Enterprise Document AI Benchmark 2026: Composer 2.5 vs. GPT-4.5 Turbo vs. Llama 5

Why Document-Heavy Enterprises Need Model Benchmarks Now As of May 23, 2026, organizations processing thousands of invoices, contracts, and multi-page reports daily face a growing dilemma: which AI model delivers the right balance of accuracy, speed, and cost for their specific document pipeline? The latest batch of large language models—Composer 2.5, GPT-4.5 Turbo, and Llama 5—each promise improvements, but vendor claims alone don’t reveal which model actually works best for structured document tasks. B2B operations leaders need independent, reproducible data. That’s why we ran a controlled benchmark across three critical enterprise document tasks using 500 records per task. The results show clear trade-offs: Composer 2.5 excels at structured parsing but trails on open-ended summarization, while GPT-4.5 Turbo and Llama 5 offer different strengths. This article presents the raw numbers,

latency comparisons, and a practical decision framework to help you choose the right model for your workflows. Benchmark Scope: Invoice Extraction, Contract Clause ID, and Multi-Document Summarization We selected three tasks that represent the most common document AI use cases in B2B operations: Invoice extraction AI : Pulling line items, totals, dates, and vendor details from scanned or digital invoices. Accuracy measured by field-level F1 score. Contract clause identification : Locating specific clauses (e.g., termination, liability, governing law) in dense legal documents. Success rate defined by exact clause match. Multi-document summarization : Generating concise executive summaries from 5–10 related documents (e.g., quarterly reports, policy bundles). Quality evaluated by human reviewers on coherence, coverage, and conciseness. Each task used a 500-record pilot set drawn from real

enterprise corpuses (anonymized). Models were tested via their official API endpoints with default parameters (temperature 0.0 for deterministic results). No fine-tuning or prompt engineering beyond a clean base prompt was applied, ensuring a fair comparison of out-of-the-box capabilities. Composer 2.5 vs. GPT-4.5 Turbo vs. Llama 5: Accuracy Results on Structured Parsing For structured parsing tasks—invoice extraction and contract clause identification—Composer 2.5 achieved a 12% higher overall accuracy compared to the next best model (GPT-4.5 Turbo). Specifically: Invoice extraction : Composer 2.5 reached an F1 score of 0.94, versus GPT-4.5 Turbo at 0.84 and Llama 5 at 0.81. Contract clause identification : Composer 2.5 correctly located targeted clauses 89% of the time, while GPT-4.5 Turbo scored 79% and Llama 5 scored 76%. This advantage stems from Composer 2.5’s architecture, which a

ppears optimized for long-context, structured document understanding. Its attention mechanism handles table layouts, multi-column formats, and legal boilerplate with fewer errors than the general-purpose GPT-4.5 Turbo or Llama 5. However, note that structured document AI performance can vary with document quality. In our pilot, slightly distorted scans reduced Composer 2.5’s accuracy by 3–4%, still keeping it ahead of competitors. Latency Comparison: Which Model Delivers Faster Document Processing? Latency is a critical factor for real-time document pipelines. Composer 2.5 processed each record 30% faster on average than GPT-4.5 Turbo and 35% faster than Llama 5. Median per-document processing times (excluding network overhead): Composer 2.5: 2.1 seconds GPT-4.5 Turbo: 3.0 seconds Llama 5: 3.2 seconds The speed advantage is most pronounced for high-volume invoice extraction, where Compos

er 2.5 completed 500 records in about 17 minutes, compared to 25 minutes for GPT-4.5 Turbo. This lower document parsing latency directly translates to throughput gains for enterprise systems processing thousands of documents hourly. Keep in mind that latency also depends on request concurrency and API tier. These numbers reflect standard synchronous calls; batch processing could narrow the gap. Trade-Off Alert: Where Composer 2.5 Falls Behind on Open-Ended Summarization Multi-document summarization revealed a different story. On open-ended tasks requiring synthesis across varied sources, Composer 2.5 scored below both GPT-4.5 Turbo and Llama 5 : Human evaluators rated Composer 2.5 summaries as “good” only 62% of the time, vs. 78% for GPT-4.5 Turbo and 74% for Llama 5. Composer 2.5 tended to produce overly literal summaries, sticking to extracted facts rather than drawing cross-document i

nsights. In contrast, GPT-4.5 Turbo generated more cohesive narratives with better context integration. This trade-off is critical: a model that excels at extracting structured data may not be the best choice for executive briefs or market intelligence synthesis. For multi-document summarization wor