the implementation is mostly routing. If you use a CMS, build a serializer that maps headings, paragraphs, links, lists, tables, callouts, and code blocks into Markdown.

Step 2: Detect Markdown requests

At the route layer, inspect the Accept header. Treat text/markdown as the preferred signal, with text/plain as a fallback for agents that do not yet send Markdown explicitly.

Example matching logic:

function wantsMarkdown(request) {
  const accept = request.headers.get("accept") || "";
  return accept.includes("text/markdown") || accept.includes("text/plain");
}

Do not serve Markdown to every bot by default. Some crawlers expect HTML, and some SEO tooling will misinterpret non-HTML responses. Content negotiation should be explicit.

Step 3: Return the correct headers

Use Content-Type and Vary correctly:

Content-Type: text/markdown; charset=utf-8
Vary: Accept
Cache-Control: public, max-age=300, stale-while-revalidate=86400

Vary: Accept tells caches that the same URL has multiple variants. Without it, a CDN can accidentally serve Markdown to a browser or HTML to an agent.

Step 4: Add machine-readable metadata

At the top of the Markdown response, include a compact metadata block. Keep it factual and short.

---
title: "API Rate Limits"
canonical_url: "https://example.com/docs/rate-limits"
updated: "2026-08-27"
section: "Developer docs"
---

# API Rate Limits

This helps retrieval systems cite the right URL and identify stale pages. Do not stuff keywords into metadata; agents benefit from precision.

Step 5: Preserve tables, code, and links

Agents rely heavily on tables and code blocks in technical docs. Make sure the Markdown variant keeps them intact.

Good:

| Plan | Requests per minute | Burst limit |
|---|---:|---:|
| Starter | 60 | 120 |
| Pro | 600 | 1,200 |
| Enterprise | Custom | Custom |

Bad:

Starter 60 120 Pro 600 1200 Enterprise Custom Custom

The first version is retrievable, comparable, and citeable. The second forces the model to infer structure.

Step 6: Test with an agent-style fetch

Test the live route:

curl -H "Accept: text/markdown" https://example.com/docs/rate-limits

Then run the Markdown through your retrieval or summarization model. Compare it with the HTML version using the same prompt:

Extract rate limits, burst limits, plan names, and upgrade instructions.
Return a table and cite the source URL.

Measure token count, answer accuracy, and citation quality.

Step 7: Roll out by page type

Do not convert the entire site on day one. Prioritize pages with high agent value:

1. Docs and API reference
2. Pricing and plan comparison
3. Product pages
4. Help center articles
5. Changelogs
6. Policy and security pages
7. Blog posts and guides

This sequence gives immediate value to developer agents, buyer agents, and internal RAG systems.

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Step-by-step workflow 2: Build a retrieval pipeline that prefers Markdown

This workflow is for teams running their own RAG system, sales assistant, support bot, or research agent. The goal is to fetch Markdown when available and fall back gracefully.

Step 1: Crawl with an Accept preference

Configure your crawler to request Markdown first:

Accept: text/markdown,text/plain;q=0.9,text/html;q=0.8

If the server returns Content-Type: text/markdown, store that as the primary content. If it returns HTML, run your existing extraction pipeline.

Step 2: Store representation metadata

For each document, store:

Field	Example
canonical_url	https://example.com/docs/authentication
content_type	text/markdown
retrieved_at	2026-08-27T12:00:00Z
etag	CDN or origin ETag
last_modified	HTTP date
source_title	Page title
token_count	Count after cleaning
hash	Content hash for diffing

This lets you compare retrieval quality across Markdown and HTML versions and avoid re-embedding unchanged pages.

Step 3: Chunk by headings, not arbitrary length

Markdown gives you natural boundaries. Chunk with heading context:

# Authentication
## API keys
## OAuth
## Token expiration
## Rotation best practices

Each chunk should include parent headings so a retrieved section remains understandable outside the full document.

Good chunk header:

Source: https://example.com/docs/authentication
Title: Authentication
Section: OAuth > Token expiration

Step 4: Use a cheap model for cleanup, not a premium model

If the Markdown is already clean, do not use a top-tier reasoning model for preprocessing. Use a cheap fast model for normalization, table repair, or metadata extraction.

