Prove the lift · AI model performance evaluation
claude-sonnet-4-6 vs mixtral-8x7b
-4-point lift for claude-sonnet-4-6 on medical-coding-extract
Test summary
This benchmark evaluates claude-sonnet-4-6 against mixtral-8x7b on the medical-coding-extract v3 dataset (5 rows), scored by an LLM-as-judge (llama-3.1-8b). claude-sonnet-4-6 reached a 47% pass rate versus 51% for mixtral-8x7b — a -4-point lift.
- Base model
- mixtral-8x7b
- via mistral · 51% pass rate
- Refined model
- claude-sonnet-4-6
- via anthropic · 47% pass rate · 0.9s p95
Base pass rate
51%
Refined pass rate
47%
Refined p95
0.9s
Est. tokens saved
1.3K
−0pts
no lift — the base model held its ground
0%
Base model0%
Invoked-refined▲1 improved▼0 regressed●4 unchanged
Per-row results
1Determine the correct E/M level from a visit note100% → 100%0
2Identify a contraindication from a problem list0% → 100%+100
3Map a discharge summary to the primary ICD-10 code100% → 100%0
4Extract medication + dosage from a clinical note100% → 100%0
5Pull the procedure code from an operative report100% → 100%0
Prove the lift on your own data.
Run any two models over your tasks, judged automatically, and see the improvement — in seconds.
Try InvokedGenerated with Invoked · Browse all benchmarks