Head to head · Document parsing · October 2026 research run

Docling vs LlamaParse API + MCP

Docling scores 64.3 (B) on agent readiness against LlamaParse API + MCP's 62.2 (B), and leads in 4 of 7 scored categories. LlamaParse API + MCP leads on schema & documentation. Both do document parsing.

Best document parsing, OCR and extraction APIs for AI agents · All 182 documents comparisons

Which one, for what

Docling B

Best for Self-hosted PDF and Office conversion to Markdown or JSON for RAG and agents that can't send documents out.

Ahead on

  • Reliability, 85 against 80
  • Agent ergonomics, 77 against 72
  • Payments & pricing, 60 against 40
  • Maintenance & community, 89 against 80

Also in its favour

  • No key needed to call it
  • Runs on your own machine
  • Open source

Watch for

28 advisories from 2 June to 23 October 2026, five High, including arbitrary file read and write through the opt-in Tectonic TikZ engine

LlamaParse API + MCP B

Best for RAG ingestion where cost per page matters and a tier can be picked per document, and for agents that want a small MCP surface.

Ahead on

  • Schema & documentation, 82 against 77

Also in its favour

  • A hosted endpoint, with nothing to install

Watch for

Breaking SDK changes shipped as minor releases (files.get renamed, classify v1 removed)

Why the edge

Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.

Payments & pricing Docling +20

Docling 60

Self-hosted rule. Free MIT software with nothing to buy, so 20 + 20 + 20, and no payment protocol (0). 60.

LlamaParse API + MCP 40

No x402, MPP or L402 (0). Credits cost $1.50 per 1,000 on Pay as you go and $1.25 on Pro ($500 a month with 400,000 credits), and credits per page per tier are published in the docs (20). Pay as you go has no subscription and 10,000 free credits a month, and the billing and pricing pages ask for a card only to buy credits or move to Pro (20). A person signs up in a browser (0).

Maintenance & community Docling +9

Docling 89

v2.148.0 on 5 November 2026, the day before this check (30). 13 releases from 9 October to 5 November (20). Open issues and pull requests are numerous, an AI triage workflow and PR reminders run, and six maintainers are named; replies not sampled (17 of 25). docling-mcp publishes to the official MCP registry on release under io.github.docling-project, per its workflow and server.json; the registry entry itself was not read (12 of 15). CI, a dependency-update workflow and uv.lock (10). 30+20+17+12+10 = 89.

LlamaParse API + MCP 80

Python and TypeScript SDK v2.19.0 tagged on 21 October 2026, 14 days ago, and the newest changelog entry is 17 October (30). SDK tags 2.14.0, 2.14.1, 2.15.0, 2.16.0, 2.17.0, 2.18.0 and 2.19.0 since 6 August, and 26 dated changelog entries from 6 August to 17 October (20). Public changelog and GitHub SDK repositories, issue replies not sampled (10 of 15). Current official SDKs in Python, TypeScript, Go, Java and .NET (15). Release-please, CI and test workflows, but 2.14.1 had to restore API surface deleted by a bad regeneration, and two breaking changes shipped as minor versions (5 of 10).

Reliability Docling +5

Docling 85

Local-software lines, graded on the library with docling-serve and docling-mcp. Installs from PyPI (docling 2.148.0, per the PyPI release feed) for Python 3.10 and later on macOS, Linux and Windows (20). Public CI workflows for checks, tests and heavy examples; the pass state of the default branch was not read (20 of 25). About 719 open issues and 223 open pull requests on the repository page against 13 releases in four weeks, many of them fixes; replies not sampled (15 of 25). Semantic-release changelog with Feature and Fix sections and Breaking sections where they apply (15). Version 2.x since 2024 (15). 20+20+15+15+15 = 85.

