Oleksandr Kuzmenko, EditorPublished 6 min readAI-assisted · editor-reviewed
TL;DR
- Decision models run up to 90x cheaper ($0.05-$0.10 per 1k records) and 10x faster (0.15s) than frontier LLMs.
- Error calibration matters more than raw accuracy when designing multi-model routing cascades.
- A hybrid harness using Jev for primary scoring and Claude Opus for ambiguous cases reduces API spend by >4x with zero accuracy loss.
Key facts
- Decision Model Cost
- $0.05 - $0.10 per 1,000 identities
- Decision Model Latency
- 0.15 - 0.17 seconds
- Jev Out-of-the-Box Accuracy
- 95.7% (at 60% threshold)
- GPT-6 Luna Accuracy
- 97.3% (at 70% threshold)
- Hybrid Harness Savings
- >4x lower cost vs standalone Opus
The Economics of Specialized Decision Models
Automating data quality audits such as customer identity resolution (IDR) requires verifying whether merged records represent the same individual. Frontier models like Claude Opus achieve human-level accuracy on tricky over-merging edge cases, but high token pricing and latency make auditing 100,000 identities slow and expensive.
Decision models like Jev and GPT-6 Luna represent a specialized class of models designed to output a calibrated float score (0 to 1) rather than standard conversational text tokens. Out of the box, they run at 0.15 to 0.17 seconds per identity and cost $0.05 to $0.10 per 1,000 evaluations — up to 90 times cheaper and 10 times faster than frontier models.
Confidence Calibration Enables Efficient Fallbacks
While Luna achieved higher out-of-the-box accuracy (97.3% at a 70% cutoff vs Jev's 95.7% at a 60% cutoff), Jev proved far better calibrated for routing harnesses. When Jev made errors, it reported low confidence; Luna's mistakes often arrived with high confidence, defeating threshold-based fallbacks.
By chaining Jev as the primary filter and escalating any score falling within the uncertainty band directly to Claude Opus, Hightouch maintained full frontier accuracy across edge-case benchmarks while cutting total evaluation costs by more than 75%.
Try it in 2 minutes
async def evaluate_identity(records, confidence_cutoff=0.60):
# Fast decision pass
decision = await jev.evaluate(records=records)
if decision.confidence >= confidence_cutoff:
return decision.is_valid
# Fallback to frontier model for ambiguous cases
return await claude_opus.evaluate(records=records)When to use
- Grading large batches (>10,000) of extraction or classification records where frontier models exceed budget limits.
- Running continuous CI/CD evaluation harnesses on production agent outputs.
- Tasks where mistakes cluster predictably around low-confidence score bands.
What to do today
- Audit existing evaluation and classification prompts to identify tasks reducible to binary or scalar decisions.
- Benchmark confidence calibration curves on small edge-case datasets before picking a fast classification model.
- Implement a confidence threshold routing layer in your agent harness to offload high-confidence inputs from frontier models.
Step names are taken from What to do today.
Step names are taken from What to do today. Audit existing evaluation and classification prompts to identify tasks reducible to binary or scalar decisions.. Benchmark confidence calibration curves on small edge-case datasets before picking a fast classification model.. Implement a confidence threshold routing layer in your agent harness to offload high-confidence inputs from frontier models.
- Audit existing evaluation and classification prompts to identify tasks reducible to binary or scalar decisions.
- Benchmark confidence calibration curves on small edge-case datasets before picking a fast classification model.
- Implement a confidence threshold routing layer in your agent harness to offload high-confidence inputs from frontier models.
What the community says
It could make sense to use a cheap model to decide if you need to run a long, high effort or otherwise costly effort. Then you can take many decisions at low cost, but think hard when it’s time.