Good candidates:

• GPT-5 nano for very cheap cleanup at $0.05 input / $0.40 output per 1M tokens
• Gemini 2.5 Flash-Lite at $0.10 input / $0.40 output per 1M tokens
• Mistral Small 4 at $0.15 input / $0.60 output per 1M tokens
• DeepSeek V4 Flash at $0.14 input / $0.28 output per 1M tokens

Save premium models for judgment-heavy tasks like policy interpretation, competitive analysis, or multi-document synthesis.

Step 5: Embed and retrieve

Use your embedding pipeline after Markdown cleanup. Preserve source URLs and section headings in metadata. At answer time, send only the relevant chunks to the generation model.

Markdown improves this stage because chunks have fewer layout artifacts and better semantic boundaries.

Step 6: Add answer validation

For high-stakes workflows, run a second pass that checks whether every claim is supported by retrieved chunks. This is where stronger models are useful.

Recommended validators:

• GPT-5.2 for balanced long-context validation at $1.75 input / $14 output per 1M tokens
• Claude Sonnet 5 for strong document review at $2 input / $10 output per 1M tokens
• Gemini 3 Pro when you need a 2,000,000-token context window at $2 input / $12 output per 1M tokens

Step 7: Track token savings by source

For every crawled page, store HTML token count and Markdown token count. Your dashboard should show:

• Average token reduction
• Retrieval hit rate
• Answer citation accuracy
• Pages with broken Markdown
• Pages with stale metadata
• Cost per 1,000 answers

This gives founders and content teams a business case for maintaining the Markdown layer.

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Which agent stacks support Accept Markdown today

Support does not require a special proprietary protocol. Any stack that can set HTTP headers can use Accept Markdown. The difference is whether the tool exposes header control cleanly and whether the agent can choose direct fetching before browser automation.

Stack or tool type	Accept header support	Best use
Custom Python/Node agents	Yes	Full control over fetch, cache, retrieval, and routing
LangChain-style retrieval pipelines	Yes through loaders or custom fetchers	RAG ingestion and agent tools
LlamaIndex-style document loaders	Yes with custom readers	Knowledge base ingestion
Browser automation agents	Partial to yes	Fetch Markdown first, browse only for actions
Server-side crawlers	Yes	Docs, pricing, and content ingestion
Headless CMS webhooks	Yes indirectly	Generate cached Markdown variants
No-code agent builders	Mixed	Depends on whether HTTP request headers are configurable
Chat-only assistants	Mixed	Often cannot set headers directly without a tool layer

The most reliable architecture is a custom fetch tool exposed to your agent:

Tool: fetch_readable_url
Input: URL
Behavior:
- Request Accept: text/markdown,text/plain;q=0.9,text/html;q=0.8
- If Markdown is returned, pass it through
- If HTML is returned, extract main content
- Return title, canonical URL, content type, token count, and content

This lets any model use the same retrieval behavior. The model does not need native Accept Markdown awareness; the tool layer handles it.

For browser agents, make Markdown the “read” path and the browser the “act” path. The agent reads clean Markdown to understand the site, then uses the browser only to click, submit forms, authenticate, or verify visual state.

Model Choice and Cost

Accept Markdown reduces input tokens, but model choice still determines whether the workflow is cheap enough to run at scale. The right stack separates fetching, cleanup, retrieval, synthesis, and validation instead of sending every page to a premium model.

Recommended model stack

Workflow stage	Recommended model	Price per 1M tokens	Why
Markdown cleanup	GPT-5 nano	$0.05 in / $0.40 out	Cheapest OpenAI cleanup tier
Low-cost extraction	DeepSeek V4 Flash	$0.14 in / $0.28 out	Very low output cost
Support answer generation	GPT-5 mini	$0.25 in / $2 out	Strong middle-ground for volume
Long docs synthesis	Gemini 3 Pro	$2 in / $12 out	2M context for large corpora
Premium validation	Claude Sonnet 5	$2 in / $10 out	Strong document reasoning
High-stakes analysis	GPT-5.2 pro	$21 in / $168 out	Reserve for expensive expert review

For most Accept Markdown workflows, the premium model is overkill. Do not use GPT-5.2 pro, GPT-5 Pro, or Claude Fable 5 to clean pages, chunk Markdown, extract FAQs, or summarize individual help articles. Use premium models only when the answer has legal, security, financial, or enterprise-sales consequences.

Example cost per agent read

Assume an agent reads a page and produces a concise structured summary.

