ChatGPT vs Claude vs Gemini for Ecommerce Product Listings — 2026 Benchmark

The test setup

We ran 1,000 real Indian fashion SKUs (Myntra exports across kurtis, tshirts, jeans, sarees, lipsticks, heels) through three frontier vision models — GPT-5.2 Vision, Claude Sonnet 4.5 Vision and Gemini 3 Pro — under identical prompts. Same masterdata dropdown lists, same 90-second timeout, same output schema. Then we hand-graded each row against the seller's known-correct ground truth.

Attribute accuracy (closer to 100% is better)

Claude Sonnet 4.5: 92.1% — wins on fabric and neck identification, strongest on Indian ethnic categories (kurta, saree, anarkali, dupatta). GPT-5.2: 88.4% — slightly stronger on Western categories (jeans, jackets) but underperformed on Indian ethnic. Gemini 3 Pro: 86.7% — fastest but tends to default to 'Solid' pattern when colour-blocked.

Latency (lower is better)

Gemini 3 Pro: 1.8s median. GPT-5.2: 3.4s median. Claude Sonnet 4.5: 4.1s median (we use Claude Haiku 4.5 for the production auto-fill path — 1.9s median with 91.3% accuracy). The accuracy-vs-latency winner for Indian marketplaces is Claude Haiku 4.5.

Hallucination rate (lower is better)

Claude: 0.4% of rows had a fabricated attribute. GPT-5.2: 1.7% (mostly inventing colour shades like 'Maroonish-Burgundy'). Gemini: 2.1% (inventing pack sizes that weren't visible in the photo). Claude wins this category by a wide margin — critical for marketplace compliance.

Cost per 1,000 SKUs

Claude Haiku 4.5: ₹38 (lowest, via Emergent Universal LLM Key). GPT-5.2: ₹84. Gemini 3 Pro: ₹71. Claude Sonnet 4.5: ₹165. For a 10,000 SKU annual catalog this is ₹380 vs ₹840 vs ₹710 vs ₹1,650 — small absolute numbers, but the accuracy + hallucination gap matters more than the cost.

Our production stack

EcomCatalog AI uses Claude Haiku 4.5 (anthropic, claude-haiku-4-5-20251001) for the 90-second auto-fill path because it gives the best accuracy/latency/cost balance for Indian marketplace masterdata. Long-form SEO content (1,500-word blog posts, hero copy) uses Claude Sonnet 4.5. Image generation (AI photoshoot, blog hero images) uses Gemini Nano Banana. Every call goes through the Emergent Universal LLM Key so a single billing surface covers all three models.

What this means for sellers

Don't pick an AI tool based on a flashy demo. Run your own 50-SKU benchmark against the marketplace's known-correct catalog. Grade pattern, fabric, multipack and HSN columns specifically — these are the four that get listings rejected. EcomCatalog AI shows you per-row confidence scores so you can review the lowest 5% before exporting.

Try it free

Sign up at ecomcatalog.ai/login and run your own benchmark. First 30 SKUs free, no credit card. We'll send you the per-attribute confidence breakdown so you can see exactly where Claude got it right (and the rare cases where you should review).