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Airparser

Airparser Review: GPT-Powered Resume Parser Tested (2026)

Tested Hands-OnResume ParserGPT-Powered ExtractionJSON OutputPDF ParsingLast verified May 2026

Our take

Airparser is a strong GPT-powered resume parser that delivers clean, human-readable JSON output across all resume formats. It outperforms Affinda on CGPA capture, job title extraction, certification completeness, and soft skill inclusion. The schema is defined once in natural language and applied automatically to every file after that. Best choice when readable, selective JSON output is the priority over deep skill taxonomy metadata. Free trial available on signup.

In-Depth Review

Our detailed analysis of Airparser — features, performance, and real-world testing.

Rugved

AI Demos Team

Verified Review

Feature-by-Feature Breakdown

We tested each feature individually. Click any card to see inputs, outputs, and our observations.

Clean Resume Parsing

all major fields extracted with CGPA captured correctly

9/10

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Test Summary

Feature tested: Clean Resume Parsing

Result: Passed (9/10) — all major fields extracted with CGPA captured correctly

Airparser accepts a standard single-column PDF resume and extracts all defined fields into a clean, readable JSON structure. Field names are descriptive and values are plain strings rather than nested metadata objects. CGPA numeric value (8.2/10) captured correctly — a direct improvement over Affinda where the numeric score was missing.

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Airparser input.1.pdf

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airparser output 1.txt

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Bottom Line

Excellent output on clean resumes. All major fields extracted correctly including full name, email, phone, LinkedIn URL, location, both work experiences with full responsibilities, education with CGPA, both certifications, and skills in categorised sub-groups preserving the original grouping from the resume. One notable failure — email extracted as "rugged.nichite@email.com" instead of "rugved.nichite@email.com" — a hallucination misread on a clean, clearly formatted field. This is a concern for production use where contact information accuracy is critical.

Multi-Column Resume Parsing

Outstanding — strongest multi-column result across all tools tested

10/10

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Test Summary

Feature tested: Multi-Column Resume Parsing

Result: Passed (10/10) — Outstanding — strongest multi-column result across all tools tested

Airparser handles multi-column PDF layouts automatically without any layout hints or manual adjustment. Both the left column and right sidebar are parsed correctly and merged into a single structured output.

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Airparser input.2.pdf

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Airparser Output 2.json

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Bottom Line

Outstanding multi-column result. All fields from both columns extracted correctly including job title headline ("Software Engineer — Machine Learning") which was missing entirely in Affinda's output. CGPA (8.7/10) captured correctly. Both projects extracted with full descriptions including the "Accuracy: 89%" detail that was truncated in Affinda. Language proficiency levels preserved. Only weakness is skills returned as a flat list rather than grouped by category — a schema design issue rather than a parsing failure.

Messy Resume Parsing

Strong — both certifications and all soft skills captured unlike Affinda

8/10

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Test Summary

Feature tested: Messy Resume Parsing

Result: Passed (8/10) — Strong — both certifications and all soft skills captured unlike Affinda

Airparser handles non-standard resume formatting including inconsistent date formats, flat comma-separated skill lists, missing section headers, and mixed percentage formats. GPT reasoning infers field values even from poorly structured text without any special handling.

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Airparser input.3.pdf

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airparser output.3.txt

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Bottom Line

Strong result on the messy resume. Both certifications captured — Python from Udemy (2020) and AWS basics from Coursera (2022) — while Affinda missed the AWS certification entirely. All soft skills including good communication, team player, fast learner, and problem solving captured in the skills output, which Affinda did not capture. All 3 education entries extracted with marks despite inconsistent percentage formats. Weaknesses are skills returned as a single flat string rather than an array, marks field not normalised ("72 percent marks" instead of "72%"), and no firstName/lastName split.

Frequently Asked Questions

Pricing & Access

TESTED

Trial

Free

30 credits on signup, no credit card required. Parse up to 30 emails, documents, or PDF pages. All core features included including data export and integrations.

Starter

$39/mo

100 credits per month, GPT-powered parsing, JSON export, Google Sheets and Excel export, Zapier and Make integrations included.

Growth

$59/mo

500 credits per month, all Starter features plus batch processing and webhook support.

Business

$179/mo

2,000 credits per month, all Growth features plus priority support.

Enterprise

$549/mo

5,000 credits per month, all Business features plus dedicated support and custom integrations.

Pricing checked May 2026. We re-check quarterly. Annual plans available with ~17% discount equivalent to 2 months free. Visit airparser.com/pricing for current plans.

Is This Right For You?

A side-by-side guide based on our hands-on testing.

✓ Use This If

●You need clean, human-readable JSON output that is easy to work with downstream

●You want to define exactly which fields are extracted using plain natural language

●You need CGPA numeric values captured correctly from education entries

●You need soft skills extracted alongside technical skills

●You are working with multi-column resume layouts and need reliable sidebar parsing

●You need both certifications captured even from messy, unstructured resume inputs

✕ Skip This If

●You need deep skill taxonomy metadata with EMSI IDs, categories, and software flags — use Affinda instead

●You need guaranteed contact field accuracy for production use — the email hallucination on a clean resume is a concern

●You need skills returned as a structured array rather than a flat string for messy resume inputs

●You need firstName and lastName as separate fields — Airparser returns full name only unless the schema specifies otherwise

Use Case Track Record

Parse resumes into structured data using an API

Strong — clean readable JSON, better CGPA and certification capture than Affinda but email hallucination on clean input is a concern

Featured in Rankings

Independent rankings where Airparser was tested and rated.

RankingsBest

Best AI Tools for Parsing Resumes via API (2026)

This ranking evaluates AI resume parsing APIs based on their ability to...

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Yes. Airparser parsed a two-column resume layout with a sidebar correctly on first upload with no manual configuration or layout hints. Both the main column and sidebar content including certifications, skills, and language proficiency levels were extracted and merged into a single clean JSON output.

This is a direct advantage over Affinda. Airparser captured the CGPA numeric value (8.2/10 on Input 1 and 8.7/10 on Input 2) correctly as a string in the education output. Affinda identified the grade unit but consistently missed the actual numeric score.

Yes, and it was observed in testing. On the clean resume input, Airparser extracted the email as "rugged.nichite@email.com" instead of the correct "rugved.nichite@email.com" — misreading the name portion of the email address. This type of hallucination error on a clearly formatted field is a concern for any production use case where contact information accuracy is critical.

Yes. On the messy resume input, Airparser captured all soft skills from the resume including good communication, team player, fast learner, and problem solving — all of which Affinda missed. However the skills were returned as a flat string rather than an array of individual skill objects on the messy input.