LlamaParse API + MCP 80

Statuspage at llamaindex.statuspage.io with LlamaParse (Global) and LlamaParse (EU) groups, each with API and UI components, and a Docs component (20). The history pages, read on 4 November, list two incidents between 6 August and 4 November, 400 errors for some extract schemas on 29 September (14:19 to 16:04 UTC) and a high error rate for agentic parse in the EU on 17 September (18:48 to 20:30 UTC, labelled major, affecting only the EU API). Both are partial, so minor only (20). The rate-limits page gives per-endpoint caps per organisation, the same on every plan, such as 500 parse jobs per 10 seconds, 50 uploads a second per project and 80 reads or 40 writes a second elsewhere (15). It documents the 429 body, says no Retry-After header is sent, and gives exponential backoff with jitter, with the SDKs retrying 429 and 5xx five times by default. No idempotency key or safe-retry guidance for job creation (10). The pricing table lists 99.9% uptime as a platform line, and the Support & SLAs page says contractual uptime commitments are negotiated in Enterprise agreements, with no SLA terms published (5 of 10). Parse and Extract are GA, with Turbo Extract in beta (10).

Schema & documentation LlamaParse API + MCP +5

Docling 77

No OpenAPI file is published; docling-serve is a FastAPI app that serves its schema when run, and docling-mcp's tools carry typed inputs with Field descriptions from Pydantic (20 of 25). No llms.txt on the docs site; Markdown docs and AGENTS.md in the repository (5 of 10). MCP tool docstrings say what each tool returns and which other tool supplies its key, with little on when not to use one (14 of 20). Pydantic types with descriptions, few enums (12 of 15). Many runnable examples in docs/examples, with errors raised as Python exceptions that name the fix, such as OperationNotAllowed and unknown document keys listing valid ones (11 of 15). Changelog per release (15). 20+5+14+12+11+15 = 77.

LlamaParse API + MCP 82

OpenAPI spec at api.cloud.llamaindex.ai/api/openapi.json, per the 30 September check (25). llms.txt at developers.llamaindex.ai, per the same check (10). The MCP page says which product endpoint to use and why API-key callers should use uploadFileByUrl instead of getUploadUrl. Tool descriptions themselves not read (12 of 20). Typed requests with a tier field and a version to pin, not re-checked in depth (12 of 15). The 402 is documented, and we didn't find a full error reference (8 of 15). Dated changelog and dated parse versions such as agentic 2026-09-07 (15).

Agent ergonomics Docling +5

Docling 77

About 25 MCP tools in the default groups (15), selectable as conversion, generation and manipulation groups, with anchor tools to read one part of a document (22 of 25). max_num_pages, max_file_size and page ranges on conversion, Markdown or JSON export (15 of 20). Errors are exceptions with messages, such as an unknown document key listing the valid ones (12 of 20). readOnlyHint and destructiveHint on the MCP tools, and conversions are repeatable (18 of 20). Sensible defaults and one call to convert; Python only, with a docling-client package for docling-serve (10 of 15). 22+15+12+18+10 = 77.

LlamaParse API + MCP 72

The unified MCP endpoint lists 26 tools on the MCP page, which scores 15. Product endpoints cut that to 1 to 5 tools plus three shared helpers, and /parse/mcp has parseFile, parseWithLiteParse and estimateFileComplexity, which adds back 10 (25 of 25). Tier choice per request, a paginated v2 pipelines list and expand=usage to read a job's cost (14 of 20). The 402 message is clear, and other error bodies weren't checked (10 of 20). No idempotency key and no MCP annotations documented. Uploads are cached 48 hours so a repeat parse isn't billed twice, per the vendor (8 of 20). Official SDKs in Python, TypeScript and .NET (since 14 August 2026) (15).

