Claude 3.5 Sonnet vs GPT-4o vs Gemini 1.5 Pro for Ingesting Complex Academic PDFs: Which LLM Extracts Equations and Methodology Without Hall

Feeding a basic markdown file to a modern LLM is easy. But throwing a 45-page, multi-column academic PDF crammed with Greek symbols, nested tables, and obscure methodology diagrams at one is a different story. If you are building research agents or automated literature-review pipelines, you have likely realised that standard parser wrappers often turn elegant LaTeX equations into a soup of garbled Unicode characters.

To find out which model actually has the cognitive stamina to digest dense scientific papers without hallucinating variables, we put the three top-tier models to the test: Anthropic’s Claude 3.5 Sonnet, OpenAI’s GPT-4o, and Google’s Gemini 1.5 Pro.

We evaluated them across three key vectors: spatial document parsing (handling multi-column layouts and inline charts), mathematical precision (converting raw visual equations to clean LaTeX), and context recall efficiency.

Round 1: Spatial Layout Parsing and Table Ingestion

Academic PDFs are a structural nightmare. They feature two-column layouts, wrapped text, floating figures with captions that look like body copy, and tables that span multiple pages.

When we fed a double-column physics paper directly to the models as a raw file, /platforms/openai occasionally stumbled on column boundaries. It sometimes read straight across the page, merging unrelated sentences from column one and column two into a confusing, hallucinatory hybrid. Its vision-based parser is fast, but it tends to lose track of reading flow when confronted with complex sidebars or dense footnotes. If you experience persistent structural failures with your documents, check the official troubleshooting guides at OpenAI Support to optimise your pre-processing pipelines.

/platforms/gemini handled the spatial layouts exceptionally well, largely because Google’s native multimodal architecture treats document structure as a core modality. It didn't get tripped up by vertical page separators. However, its native PDF parser has a habit of aggressively aggressive-filtering out what it thinks is noise—sometimes skipping over small footnote subscripts that happen to contain critical parameter values.

/platforms/claude absolutely dominated this round. When parsing multi-column structures, it preserved the logical flow of the text perfectly. More importantly, its ability to convert complex, borderless tables into clean markdown was flawless. It did not misalign columns or misinterpret empty cells as belonging to the adjacent column. For a deeper understanding of how modern models handle spatial parsing, check out our /glossary.

Round 2: Mathematical Rigour and LaTeX Extraction

Extracting equations from a PDF is where the real pain begins. We tested the models on their ability to locate inline math and block-level equations, converting them into syntactically correct LaTeX.

• GPT-4o: Excellent at locating equations, but highly prone to losing subscripts. A term like \theta_{i,t-1} frequently degraded into \theta_{it1}. For casual reading, this is fine; for executing code based on these papers, it is catastrophic.
• Gemini 1.5 Pro: Gemini was hit-or-miss. It captured the general structure of the mathematical formulas but occasionally substituted standard symbols with similar-looking characters from other alphabets, which broke our compiler. If you run into parsing blocks, you can find specific API adjustments at Gemini Support.
• Claude 3.5 Sonnet: Sonnet is exceptionally precise here. It successfully extracted deeply nested matrices and complex calculus notation without dropping braces or misidentifying Greek letters. It ticked all the boxes for accuracy, delivering clean, compilation-ready LaTeX on almost every attempt.

Round 3: Context Windows vs. Needle-in-a-Haystack Recall

When you are analysing a literature review or a massive clinical trial report, you need to search across hundreds of pages. This is where context limits and search precision clash.

Gemini 1.5 Pro boasts a massive 2-million-token context window. You can load dozens of full-length papers into a single prompt. For high-level thematic mapping, this is incredibly useful. However, we noticed that when asked to retrieve a specific, minor methodology detail hidden deep within page 87 of a combined document, Gemini sometimes gave a generic summary instead of the exact detail. It has the room, but its focus can occasionally wander.

Claude 3.5 Sonnet has a smaller 200,000-token context limit, but its recall accuracy is incredibly sharp. It rarely misses a detail, even when that detail is buried in the middle of a massive block of dense text.

GPT-4o sits at 128,000 tokens. It is fast, but it is best reserved for shorter, single-paper analysis rather than massive, multi-document synthesis.

Pricing, Limits, and API Economics

If you are running these extractions at scale, your choice of platform will heavily impact your API bill. Here is how the pricing stack compares per million tokens:

• Claude 3.5 Sonnet: $3.00 input / $15.00 output. Highly efficient, but the 5x markup on output tokens means you should keep your extraction prompts tightly focused on structured JSON keys rather than asking for long-winded conversational explanations.
• GPT-4o: $2.50 input / $10.00 output. Slightly cheaper than Sonnet, making it highly competitive if you use pre-parsed text instead of raw images.
• Gemini 1.5 Pro: $1.25 input / $5.00 output (for prompts under 128k tokens; prices double for longer prompts). Highly economical for bulk processing, though you will need to account for more post-processing validation to catch occasional formatting slips.

The Verdict

If your pipeline demands absolute mathematical accuracy, flawless table extraction, and syntactically clean LaTeX, Claude 3.5 Sonnet is the undisputed champion. It is worth the slight premium in API pricing because it saves hours of downstream data-cleaning and debugging.

If you are digesting vast libraries of papers simultaneously to find broad thematic links, Gemini 1.5 Pro’s massive context window and attractive pricing make it the logical tool for the job. Just make sure to run a secondary validation script over any critical extracted values.

For those looking to streamline their prompt structures before building their parsing pipelines, try building a customized test prompt on our /prompts.