---
title: "LlamaParse vs GPT, Claude and Gemini for PDF parsing (2026)"
description: "Compare published PDF parsing results for LlamaParse, GPT, Claude and Gemini: tables, charts, cost per 1,000 pages and when a model alone is enough."
published: "2026-08-29"
updated: "2026-11-04"
source: "https://developers.llamaindex.ai/llamaparse/parse/features/layout-and-bounding-boxes/"
---

# LlamaParse vs GPT, Claude and Gemini for PDF parsing (2026)

*Disclosure: LlamaIndex makes LlamaParse and built ParseBench. LlamaParse combines its own models, including OCRia-1, with models from OpenAI, Anthropic and Google. This comparison uses published ParseBench results, with evaluation code and outputs available through the benchmark repository.*

**Is LlamaParse better than sending PDFs straight to GPT, Claude or Gemini?** On the published ParseBench comparison, LlamaParse Agentic leads the tested general-purpose configurations overall: 82.83 at \$12.50 per 1,000 pages, versus 78.80 at \$61.30 for Claude Opus 5.5 at high effort. That supports evaluating it first for complex document parsing. For a few simple PDFs and a one-off answer, a direct model call can be enough.

- **Best overall:** LlamaParse Agentic Plus, 86.69
- **Best value for complex PDFs:** LlamaParse Agentic, 82.83 at \$12.50 per 1,000 pages
- **Tables:** Claude Opus 5.5 at high effort leads, 94.26 vs 93.27 for Agentic Plus
- **Charts:** LlamaParse Agentic Plus leads, 92.19 vs 70.89 for Opus
- **Lowest cost shown:** GPT-6 Luna at medium reasoning and Claude Haiku 5.5, \$1.40 per 1,000 pages, scoring 61.03 and 56.02
- **Bounding boxes and page confidence:** returned by LlamaParse; frontier models give coordinates only when prompted

## Which LlamaParse advantages matter for a buying decision?

The clearest supported case is the tested combination of overall parsing quality, chart interpretation and localized evidence. Agentic Plus leads the tested high-effort Opus row on charts by 21.30 points (92.19 versus 70.89) and visual grounding by 14.87 points (75.43 versus 60.56). Opus leads on tables (94.26 versus 93.27) and content faithfulness (91.25 versus 89.74), so a table-only claim would tell a different story. [Pinned ParseBench results](https://github.com/run-llama/ParseBench/blob/0ccc16783612a8ff37e77139f5e799510cde47b6/leaderboard.csv).

Use those differences to choose what to trial first. The benchmark tests the specified parsing prompts and configurations; it does not establish that LlamaParse wins every question-answering task or every custom model pipeline.

## How do GPT, Claude, Gemini and LlamaParse compare?

These are published [ParseBench results](https://github.com/run-llama/ParseBench/blob/0ccc16783612a8ff37e77139f5e799510cde47b6/leaderboard.csv) at leaderboard commit 0ccc167 (November 2, 2026). Scores are on a 0–100 scale. Overall is the mean of five dimensions: tables, charts, content faithfulness, semantic formatting and visual grounding. It is a benchmark score, not the percentage of documents parsed without errors.

| Setup | Overall | Tables | Charts | Visual grounding | Cost per 1,000 pages |
| --- | --- | --- | --- | --- | --- |
| LlamaParse Agentic Plus | **86.69** | 93.27 | **92.19** | **75.43** | \$56.25 |
| LlamaParse Agentic | 82.83 | 90.90 | 81.20 | 69.42 | \$12.50 |
| LlamaParse Cost Effective | 77.68 | 86.72 | 73.98 | 62.22 | \$3.75 |
| Claude Opus 5.5, high effort | 78.80 | **94.26** | 70.89 | 60.56 | \$61.30 |
| Claude Opus 5.5, default | 76.97 | 93.88 | 64.12 | 58.44 | \$57.90 |
| Claude Haiku 5.5 | 56.02 | 86.85 | 10.13 | 27.63 | \$1.40 |
| GPT-6 Astra, low reasoning | 69.81 | 93.18 | 35.08 | 56.04 | \$98.70 |
| GPT-6 Luna, medium reasoning | 61.03 | 86.76 | 42.97 | 29.60 | \$1.40 |
| GPT-6 Luna, max reasoning | 64.13 | 87.44 | 45.71 | 41.48 | \$7.20 |
| Gemini 3.8 Flash, low thinking | 69.15 | 88.22 | 35.12 | 67.81 | \$7.10 |
| Gemini 3.8 Flash, high thinking | 71.20 | 89.15 | 32.58 | 69.03 | \$34.10 |
| Google Cloud Document AI | 48.66 | 54.95 | 1.59 | 61.26 | \$10.00 |

Costs convert the leaderboard's cents per page into dollars per 1,000 pages. They describe the benchmark workload and configurations, excluding downstream model calls and optional processing features. Token-based costs will vary with your documents and current provider rates. The table covers the published configurations listed here, not every current model; no speed ranking is claimed.