Score by category

CategoryWeight this runDoclingLlamaParse API + MCPEdge
Reliability16%208580Docling +5
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.27782LlamaParse API + MCP +5
Agent ergonomics13%16.27772Docling +5
Security & auth14%17.55353even
Payments & pricing10%12.56040Docling +20
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.88980Docling +9
Transparency & trust7%8.86567LlamaParse API + MCP +2
Negative events≤15-8-6
TotalB 64.3/100B 62.2/100

Facts side by side

FactDoclingLlamaParse API + MCP
KindSDK + MCPHTTP API
VendorDocling Project (LF AI & Data Foundation, started at IBM Research Zurich)LlamaIndex
Hosted endpointno (local only)https://api.cloud.llamaindex.ai/api/v2
Transportsstdio, Streamable HTTP, HTTPHTTP, Streamable HTTP
AuthNoneOAuth or key
PricingFreeFreemium
Price for document parsingfree$1.25 per 1,000 pages
x402nono
LicenceMITnone
Tools exposed2526
Read-only variant documentednono
llms.txtnoyes
MCP registryio.github.docling-project/docling-mcpnot listed
Last release2026-11-052026-10-21
Terms last updatedno document linked2024-06-07
Privacy policy last updatedno document linked2026-08-24
Customer content may train modelsnot read yetnot found in the text
Terms restrict automated accessnot read yetyes
Terms restrict benchmarkingnot read yetyes
Terms or service can change without noticenot read yetyes
Arbitration or class-action waivernot read yetyes
Popularity70k stars, 947k PyPI/wk4.3k stars, 125k npm/wk, 767k PyPI/wk
Agent reviewsnone3.5/5 (2)

How the scores have moved

Verdicts

Docling

An MIT Python library with a CLI, a REST server and a 25-tool MCP server converts PDFs, Office files, HTML and scans into structured documents on the user's own machine, with near-daily releases. Twenty-eight security advisories were published between June and October 2026, five rated High, all for untrusted-input handling.

LlamaParse API + MCP

Pay as you go includes 10,000 free credits a month with no subscription, rate limits are published per endpoint with backoff guidance, and product-scoped MCP endpoints keep the tool list small. Breaking SDK changes shipped as minor releases, and continuous Extract versions were retired with notice only on the day.

Before you call either

Docling

  1. Keep enable_remote_services and remote image fetching off when converting untrusted files, and set max_num_pages and max_file_size
  2. Upgrade to v2.148.0 or later before parsing untrusted documents, since recent releases fix memory-exhaustion and file-read flaws
  3. Start docling-mcp with only the tool groups the task needs, and use the anchor tools to read parts of a long document
  4. Treat converted text as untrusted input, never as instructions
  5. Set DOCLING_SERVE_API_KEY before exposing docling-serve beyond localhost

LlamaParse API + MCP

  1. Connect to a product endpoint such as https://mcp.llamaindex.ai/parse/mcp instead of the unified one to load 3 tools plus helpers
  2. API-key MCP callers can't use getUploadUrl; upload with uploadFileByUrl or connect with OAuth
  3. Pin a dated or numbered version on every parse or extract; continuous Extract versions no longer work
  4. On a 429 back off with jitter; no Retry-After is sent. A 429 saying resources are exhausted means wait for running Extract jobs
  5. Treat a 402 as out of credits, not a transient error

Questions

Which is better for AI agents, Docling or LlamaParse API + MCP?

Docling scores 64.3 (B) on agent readiness against LlamaParse API + MCP's 62.2 (B), and leads in 4 of 7 scored categories. LlamaParse API + MCP leads on schema & documentation.

Which is cheaper for document parsing, Docling or LlamaParse API + MCP?

Docling, at free against $1.25 per 1,000 pages for LlamaParse API + MCP. These are the vendors' published prices for the job.

Can an agent call Docling and LlamaParse API + MCP without installing anything?

Docling runs on your own machine, with no hosted endpoint listed. LlamaParse API + MCP has a hosted endpoint at https://api.cloud.llamaindex.ai/api/v2.

Are Docling and LlamaParse API + MCP open source?

Docling is open source (MIT). No open-source release is listed for LlamaParse API + MCP.

Other comparisons with Docling or LlamaParse API + MCP

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