AI Model Benchmark

Real-world e-commerce customer service scenarios, blind evaluation by multiple AI judges

This benchmark compares ChatGPT, Claude, Gemini, and Grok on real e-commerce customer service questions. It is designed for teams choosing the best AI model for support chat, help desk automation, and AI sales-assistant workflows.

Current leader: ChatGPT 5.4 with an average score of 70.4 across 30 shared questions and 1352 blind evaluations.

30 questions evaluated 1352 evaluations performed Last updated: Apr 19, 2026
Leaderboard of AI models ranked by blind evaluation scores on shared e-commerce customer service questions.
# Model Provider Overall Score Avg Response
1 ChatGPT 5.4 OpenAI
70.4
8.3s
2 Claude Sonnet 4.6 Anthropic
67.1
6.9s
3 ChatGPT 5.4 mini OpenAI
66.3
3.7s
4 Claude Opus 4.7 Anthropic
65.5
32.4s
5 Claude Haiku 4.5 Anthropic
64.6
4.5s
6 ChatGPT 4.1 OpenAI
64.2
5.4s
7 Claude Opus 4.6 Anthropic
64.1
10.3s
8 ChatGPT 4.1 mini OpenAI
63.5
4.9s
9 Gemini 3.1 Pro Preview Google
61.9
13.4s
10 Gemini 3.1 Flash-Lite Google
60.4
2.8s
11 Grok 4.1 Fast xAI
59.1
3.8s
12 Grok 4.20 xAI
56.2
3.0s
13 Gemini 3 Flash Google
54.2
10.7s

Score Breakdown

Per-criterion benchmark scores showing how each model performs on accuracy, relevance, completeness, helpfulness, tone, and conciseness.
Model Accuracy (30%) Relevance (20%) Completeness (15%) Helpfulness (15%) Tone (10%) Conciseness (10%)
ChatGPT 5.4 61.2 79.3 66.5 68.9 84.4 73.9
Claude Sonnet 4.6 54.7 78.2 61.5 64.0 84.7 77.4
ChatGPT 5.4 mini 58.3 74.4 59.1 62.4 80.3 77.2
Claude Opus 4.7 51.9 77.5 60.6 64.4 80.8 75.8
Claude Haiku 4.5 52.0 75.8 60.0 60.9 83.3 74.1
ChatGPT 4.1 48.2 77.9 61.1 61.0 84.7 73.8
Claude Opus 4.6 47.1 78.2 62.8 59.8 85.0 74.3
ChatGPT 4.1 mini 49.7 76.0 58.3 58.8 83.0 75.0
Gemini 3.1 Pro Preview 61.1 68.1 48.1 54.9 79.1 66.1
Gemini 3.1 Flash-Lite 45.9 71.9 57.8 56.8 82.7 68.0
Grok 4.1 Fast 42.7 73.3 57.1 54.6 79.1 69.5
Grok 4.20 34.1 74.4 55.3 51.2 80.8 70.5
Gemini 3 Flash 45.9 65.2 47.8 47.1 75.2 56.2

How It Works

Real Questions

Selected from actual production customer service conversations in e-commerce.

Same Prompt

All models receive the identical system prompt, knowledge base, and question.

Blind Evaluation

Evaluators see only 'Answer A', 'Answer B' — they don't know which model wrote it.

Cross-Evaluation

Top-tier models from each provider evaluate answers. No model judges its own response.

Scoring Criteria

Each answer is scored 0-100 on six criteria with the following weights:

Accuracy 30%
Relevance 20%
Completeness 15%
Helpfulness 15%
Tone 10%
Conciseness 10%

To keep the comparison fair, public scores are calculated only from questions answered by every model included in the selected comparison set. That prevents newer or retired models from benefiting from an easier question mix.

Frequently Asked Questions

This benchmark measures how well leading AI models handle real customer service tasks for online stores. It focuses on practical support quality — accuracy, helpfulness, tone, and conciseness — rather than coding, math, or generic reasoning tests.

The best model depends on your store, language mix, product complexity, and speed requirements. This page shows which models currently perform best in our blind benchmark, helping you shortlist candidates for your own live testing.

Each provider has strengths. ChatGPT models tend to be fast and widely supported. Claude models often excel at nuanced, context-heavy responses. Gemini models offer strong multilingual capabilities. Grok models provide competitive performance at lower latency. Check the leaderboard above for the latest blind comparison.

Every model receives the identical question, system prompt, and knowledge base. Their answers are then labeled anonymously (Answer A, Answer B, etc.) and scored by top-tier AI judges from each provider — OpenAI, Anthropic, Google, and xAI. No model evaluates its own response, eliminating self-evaluation bias.

Yes. The questions come from real production conversations in online stores, including Shopify, Shoptet, WooCommerce, and others. Use the leaderboard as a starting point, then test top models with your own product catalog and brand tone before going live.

Use the leaderboard as a decision aid, not as the only deciding factor. Start with the highest-ranked models, then test them on your own knowledge base, brand tone, and response speed requirements before rolling out in production.

We add new models as providers release them and periodically expand the question set with fresh real-world scenarios. When a new model is added, it is tested on the same shared questions as all existing models to keep the comparison fair.
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