HTML path:

• Input: 18,000 tokens
• Output: 800 tokens

Markdown path:

• Input: 7,000 tokens
• Output: 800 tokens

Using GPT-5 mini at $0.25 input / $2 output per 1M tokens:

Path	Input cost	Output cost	Cost per read	Cost per 100,000 reads
HTML	$0.0045	$0.0016	$0.0061	$610
Markdown	$0.00175	$0.0016	$0.00335	$335

That is $275 saved per 100,000 page reads on GPT-5 mini. The savings grow when agents read multiple pages per task or use more expensive models.

Premium model cost comparison

If a high-stakes research agent uses Claude Sonnet 5 at $2 input / $10 output per 1M tokens, the same token reduction matters more:

Path	Input tokens	Output tokens	Cost per read	Cost per 100,000 reads
HTML	18,000	800	$0.044	$4,400
Markdown	7,000	800	$0.022	$2,200

The output cost stays the same, but input savings cut total read cost in half. Use the AI Cost Check calculator to plug in your own token counts, model choice, and monthly volume.

Cheaper fallbacks

For high-volume ingestion, the best fallback models are:

• GPT-5 nano: best for metadata extraction and formatting checks.
• Gemini 2.0 Flash-Lite: $0.075 input / $0.30 output per 1M tokens, useful for cheap extraction at scale.
• Mistral Small 3.2: $0.10 input / $0.30 output per 1M tokens, strong budget option for classification.
• DeepSeek V4 Flash: best low-cost output-heavy cleanup at $0.28 output per 1M tokens.

For generation quality at reasonable cost, GPT-5 mini, Claude Haiku 4.5, and Gemini 3.7 Flash are better fits than premium reasoning models. If you are comparing broader model options, start with GPT-5 vs Claude Sonnet 4.5 and GPT-5 vs Gemini 3 Pro.

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Implementation patterns for developers

There are three practical ways to ship Accept Markdown. Pick based on your stack.

Pattern 1: Source Markdown served directly

Best for docs, blogs, and static sites. Your .md or .mdx files are the source. The HTML route renders the content, and the Markdown variant returns the source after removing unsupported components.

Pros:

• Lowest maintenance
• Strong fidelity
• Easy diffing
• Great for developer docs

Cons:

• MDX components need text fallbacks
• Marketing pages often live outside Markdown

Pattern 2: CMS rich text serialized to Markdown

Best for content teams using a headless CMS. The CMS stores structured blocks, and your server serializes those blocks to Markdown when requested.

Pros:

• Editors keep existing workflow
• Structured content maps well to Markdown
• Works for help centers and product pages

Cons:

• Requires a serializer
• Complex custom blocks need careful handling
• Tables and nested content need QA

Pattern 3: HTML extraction with caching

Best for legacy sites. Generate Markdown from the rendered HTML or server-side HTML, then cache it.

Pros:

• Fastest retrofit
• No CMS migration
• Works across mixed page types

Cons:

• More fragile
• Extraction can miss hidden-but-important content
• Requires monitoring when templates change

For most teams, start with Pattern 1 for docs and Pattern 2 for content. Use Pattern 3 only when you cannot access structured content.

What to include in the Markdown response

A useful Markdown representation should be concise but complete. Include the content needed to answer questions accurately.

Use this checklist:

Element	Include?	Notes
Title	Yes	Use one H1
Canonical URL	Yes	Metadata block or footer
Last updated date	Yes	Critical for agents
Headings	Yes	Preserve hierarchy
Body text	Yes	Main content only
Tables	Yes	Use Markdown tables
Code blocks	Yes	Use fenced code with language
Links	Yes	Preserve destination URLs
Images	Sometimes	Include alt text and captions
Navigation	No	Exclude global nav
Footer boilerplate	No	Exclude unless page-specific
Cookie banners	No	Never useful
Structured data	Sometimes	Include if it adds facts not visible on page

For pricing pages, include plan names, prices, limits, feature rows, billing notes, and upgrade instructions. For docs, include endpoints, parameters, response examples, errors, and version notes. For blog posts, include the article body, key tables, cited links, and author/update metadata.

Risks, limits, and when not to use Accept Markdown

Accept Markdown is powerful, but it introduces operational risks if treated casually.

Risk 1: Content drift

If the Markdown variant is generated separately, it can become stale. Avoid separate hand-maintained AI pages unless you have a publishing workflow that updates both. The safest pattern is one source, two renderers.

Risk 2: Cache mistakes

As noted earlier, Vary: Accept is mandatory. Also test CDN behavior, edge functions, static rendering, and preview deployments.