**Our recommendation:** start with LlamaParse Agentic for mixed PDFs. It scores 4.03 points above Opus at high effort for about one-fifth of the parsing cost. Use Agentic Plus when chart extraction is a priority. If tables dominate your workload, test Opus alongside it: Opus has the strongest table result here. For the lowest processing spend among the displayed setups, GPT-6 Luna at medium reasoning and Haiku 5.5 both cost \$1.40 per 1,000 pages, with lower overall scores.

## How were these PDF parsing results measured?

[ParseBench](https://github.com/run-llama/ParseBench) covers approximately 2,000 human-verified pages from enterprise documents, including insurance, finance and government material. The comparison uses published, verified leaderboard rows; the [pipeline definitions](https://github.com/run-llama/ParseBench/blob/main/docs/pipelines.md) specify each model and reasoning configuration. The frontier-model file pipelines process individual pages as native PDFs and use detailed instructions for Markdown, HTML tables, chart data and layout coordinates, with the full layout prompt available in the [source code](https://github.com/run-llama/ParseBench/blob/main/src/parse_bench/inference/providers/parse/_layout_utils.py). The repository links evaluation code and published outputs; these results measure page parsing, not whole-document question answering or every possible custom pipeline.

## Where do the differences show up?

### Charts need the underlying values, not just a description

A model can describe a chart correctly while failing to recover its data. If your agent compares quarterly revenue, it needs values associated with the right series, dates and units. A readable paragraph about the trend will not do that job.

Charts show a large gap in these results. Agentic Plus scores 92.19, compared with 70.89 for Opus at high effort, 35.08 for GPT-6 Astra at low reasoning and 32.58 for Gemini 3.8 Flash at high thinking. Even Cost Effective, at 73.98, exceeds those three model configurations on this dimension.

That makes chart-heavy financial reports a strong reason to evaluate a parser before committing to direct model calls. It does not mean every chart fails with a frontier model. It means the aggregate results favor LlamaParse on this task.

### Tables are a real strength of frontier models

Merged headers and spanning cells determine which value belongs to which category. Flatten a two-row header incorrectly and a number can end up attached to the wrong year or business unit, even when every character was transcribed correctly.

Opus at high effort scores 94.26 on tables, ahead of Agentic Plus at 93.27. GPT-6 Astra also performs strongly at 93.18, above Agentic's 90.90. A dedicated parser does not automatically win every document task.

If you mostly process tables, compare the actual structures each setup returns. Check merged cells, repeated headers and missing rows alongside cost. The overall leaderboard alone should not decide a table-only workload.

### Reading text and preserving the page are different tasks

Dense reports combine paragraphs, footnotes, figures and multiple columns. A pipeline can recover the words while losing the relationship between them. That matters when a footnote changes the meaning of a value or a heading determines which section a paragraph belongs to.

ParseBench scores content faithfulness and semantic formatting separately for this reason. Opus at high effort exceeds Agentic Plus on content faithfulness, 91.25 versus 89.74. LlamaParse leads on semantic formatting: Agentic scores 84.81 and Agentic Plus 82.84, versus 77.06 for Opus. The useful question is which errors would break your workflow: missing text, lost structure, incorrect chart values or misplaced source regions.

## When is sending a PDF directly to a model enough?

Start with a direct model call when you have a small number of readable PDFs, straightforward layouts and a specific question to answer. Summarizing a short report or finding a clause may not require a reusable parse of every page. Existing API integration also makes this a practical starting point. Check a representative sample, including the hardest pages, before expanding the workflow. Add a parsing layer when you need consistent document output across many files, reliable chart data or source regions for review. Volume matters, but so does the cost of a quiet error in a single document.

## What do you build yourself with direct model calls?

The initial request can be simple. Keeping it reliable across changing documents takes more work:

- **Page handling:** split oversized files, preserve page identity and reassemble results in order.
- **Retries and throughput:** handle rate limits, transient errors, concurrency and partial failures without duplicating work.
- **Incomplete output:** detect truncation, missing sections and refusals, then decide what to retry or review.
- **Output checks:** enforce the expected structure and validate values against your business rules. Schema-valid JSON can still contain incorrect values.
- **Grounding:** connect text, tables and values to source pages and regions your users can inspect.
- **Quality assessment:** decide which results need review and evaluate that decision on your own documents.
- **Cost control:** track input, output and reasoning usage, then retest when prompts or models change.

