AI Models Benchmarked for IT Support Ticket Resolution
New data from Shield Technology Partners measures the cost, speed, and accuracy of leading models in handling complex IT tasks.
Updated on Oct. 30, 2026 in Artificial Intelligence
Live Poll
Do you trust AI agents to handle complex customer support requests without human intervention?
Shield Technology Partners has released a performance benchmark evaluating AI models on their ability to resolve 52 challenging IT support tickets. The findings provide a data-driven comparison of success rates and operational costs for models including GPT-6 Astra, Claude Opus 5.5, and Gemini 3.8 Flash.
Why it matters
As managed service providers (MSPs) automate more complex technical workflows, these benchmarks help prioritize the trade-offs between model reasoning accuracy, processing latency, and per-ticket expenditure. The data aims to guide infrastructure strategy as firms move beyond basic task automation.
In the evaluation of 52 IT tickets, GPT-6 Astra at extra-high reasoning effort reached a 61.5% success rate compared to 53.8% for Claude Opus 5.5 high. Costs ranged from $0.13 per ticket for DeepSeek V4.1 Flash to $7.60 for the highest-effort Astra setting, while processing times extended from under 10 minutes to nearly 20 minutes as reasoning intensity scaled.
The players
Shield Technology Partners
An IT infrastructure services firm that develops automation tools for managed service providers.
Forge
An internal AI-driven engineering tool managed by Shield Technology Partners that handles over 50% of actionable IT support tickets.
The details
Shield Technology Partners evaluated models on tickets previously processed incorrectly by their own AI Engineer tool, Forge. The test measured performance based on each model's success in resolving these complex, multi-step technical inquiries. By scaling reasoning effort—the computational resources allocated to chain-of-thought processing—the team documented how latency and cost correlate with accuracy in high-stakes automation.
Timeline
- 2026-10-30
Shield Technology Partners published the AI model benchmarks.
The Tech Race
This benchmarking effort moves the AI performance race from generic reasoning capabilities to domain-specific utility for managed service providers. It highlights the divergence between lightweight, low-cost models like DeepSeek V4.1 Flash and high-compute alternatives required for complex ticket resolution.
MSPs and IT support teams can use these metrics to select AI models based on their existing budget and latency constraints. The results show that increasing reasoning effort provides marginal gains in accuracy at significantly higher costs, necessitating a tiered approach to automated ticket triage.
The takeaway
The data demonstrates that for complex technical tasks, model accuracy is currently tied to significant increases in latency and cost. Industry observers should watch for how AI labs respond to these findings in subsequent model versions to reduce the current trade-off between reasoning depth and operational efficiency.
Further reading
For more on the current state of enterprise deployment, visit Artificial Intelligence.
Source note: This article includes information reported by Channel Insider.
Live Poll
Do you trust AI agents to handle complex customer support requests without human intervention?
