{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:4PMOIQP76HB4HM5KWPOVQWTUIP","short_pith_number":"pith:4PMOIQP7","schema_version":"1.0","canonical_sha256":"e3d8e441fff1c3c3b3aab3dd585a7443f1c186503a67e005c7e92aa3eb355de5","source":{"kind":"arxiv","id":"2508.03931","version":1},"attestation_state":"computed","paper":{"title":"Analyzing Prominent LLMs: An Empirical Study of Performance and Complexity in Solving LeetCode Problems","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Asish Nelapati, Chandan Shivalingaiah, Everton Guimaraes, Nathalia Nascimento","submitted_at":"2025-08-05T21:50:52Z","abstract_excerpt":"Large Language Models (LLMs) like ChatGPT, Copilot, Gemini, and DeepSeek are transforming software engineering by automating key tasks, including code generation, testing, and debugging. As these models become integral to development workflows, a systematic comparison of their performance is essential for optimizing their use in real world applications. This study benchmarks these four prominent LLMs on one hundred and fifty LeetCode problems across easy, medium, and hard difficulties, generating solutions in Java and Python. We evaluate each model based on execution time, memory usage, and al"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2508.03931","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.SE","submitted_at":"2025-08-05T21:50:52Z","cross_cats_sorted":[],"title_canon_sha256":"37a77a6505a92ef005664f2fd33d7f21e25ddebf15acdc615cd48b76418d8c3e","abstract_canon_sha256":"875b45936972d982affe097642d1a36e11cb9afdd3e22449a9f417fb06e8ce7f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:49:17.449729Z","signature_b64":"Btna6skH3uYW7z7krRPsXl7gtRnZF+cDNt9WlTYHRf3VP7PDXb27u0wonwiBYIypIBpdskS9kFa9RZWmHyuLDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e3d8e441fff1c3c3b3aab3dd585a7443f1c186503a67e005c7e92aa3eb355de5","last_reissued_at":"2026-07-05T11:49:17.449219Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:49:17.449219Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Analyzing Prominent LLMs: An Empirical Study of Performance and Complexity in Solving LeetCode Problems","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Asish Nelapati, Chandan Shivalingaiah, Everton Guimaraes, Nathalia Nascimento","submitted_at":"2025-08-05T21:50:52Z","abstract_excerpt":"Large Language Models (LLMs) like ChatGPT, Copilot, Gemini, and DeepSeek are transforming software engineering by automating key tasks, including code generation, testing, and debugging. As these models become integral to development workflows, a systematic comparison of their performance is essential for optimizing their use in real world applications. This study benchmarks these four prominent LLMs on one hundred and fifty LeetCode problems across easy, medium, and hard difficulties, generating solutions in Java and Python. We evaluate each model based on execution time, memory usage, and al"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.03931","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2508.03931/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2508.03931","created_at":"2026-07-05T11:49:17.449279+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.03931v1","created_at":"2026-07-05T11:49:17.449279+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.03931","created_at":"2026-07-05T11:49:17.449279+00:00"},{"alias_kind":"pith_short_12","alias_value":"4PMOIQP76HB4","created_at":"2026-07-05T11:49:17.449279+00:00"},{"alias_kind":"pith_short_16","alias_value":"4PMOIQP76HB4HM5K","created_at":"2026-07-05T11:49:17.449279+00:00"},{"alias_kind":"pith_short_8","alias_value":"4PMOIQP7","created_at":"2026-07-05T11:49:17.449279+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.17514","citing_title":"Unlocking LLM Code Correction with Iterative Feedback Loops","ref_index":24,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4PMOIQP76HB4HM5KWPOVQWTUIP","json":"https://pith.science/pith/4PMOIQP76HB4HM5KWPOVQWTUIP.json","graph_json":"https://pith.science/api/pith-number/4PMOIQP76HB4HM5KWPOVQWTUIP/graph.json","events_json":"https://pith.science/api/pith-number/4PMOIQP76HB4HM5KWPOVQWTUIP/events.json","paper":"https://pith.science/paper/4PMOIQP7"},"agent_actions":{"view_html":"https://pith.science/pith/4PMOIQP76HB4HM5KWPOVQWTUIP","download_json":"https://pith.science/pith/4PMOIQP76HB4HM5KWPOVQWTUIP.json","view_paper":"https://pith.science/paper/4PMOIQP7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.03931&json=true","fetch_graph":"https://pith.science/api/pith-number/4PMOIQP76HB4HM5KWPOVQWTUIP/graph.json","fetch_events":"https://pith.science/api/pith-number/4PMOIQP76HB4HM5KWPOVQWTUIP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4PMOIQP76HB4HM5KWPOVQWTUIP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4PMOIQP76HB4HM5KWPOVQWTUIP/action/storage_attestation","attest_author":"https://pith.science/pith/4PMOIQP76HB4HM5KWPOVQWTUIP/action/author_attestation","sign_citation":"https://pith.science/pith/4PMOIQP76HB4HM5KWPOVQWTUIP/action/citation_signature","submit_replication":"https://pith.science/pith/4PMOIQP76HB4HM5KWPOVQWTUIP/action/replication_record"}},"created_at":"2026-07-05T11:49:17.449279+00:00","updated_at":"2026-07-05T11:49:17.449279+00:00"}