LlamaParse provides a maintained parsing API and structured document output. Your team still owns downstream business validation and the decisions made from that output. For the longer engineering discussion, see [LLM APIs Aren't Complete Document Parsers](https://www.llamaindex.ai/blog/llm-apis-are-not-complete-document-parsers).

## How does each model API handle PDFs?

### Gemini

[Gemini's document-processing API](https://ai.google.dev/gemini-api/docs/document-processing) supports native PDF understanding, including text and visual content, with documented limits of 50 MB or 1,000 pages. Gemini 3.8 Flash at low thinking costs \$7.10 per 1,000 benchmark pages and scores 69.15 overall. High thinking raises the overall score to 71.20 and cost to \$34.10, but does not improve every category. Its 69.03 visual-grounding score exceeds LlamaParse Cost Effective (62.22) and nearly matches Agentic (69.42), though Agentic Plus leads at 75.43. That is a reason to test its prompted coordinates, alongside its lower chart scores, rather than assume a parser always locates content more accurately.

### GPT

[OpenAI's PDF input support](https://developers.openai.com/api/docs/guides/pdf-files) supplies extracted text and page images to compatible vision models. GPT-6 Astra at low reasoning performs well on tables, scoring 93.18, but scores 69.81 overall at \$98.70 per 1,000 benchmark pages. Luna offers a cheaper starting point: medium reasoning costs \$1.40 with a 61.03 overall score. Increasing Luna to max reasoning raises that score to 64.13 and cost to \$7.20. Choose the model and reasoning setting together, then measure whether the extra spend improves the content your workflow actually needs.

### Claude

[Claude supports PDF text and visual analysis](https://platform.claude.com/docs/en/build-with-claude/pdf-support). Opus 5.5 at high effort is the strongest verified general-purpose model setup overall in this leaderboard snapshot and the table leader in this comparison, at \$61.30 per 1,000 pages. Claude also offers [native PDF page citations](https://platform.claude.com/docs/en/build-with-claude/citations), so citations alone are not a reason to add a parser. Page citations and bounding boxes serve different needs: one identifies the page supporting an answer; the other identifies a region within it. Haiku 5.5 is substantially cheaper here, with a lower overall score of 56.02.

## Can you get bounding boxes and confidence scores?

Yes, but distinguish the output interface from its measured accuracy.

LlamaParse returns [structured bounding boxes](https://developers.llamaindex.ai/llamaparse/parse/features/layout-and-bounding-boxes/), with options for word, line and table-cell grounding. Frontier models can also generate coordinates when prompted; ParseBench explicitly asks them to do so. Gemini's visual-grounding result, above LlamaParse Cost Effective and close to Agentic, shows why “native output” and “most accurate boxes” should be evaluated separately.

LlamaParse also exposes page confidence in its [result metadata](https://developers.llamaindex.ai/llamaparse/parse/guides/response-format/). Its optional high-effort assessment adds document-level confidence and a breakdown for an additional five credits per page, equivalent to \$6.25 per 1,000 pages at the documented credit rate. A number a model generates after being asked “how confident are you?” should not be treated as a calibrated probability of correctness. Validate review thresholds against labeled examples from your own workload.

## Frequently asked questions

### Can Gemini replace OCR?

Gemini can read text and visual content from PDFs, including pages that require visual recognition. For a direct question or summary, that can be sufficient. Test completeness, table structure and chart values before using it to replace the parsing stage of a production workflow.

### Is GPT good at reading tables in PDFs?

Yes. GPT-6 Astra at low reasoning scores 93.18 on ParseBench tables, close to Agentic Plus at 93.27. Its lower chart and overall scores show why table performance should not be generalized to every PDF task.

### Does LlamaParse use GPT, Claude or Gemini?

LlamaParse uses its own models, including OCRia-1, and models from those providers within a maintained parsing pipeline. You can send its output to GPT, Claude or Gemini afterward. Using LlamaParse does not require choosing the LlamaIndex framework for the rest of your stack.

### What does parsing 1,000 pages cost?

In this benchmark snapshot, LlamaParse costs \$3.75 on Cost Effective, \$12.50 on Agentic and \$56.25 on Agentic Plus. Direct model costs range from \$1.40 for the displayed Luna and Haiku configurations to \$98.70 for Astra. These are parsing costs; optional features and subsequent reasoning or extraction calls add to the total.

### How do I turn contracts or invoices into JSON?

Parsing preserves document content and structure. Extracting invoice totals or contract renewal terms adds a field schema and task-specific checks. Use schema-guided extraction, such as LlamaParse Extract, or build that step on parsed content with your chosen model. ParseBench measures parsing and does not establish field-extraction accuracy.

[Try LlamaParse](https://www.llamaindex.ai/pricing) with 10,000 free credits per month and compare its output with your current model on the same documents.