Risk 3: SEO confusion

Search engines generally expect HTML for normal crawling. Do not redirect humans or default crawlers to Markdown. Serve Markdown only when requested through Accept.

Risk 4: Sensitive content leakage

If your Markdown serializer bypasses UI-level filtering, it can expose draft fields, internal notes, hidden CMS blocks, or gated content. Treat the Markdown route as a public representation of the page and apply the same authorization rules as HTML.

Risk 5: Over-optimization for agents

Do not remove important nuance to save tokens. A short page that omits caveats, eligibility, warnings, or constraints can cause worse agent answers. The goal is clean content, not thin content.

Do not use Accept Markdown for pages where the main value is visual interaction: design portfolios, interactive calculators, canvas apps, dashboards, maps, or pages where state is generated only after authentication. For those, expose a separate documented API or structured export.

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Measurement plan: prove it improves retrieval

A rollout should include measurement, not just implementation. Track both technical and business metrics.

Technical metrics

Metric	Target
Markdown token reduction	40%+ vs HTML
Broken Markdown pages	<1%
Missing title or canonical URL	0%
Retrieval chunk duplication	Down 30%+
Answer citation accuracy	Up 15%+
Ingestion reprocessing cost	Down 25%+

Business metrics

Team	Metric
Developer relations	Fewer implementation support tickets
Sales	More accurate AI-generated vendor comparisons
Support	Higher self-serve answer accuracy
Content	Better reuse in newsletters, docs, and agents
Product	Cleaner onboarding for coding assistants
Security/legal	Faster policy and evidence review

Run an A/B evaluation with the same set of pages. Feed HTML-derived chunks to your agent for one test set and Markdown-derived chunks for another. Ask factual questions with known answers, then grade exactness, citation quality, and unsupported claims. This gives leadership a measurable reason to prioritize the work.

Recommended rollout plan

Ship Accept Markdown in four phases.

Phase 1: Docs and high-intent pages

Start with 20-50 pages: top docs, pricing, product comparison, and security pages. These have the highest value for agents and the clearest structure.

Phase 2: Internal RAG ingestion

Update your own crawler to request Markdown first. Re-embed the pilot pages and compare answer quality against the old index.

Phase 3: Agent tool support

Add a fetch_readable_url tool to your internal agents. Make it prefer Markdown, record content type, and return token count.

Phase 4: Sitewide expansion

Expand to help center, blog, changelog, and policy pages. Add monitoring to catch formatting regressions when templates change.

A good target is to have all docs and pricing pages serving Markdown within 30 days, then expand to the rest of your content library over the next 60-90 days.

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Frequently asked questions

What is the Accept Markdown workflow?

Accept Markdown is a content negotiation pattern where the same URL serves normal HTML to browsers and Markdown to agents that send Accept: text/markdown. It makes pages easier for AI systems to read by preserving headings, links, tables, and code while removing navigation, scripts, and layout noise.

How much money can Accept Markdown save?

A typical page can use 40-70% fewer input tokens when served as Markdown instead of full HTML. In the example above, 100,000 GPT-5 mini page reads dropped from $610 to $335, saving $275 before counting multi-page agent workflows.

Which agent stacks support Accept Markdown?

Any custom Python, Node, LangChain-style, LlamaIndex-style, crawler, or browser-agent stack that can set HTTP headers can support it today. No-code agent builders are mixed; use them only if their HTTP tools let you configure the Accept header.

Should I create separate .md pages instead?

Use separate .md pages only if content negotiation is hard in your stack. The better default is one canonical URL with Vary: Accept, because it keeps links, citations, analytics, and content governance attached to the same resource.

Which model should I use for Markdown-based retrieval?

Use cheap models like GPT-5 nano, DeepSeek V4 Flash, or Gemini 2.5 Flash-Lite for cleanup and extraction. Use GPT-5 mini, Claude Sonnet 5, or Gemini 3 Pro for answer generation and validation when quality matters.

CTA: make your site easier for agents to read

If your docs, pricing pages, or help center are part of how customers evaluate your product, add Accept Markdown now. Start with your top 20 pages, serve text/markdown only when requested, include Vary: Accept, and measure token reduction against your HTML baseline.

Use AI Cost Check to estimate how much your retrieval and agent workflows cost before and after Markdown. For model selection, compare GPT-5 vs Gemini 3 Pro, review GPT-5 mini for volume workloads, and use Claude Sonnet 5 when document reasoning quality matters more than the lowest possible price.