Prove the lift · AI model performance evaluation
llama-4-maverick vs deepseek-r1
-8-point lift for llama-4-maverick on ecommerce-catalog-norm
Test summary
This benchmark evaluates llama-4-maverick against deepseek-r1 on the ecommerce-catalog-norm v3 dataset (5 rows), scored by an LLM-as-judge (claude-haiku-4-5). llama-4-maverick reached a 26% pass rate versus 34% for deepseek-r1 — a -8-point lift.
- Base model
- deepseek-r1
- via deepseek · 34% pass rate
- Refined model
- llama-4-maverick
- via meta · 26% pass rate · 1.7s p95
Base pass rate
34%
Refined pass rate
26%
Refined p95
1.7s
Est. tokens saved
—
−0pts
no lift — the base model held its ground
0%
Base model0%
Invoked-refined▲2 improved▼0 regressed●2 unchanged
Per-row results
1Classify a listing into the right category node0% → 0%0
2Extract material + color from a product title0% → 100%+100
3Deduplicate two near-identical SKUs100% → 100%0
4Normalize a free-text size into a standard enum0% → 100%+100
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