How to compare AI models for your Shopify workflows

Choose an AI model by testing the work your store needs completed. Catalogue edits, support drafts and storefront code have different failure costs. A fluent answer can still invent a product attribute or apply the wrong returns policy.

The September release cycle gives ecommerce teams more options to evaluate. This is a practical selection framework, not a hands-on model ranking or evidence that switching models increases store conversion.

Three candidates to investigate

Primary pages checked on 23 October describe these options:

• GPT-6.1 Sol: OpenAI positions it as a lower-cost option for complex coding, computer use and professional work, and recommends comparing it with Astra on your own tasks.
• Claude Sonnet 5.5: Anthropic’s 28 September announcement describes a faster, lower-cost complement to Opus 5.5 for well-scoped tasks, bug fixes and documents. Reported speed and task-cost gains are vendor results, not merchant savings guarantees.
• Grok 4.7: the 21 September announcement describes improvements in coding and knowledge work, with access through the Grok API and coding tools. This does not establish a Shopify integration.

These are candidates, not an exhaustive market list. API availability does not tell you which model, tools or permissions an application exposes to your account.

Claude Sonnet 5.5 is now available: https://t.co/DuxGkiPLRY

— Anthropic (@AnthropicAI) September 28, 2026

Anthropic’s 28 September launch post. Read on X.

Build a representative test set

Our suggested starting point is a set of previously resolved tasks with known answers. Remove unnecessary personal data, retain the source material needed to do the work and give each candidate the same inputs. Include awkward cases as well as easy ones.

• Catalogue editing: request a description from an approved specification. Fail an answer that invents a material, dimension, compatibility claim or certification, even if the prose is better.
• Support drafting: supply a sample order scenario and the applicable policy. Check dates and conditions, unauthorised refund promises and whether missing information is flagged.
• Storefront maintenance: use a bounded bug with a reproducible example in a development environment. Require a reviewable change and checks demonstrating the fix without damaging adjacent behaviour.

Write pass conditions before the comparison. Have a reviewer score outputs without seeing the model name where practical. Record the prompt, version, settings and tool access so a surprising result can be reproduced. Repeat important cases: one successful answer is not a reliability estimate.

Measure accepted work, not cheap tokens

Track model and tool charges, review time and correction time against the number of accepted tasks. Keep cash charges and staff time separate unless you have agreed an hourly cost. Record unsupported claims, unnecessary actions and cases needing escalation alongside speed.

Check current pricing for the actual route you use. Context length, caching, processing tier and tool calls can change the bill. Subscription prices and API token rates describe different purchases. A cheap first response may require several retries; a more expensive model may add no value to a tightly constrained task.

Separate model choice from store access

Begin with read-only inputs and draft outputs. Review catalogue changes before saving, support replies before sending and code before deployment. Test permissions and approval controls in the surrounding application; a stronger model does not supply missing controls.

OpenAI’s September developer demonstration provides context for computer-use workflows. It is vendor material, not our Shopify test result.

Source: OpenAI, 4 September 2026. Watch the original demonstration.

Choose for the workflow

Adopt a candidate when it meets your acceptance criteria at a workable total cost. Keep the previous process available and rerun relevant cases when the model or integration changes. Different tasks can justify different choices.

For creative production, our UGC and AI ad testing guide separates producing more output from establishing that the output works.