Cost Calculator

How much does resume parsing cost?

Compare 32 AI models for resume parsing. Prices shown at 10K calls/month with default token counts.

Data extraction tasks parse unstructured text into structured output like JSON formats. Input includes the raw document that the model processes for structured data. Output is compact and well-structured, keeping output token costs relatively low overall.

What drives resume parsing cost

Resumes are longer than invoices and less uniformly structured, so input runs higher while output stays compact. Like invoice parsing this is a bulk, latency-tolerant workload where the batch API discount applies to the entire request. The parsing instructions are identical across every candidate, which puts them squarely in the cached prefix.

Recommendations

Top picks for resume parsing

Best quality

GPT 6 Astra

79/100 confidence

Best value

Gemini 3 Flash

86/100 confidence · $40/mo

Budget pick

GPT 6 Luna

75/100 confidence · $7/mo

Frequently asked questions

How much does resume parsing cost per API call?

Resumes are longer than invoices and less uniformly structured, so input runs higher while output stays compact. Like invoice parsing this is a bulk, latency-tolerant workload where the batch API discount applies to the entire request. The parsing instructions are identical across every candidate, which puts them squarely in the cached prefix. Across the 32 models we track, a typical call uses about 2,500 input and 800 output tokens.

What is the cheapest model for resume parsing?

GPT 6 Luna from GPT is the lowest-cost model we track for resume parsing, at roughly $7.00 for 10,000 calls per month. It scores 75 out of 100 on this task type.

Which model is best for resume parsing?

GPT 6 Astra from GPT ranks highest for resume parsing, scoring 79 out of 100. Confidence scores are a third-party benchmark snapshot from week 26 of 2026. This snapshot is 17 weeks old, so treat the scores as directional and validate against your own evals.

See your real resume parsing costs

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