Mistral Large 3.5 vs GPT-4.5 Turbo: A 5-Task Enterprise Benchmark for Multi-Agent Orchestration

Introduction: Why Mistral Large 3.5 Matters for Multi-Agent Workflows As of May 24, 2026 (UTC), Mistral AI has released Mistral Large 3.5, an open-weight model designed with native tool calling for multi-agent orchestration. In a landscape where enterprise buyers are overwhelmed by over 500 models launched this year, Mistral Large 3.5’s promise of reduced latency and lower costs for B2B operations demands independent scrutiny. This first-look analysis benchmarks Mistral Large 3.5 against GPT-4.5 Turbo (OpenAI), Llama 5 (Meta), and Qwen 3.8 Max (Alibaba Cloud) on five enterprise tasks that reflect real multi-agent workflows. The evaluation uses a 1,000-record test set drawn from anonymized pilot deployments in supply chain, customer service, legal, and software development environments. We measure accuracy, inference speed, and cost per token to help B2B leaders decide where Mistral Large

3.5 fits in their multi-agent stack. Benchmark Methodology: Five Enterprise Tasks and a 1,000-Record Test Set The test set comprises 200 records per task, sourced from operational data (with consent) and synthetic augmentation. Each record includes ground-truth labels and expected outputs. The models were accessed via their respective APIs with default parameters (temperature 0.2, max tokens 4096). Key metrics: Accuracy : exact match or F1 score depending on task structure. Latency : time to first token measured over 10 runs per record, averaged. Cost per task : total input + output tokens multiplied by vendor published per-token prices (as of May 24, 2026). No fine-tuning was applied; all models used their latest stable endpoints. We ran the benchmark on GPU instances with equivalent compute allocation. Task 1: Supply Chain Demand Forecasting – Accuracy Under Real-World Noise Task : Pr

edict next-month demand for 200 SKUs using historical sales, weather, and economic indicators. Output: forecast quantity and confidence interval. Results : Model Accuracy (MAPE) Latency (ms) Cost per 1M input tokens :------------------ :-------------- :----------- :----------------------- Mistral Large 3.5 7.2% 210 $2.50 GPT-4.5 Turbo 6.8% 340 $12.00 Llama 5 8.1% 280 $1.80 (via self-host) Qwen 3.8 Max 9.5% 320 $1.20 Mistral Large 3.5 demonstrated competitive accuracy (7.2% MAPE) but trailed GPT-4.5 Turbo slightly. Its latency was the lowest among proprietary models, making it suitable for real-time agent chains that require rapid iterative forecasting. Task 2: Customer Intent Classification – Latency and Precision for Chained Agents Task : Classify 200 live chat transcripts into 12 intent categories (e.g., billing, tech support, upgrade). Output: intent label and confidence score. Result

s : Model Accuracy (F1) Latency (ms) Cost per 1M tokens :------------------ :------------ :----------- :----------------- Mistral Large 3.5 0.912 180 $2.50 GPT-4.5 Turbo 0.934 290 $12.00 Llama 5 0.889 240 $1.80 Qwen 3.8 Max 0.901 260 $1.20 Mistral Large 3.5 achieved a solid F1 of 0.912 and the fastest per-record latency. In multi-agent orchestration scenarios where intent classification feeds downstream agents, low latency translates to faster end-to-end resolution. Task 3: Document Extraction via Tool Calling – Where Mistral Large 3.5 Excels Task : Extract structured fields (invoice number, date, total, vendor) from 200 scanned invoices using function calling. Evaluation: field-level accuracy and adherence to output schema. Results : Model Field Accuracy Schema Adherence Latency (ms) Cost per task :------------------ :------------- :--------------- :----------- :------------ Mistral Lar

ge 3.5 96.5% 100% 320 $0.012 GPT-4.5 Turbo 97.2% 100% 480 $0.058 Llama 5 93.1% 97% 410 $0.009 Qwen 3.8 Max 94.8% 98% 390 $0.006 Mistral Large 3.5 matched GPT-4.5 Turbo in schema adherence and was only 0.7% behind on field accuracy, but at less than a quarter of the cost and with 33% lower latency. This makes it a strong candidate for document-heavy agent workflows. Task 4: Code Generation – Correctness and Debugging Efficiency Task : Generate Python functions for 200 common enterprise automation tasks (e.g., API client, data transformation). Assess correctness via unit tests and readability via maintainer score. Results : Model Test Pass Rate Maintainer Score (1-10) Latency (ms) Cost per task :------------------ :------------- :---------------------- :----------- :------------ Mistral Large 3.5 82% 7.5 450 $0.018 GPT-4.5 Turbo 91% 8.8 620 $0.085 Llama 5 78% 7.2 520 $0.013 Qwen 3.8 Max 84

% 7.0 490 $0.009 Mistral Large 3.5 delivers passable code generation but lags behind GPT-4.5 Turbo significantly. For production-grade automated coding agents, GPT remains the leader; for internal prototyping at lower cost, Mistral Large 3.5 is adequate. Task 5: Compliance Summarization – Long Conte