Enterprise Multi-Agent Model Benchmark 2026: Composer 2.5 vs GPT-4.5 Turbo, Llama 5, and Qwen 3.8 Max

Enterprise AI Models for Multi-Agent Orchestration: A Vendor-Neutral Benchmark As of May 24, 2026, the landscape of enterprise AI models has shifted with the release of Anthropic's Composer 2.5, OpenAI's GPT-4.5 Turbo, Meta's Llama 5, and Alibaba Cloud's Qwen 3.8 Max. While each model excels in general NLP benchmarks, operations leaders need specialized insights into how these models perform on multi-agent orchestration tasks—the backbone of modern B2B workflows. This article presents the first vendor-neutral benchmark designed specifically for multi-agent B2B operations, drawing on a proprietary 500-record test set from 10 enterprise pilots across supply chain, legal, customer service, engineering, and compliance domains. Why a Multi-Agent Task Benchmark Matters for B2B Operations General AI leaderboards often measure single-turn or single-agent performance on academic datasets. But rea

l-world B2B deployments demand coordination among multiple agents—each responsible for a subtask like data extraction, classification, forecast generation, or code execution. In supply chain planning, for instance, one agent might ingest real-time sensor data, another surfaces historical trends, and a third generates actionable forecasts. The ability to maintain context, share information across agents, and remain aligned with enterprise safety policies is critical. Current benchmarks fail to capture these dynamics. A model that scores top marks on text summarization may choke when asked to parse a multi-part PDF and pass structured JSON to a downstream agent. Our benchmark fills this gap by evaluating models on five tasks that mirror real multi-agent workflows. Methodology: How We Benchmarked Composer 2.5 Against GPT-4.5 Turbo, Llama 5, and Qwen 3.8 Max We assembled a 500-record test se

t derived from anonymized data from 10 B2B enterprise pilots conducted between February and April 2026. The test set covers: 100 records for supply chain forecasting (time-series + external event data) 100 records for document extraction (invoices, contracts, PDFs) 100 records for customer intent classification (live chat transcripts) 100 records for code generation (Python, SQL for agent-to-agent integration) 100 records for safety compliance (adversarial prompts, bias injection, refusal testing) Each task was evaluated using three metrics: accuracy (exact match or F1 score), latency (time to first token and total completion), and safety (refusal rate on inappropriate requests, bias score). Models were accessed via their official APIs using the latest stable versions: Composer 2.5 (Anthropic, May 2026), GPT-4.5 Turbo (OpenAI, April 2026), Llama 5-70B-Instruct (Meta, May 2026), and Qwen

3.8 Max (Alibaba Cloud, April 2026). Inference was performed on equivalent hardware (A100 GPUs) where applicable, but API-based models used vendor infrastructure. Pricing was compared using published per-token rates as of May 24, 2026, but we caution that enterprise discounts and batch pricing can significantly alter effective costs. Task 1: Supply Chain Forecasting Performance Accuracy: Composer 2.5 achieved an F1 score of 0.88 on multi-step forecasting, edging out GPT-4.5 Turbo (0.84), Llama 5 (0.81), and Qwen 3.8 Max (0.79). The advantage was most pronounced in scenarios requiring reasoning over missing data and causal inference (e.g., supplier disruptions from weather events). Latency: Composer 2.5 averaged 2.4 seconds per forecast—almost 30% slower than GPT-4.5 Turbo (1.8s) and Qwen 3.8 Max (2.0s). Llama 5, deployed locally, achieved 1.2s but with lower accuracy. Key insight: For su

pply chain teams that prioritize forecast reliability over speed, Composer 2.5 is the top choice. Real-time hubs may prefer GPT-4.5 Turbo for its speed-accuracy balance. Task 2: Document Extraction Accuracy Across Formats We tested extraction of structured fields (invoice number, date, line items, contract clauses) from diverse formats (scanned PDFs, clean digital PDFs, image-heavy documents). Precision/Recall: GPT-4.5 Turbo led with 0.93 precision and 0.90 recall, closely followed by Composer 2.5 (0.91/0.89). Llama 5 and Qwen 3.8 Max lagged on complex tables and handwritten fields. Multi-step pipeline: The most challenging scenario involved extracting a clause from a contract PDF and cross-referencing it with a separate pricing page. Composer 2.5’s reasoning chain produced the fewest errors, while GPT-4.5 Turbo occasionally missed context links. Recommendation: For document-heavy workfl

ows (e.g., procurement automation), GPT-4.5 Turbo offers the highest raw extraction accuracy, but Composer 2.5 is better suited for pipelines requiring cross-document reasoning. Task 3: Customer Intent Classification in Real-Time Conversations Models were tasked with classifying customer intents fro