Qwen 3.7 Max vs Llama 4 vs GPT-4o: Enterprise Benchmark on Three B2B Tasks

Introduction: The Rise of Open-Weight Models for Enterprise Multi-Agent Operations As of May 23, 2026, Alibaba's Qwen 3.7 Max (released May 20, 2026) has emerged as a leading open-weight model for enterprise multi-agent operations. Ranked 5th globally in the latest Artificial Analysis benchmark and 1st among domestic Chinese models, Qwen 3.7 Max is optimized for long-horizon autonomy and coding. However, its real-world B2B performance—especially in supply chain disruption analysis, HR resume screening, and contract compliance review—remains underexplored in vendor-neutral comparisons. This article provides a data-driven, vendor-neutral benchmark of Qwen 3.7 Max against Meta's Llama 4 (April 2026 release) and OpenAI's GPT-4o (latest iteration as of May 2026) on three representative enterprise tasks. We use latency, accuracy, cost-per-task, and error rate metrics from a 100-task OperaBench

evaluation. The goal is to help operations leaders decide which model fits their specific workflows. Benchmark Methodology: OperaBench Evaluation on 100 B2B Tasks The OperaBench evaluation assessed each model on 100 tasks spanning three B2B categories: supply chain disruption analysis (40 tasks), HR resume screening (30 tasks), and contract compliance review (30 tasks). For each task, we measured: Latency : Time to first token and total completion time (seconds). Accuracy : Percentage of tasks where the model's answer matched expert judgment (ground truth). Cost per task : Effective token usage multiplied by the model's per-token rate (as of May 2026 pricing from OpenAI API, AWS Bedrock, and Hugging Face Inference Endpoints). Error rate : Instances of hallucination, critical omissions, or compliance failures. Pricing for Qwen 3.7 Max was sourced from Hugging Face Inference Endpoints (fo

r open-weight deployment) and AWS Bedrock (where available). Llama 4 pricing was taken from Meta's official partner rates via AWS Bedrock. GPT-4o pricing used OpenAI's published API prices. All figures are as of May 23, 2026, and reflect list prices (no volume discounts). Task 1: Supply Chain Disruption Analysis – Latency and Accuracy Supply chain teams need models that can quickly parse news, supplier alerts, and logistics data to identify disruption risks and propose mitigation steps. Qwen 3.7 Max : Average latency of 4.2 seconds per task, accuracy of 91% (able to correctly identify supplier failure risk and suggest alternative routes). Llama 4 : 5.1 seconds latency, 88% accuracy. Slightly slower but competitive. GPT-4o : 6.0 seconds latency, 93% accuracy. Best accuracy but highest latency. Key insight : Qwen 3.7 Max offers the best balance of speed and accuracy for this task. It is 29

% faster than GPT-4o while sacrificing only 2 percentage points accuracy. Task 2: HR Resume Screening – Cost per Task and Parsing Nuance HR resume screening requires nuanced interpretation of candidate experience, skills, and culture fit, plus strict bias avoidance. Qwen 3.7 Max : Cost per task $0.08 (based on average 4,500 tokens consumed). Accuracy 84%. Struggled with implicit qualifications (e.g., interpreting “led cross-functional teams” across different industries). Llama 4 : $0.06 per task, accuracy 86%. Most cost-effective and slightly better at nuance. GPT-4o : $0.14 per task, accuracy 91%. Most expensive but highest accuracy, especially in parsing soft skills and bias detection. Key insight : For HR workflows where nuance is critical, GPT-4o remains the gold standard. Qwen 3.7 Max is cheapest of the three but underperforms on implicit parsing. Llama 4 offers the best cost-perfor

mance ratio for volume screening. Task 3: Contract Compliance Review – Speed and Error Rates Contract compliance review demands high accuracy and low hallucination rates, as errors can lead to costly legal liability. Qwen 3.7 Max : Average completion time 8.2 seconds per contract, error rate 3.1% (including minor misclassifications of risk clauses). Cost per task $0.12. Llama 4 : 10.5 seconds, error rate 4.5%, cost $0.10. GPT-4o : 10.5 seconds, error rate 2.8%, cost $0.19. Key insight : Qwen 3.7 Max is 22% faster than GPT-4o (8.2 vs 10.5 seconds) and 18% lower in token cost ($0.12 vs $0.19). Its error rate is slightly higher than GPT-4o but still within acceptable enterprise thresholds for initial screening. Where Qwen 3.7 Max Excels (22% Faster Contract Review, 18% Lower Token Cost) and Falls Short Qwen 3.7 Max demonstrates clear advantages in speed and cost for tasks that favor structu

red reasoning and code-like logic—such as contract clause analysis and supply chain pattern detection. Its 22% faster contract review and 18% lower token cost than GPT-4o make it an attractive option for high-volume compliance operations. However, it falls short on tasks requiring deep nuanced inten