deepseek-chat-v3.1 vs inkling-small for extraction
As of August 24, 2026, inkling-small measures 0.985 on information extraction vs 0.962 for deepseek-chat-v3.1 (+0.023), at 1.7× the price ($0.5747 vs $0.3355 per 1K requests).
same suite, same items, same scoring — frontier v4, measured 2026-08-24 · method
| model | vendor | measured quality | $ / 1K requests | p95 latency |
|---|---|---|---|---|
| deepseek-chat-v3.1 | deepseek | 0.962 | $0.3355 | 13033 ms |
| inkling-small | thinkingmachines | 0.985 | $0.5747 | 15156 ms |
Extraction — pulling fields out of documents, emails, and records into structured JSON — is scored field-by-field against known references, so the numbers below are deterministic, not judged. It is also the workload where reference-free LLM judging fails measurably (judges cannot see omissions), which is why extraction claims anywhere should be treated with suspicion unless the scoring method is stated.
These two are part of a larger measured frontier — what is the best model for data extraction shows every measured option for this workload, and other workloads rank these models differently: a model that wins here can lose on another kind of work, which is the whole argument for routing per workload rather than picking one model for everything.
Potion routes each request to the cheapest option measured at your quality bar — including picks this public page does not name. Get an API key or read the docs.