JevGraph v0.2.0 — Three-model relation benchmark

What changed

• Added strict Vercel AI Gateway benchmark adapters for GPT-5.6 Luna and DeepSeek V4.1 Flash.
• Froze a same-sample, same-ontology FewRel comparison against Jev.
• Preserved model failures as evidence: DeepSeek required an 8 → 4 → 2 adaptive batch continuation after incomplete completions.
• Clarified the architecture boundary: the model only selects a relation for an existing candidate pair; it does not discover entities or pairs.
• Published aggregate evidence and a complete budget audit without committing FewRel source text.

Frozen pilot

Model	Raw accuracy	Successful / attempted requests	p50 latency	Known cost
Jev	85.0%	20 / 21	444 ms	$0 reported
GPT-5.6 Luna	80.0%	20 / 20	5.15 s	$0.0144112
DeepSeek V4.1 Flash	83.75%	51 / 53	4.4 s	$0.10255982

DeepSeek latency is a mixed-batch operational result with a 22.3 s p95. Jev alone returned the full probability/confidence contract, so its gated risk/coverage result is reported separately from raw accuracy.

Verification

• Python 3.11 and 3.13 CI passed
• 14 tests passed
• Ruff passed
• wheel and source distribution attached

Known billed chat-experiment cost was $0.150184 across exploratory and final calls. Including five unknown-cost failed requests across all experiments, a conservative ceiling remains below $0.315, inside the approved $5 cap.