Free tools Windows power users keep installed
One-click scans. No signup required.
iTechGuides is reader-supported. When you buy through links on our site, we may earn an affiliate commission. As an Amazon Associate I earn from qualifying purchases. Learn more
On one ServiceNow configuration task, an SNcode-published comparison reported that SNcode used 393,000 cost-weighted tokens, versus 2.05 million for Claude Code with the ServiceNow SDK and 1.31 million for Build Agent. The figures suggest that prompts, tools and domain context can affect an agent workflow—but this was a single vendor-authored run, not evidence that most teams will save 5.2×.
What the comparison tested
The task was to add two fields to a ServiceNow Incident record, show them on the Incident form without replacing existing fields, and create a business rule that blocks resolution in one case. The comparison article says all three workflows used Claude Sonnet and received the same initial task prompt. Its recorded Claude Code and Build Agent runs also received an additional instruction not to overwrite the existing form.
The approaches were SNcode, Claude Code with the ServiceNow SDK, and Build Agent. The comparison was published by SNcode, one of the products being compared, so its measurements and interpretation should be read as vendor-reported results rather than an independent evaluation.
Reported results—and what the numbers mean
| Workflow | Rounds | Elapsed time | Cost-weighted tokens | Reported outcome |
|---|---|---|---|---|
| SNcode | 1 | 7:44 | 393K | Made and tested the changes. |
| Claude Code + ServiceNow SDK | 2 | 22:30 | 2.05M | The business rule initially failed; a general follow-up was used to fix it, and the second round tested through the API. |
| Build Agent | 2 | 21:50 | 1.31M | The article says it did not test its work. It also reports a form-layout issue, later corrected with duplicate fields. |
These are figures reported by the SNcode article for one task run in 2025, not repeated benchmark averages. Its claim that SNcode used 5.2× fewer cost-weighted tokens than Claude Code with the SDK and 3.3× fewer than Build Agent comes from dividing the reported totals for this run.
#1 Best Overall
- NVIDIA Volta GV100 Architecture — 4,608 CUDA Cores, 640 1st-Gen Tensor Cores delivering 14 TFLOPS FP32 and 112 TFLOPS deep learning performance for AI training, inference, HPC, and scientific computing workloads
- 32GB HBM2 ECC Memory — 900 GB/s Bandwidth — High-bandwidth memory on a 4096-bit bus with ECC error correction provides the memory capacity and throughput required for the largest AI models, simulations, and datasets
- PCIe 3.0 x16 Interface — 250W TDP — Standard PCIe Gen3 connectivity with passive cooling designed for enterprise rack server deployment in HPE ProLiant, Dell PowerEdge, and Supermicro platforms with adequate chassis airflow
- NVLink — Scale to 96GB Unified Memory — Connect two V100 GPUs via NVLink at 300 GB/s bi-directional bandwidth to scale GPU memory from 32GB to 96GB for larger AI training and HPC workloads
- Multi-Precision Computing — Supports FP64 (7 TFLOPS), FP32 (14 TFLOPS), FP16 (112 TFLOPS) and INT8 precision modes for flexible deployment across training, inference, and scientific simulation workloads
“Cost-weighted tokens” is the article’s custom aggregate, not raw tokens: output tokens count at 5×, cache writes at 1.25×, and cache reads at 0.1× an input token. The article says it derived those weights from Claude pricing ratios. Because output, cache-write and cache-read tokens receive different weights, the totals cannot be treated as ordinary token counts or compared without the formula.
The article also reports that Claude Code fetched 13 ServiceNow SDK documentation topics, amounting to more than 85 KB of text, and accumulated 14.8 million cache-read tokens over two rounds. Those are vendor-reported figures, not independently audited measurements. They help describe what happened in that run, but do not establish how much of the token difference each documentation fetch caused.
Rank #2
- High-Performance AI Processing: The MX3 is designed to handle the most demanding AI computer vision workloads, delivering exceptional performance and efficiency.
- Flexible Integration: The MX3 can be easily integrated into your existing systems via its M.2 M-key form factor and support for Linux operating systems.
- Energy Efficient: The MX3 is designed to provide high performance while minimizing power consumption.
- Comprehensive Software Development Kit (SDK): The MX3 is supported by a comprehensive SDK that simplifies development and deployment.
- Hardware compatability: The MX3 is compatible with the PCI-SIG M.2 M-key 2280 Specification. It can be used with the Raspberry Pi 5 with a M-key 2280 HAT.
Why prompts and tools could affect the result
With the underlying model held constant in this comparison, the SNcode author points to differences in system prompts, tools and skills as possible reasons for the gap. The proposed mechanism is practical: an agent that receives compact, relevant instructions and focused tools may spend less context on broad documentation, make fewer unproductive calls and need fewer correction rounds.
Recommended Free Tools
The article’s recommendations are to encode recurring ServiceNow-specific instructions in the system prompt, offer a small set of tools with compact outputs, and keep product knowledge in short, targeted skills instead of repeatedly loading broad documentation. It also proposes refining skills through an optimization loop. These are the author’s recommendations, not factors isolated in controlled experiments: the comparison does not show how much each change contributed independently.
Rank #3
- ✅Powered by 26 Tera-Operations Per Second (TOPS) Hailo-8 AI Processor. 2.5W typical power consumption
- ✅Scalable, enabling simultaneous processing of multi-streams & multi-models
- ✅Enabling real-time, low latency and high-efficiency AI inferencing on the edge devices
- ✅Supports TensorFlow, TensorFlow Lite, ONNX, Keras, Pytorch frameworks
- ✅Supports Linux and Windows. Supports the temperature range of -40°C to 85°C
There are also meaningful differences in execution and verification. SNcode reportedly finished and tested in one round; Claude Code needed two rounds and used SDK documentation; Build Agent did not test its result. A token total alone therefore does not establish which workflow produced the safest or most complete configuration. Form preservation, acceptance tests, retries and final-state verification all matter.
What broader ServiceNow agent research adds
A single configuration task cannot establish typical performance across ServiceNow work. ServiceNow AI Research’s WorkArena paper, published in 2024, describes a remote-hosted benchmark of 29 ServiceNow tasks. Its authors wrote: “Our empirical evaluation reveals that while current agents show promise on WorkArena, there remains a considerable gap towards achieving full task automation.” WorkArena offers a broader evaluation frame; it does not validate the SNcode article’s token measurements.
Rank #4
- 48GB AI graphics accelerator
A separate ServiceNow AI Research paper published in October 2026 examines whether online skills and memory modules justify their repeated token overhead under a fixed inference budget. Its abstract reports that, across three WebArena domains and three models, a token-matched vanilla baseline matched or surpassed three augmentation methods in aggregate success rate while often using fewer total tokens; it reports a similar trend on WorkArena-L1 with Qwen 3.6-27B. The paper also notes material run-to-run variation. This is related evidence that added context should be tested against alternatives using the same budget—not a comparison of SNcode, Claude Code and Build Agent on the Incident task.
How to evaluate an agent workflow fairly
For a useful comparison, teams should hold the task and environment constant, then measure efficiency and correctness separately. A lower token count is valuable only if the agent reaches the requested state without damaging existing configuration.
Best Value
- Professional AI & Creator Workstation: AMD Radeon AI PRO R9700 GPU with 32GB GDDR6 is engineered for AI development, professional content creation, and compute-intensive workloads.
- Massive 32GB Memory Capacity: 32GB of GDDR6 memory on a 256-bit bus provides ample bandwidth for large AI models, 8K video editing, and complex 3D rendering.
- Advanced RDNA 4 with AI Accelerators: 64 Compute Units with 3rd Gen Ray Tracing and dedicated 2nd Gen AI Accelerators for groundbreaking AI performance and visual computing.
- Professional Blower Cooling: Efficient single blower design exhausts heat directly out of the chassis, ideal for multi-GPU workstation and server configurations.
- Enterprise-Grade Thermal Solution: Vapor chamber heatsink with industrial Honeywell PTM7950 thermal interface material ensures reliable cooling under sustained professional loads.
- Use the same task prompt, model and version, ServiceNow instance and starting state, permissions, and tool access.
- Define success before running the comparison, including whether the new fields appear on the right form, existing layout is preserved, and the business rule passes its acceptance tests.
- Repeat runs. One run cannot reveal variance or show that the result generalizes.
- Report raw input, output, cache-write and cache-read tokens separately; disclose any weighting formula used to create a composite figure.
- Record elapsed time, rounds or retries, documentation and skill payload sizes, and whether the final state was verified by tests or inspection.
- Disclose affiliations and separate measured results from explanations about why one approach performed differently.
For teams building their own ServiceNow agents, the strongest takeaway is not to assume that a particular prompt style guarantees a fixed saving. Test concise, task-specific context and focused tools against a token-matched alternative, and judge the result by verified task completion as well as resource use.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
