{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:24NU3PBSHPSXYQ2CWFQ6IZVTTC","short_pith_number":"pith:24NU3PBS","schema_version":"1.0","canonical_sha256":"d71b4dbc323be57c4342b161e466b398b0fefd9f222b0fb5d938d9bf7dca6b38","source":{"kind":"arxiv","id":"2312.15011","version":1},"attestation_state":"computed","paper":{"title":"Gemini vs GPT-4V: A Preliminary Comparison and Combination of Vision-Language Models Through Qualitative Cases","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dahua Lin, Hengshuang Zhao, Jiaqi Wang, Mengchen Zhang, Tong Wu, Ye Fang, Zeyi Sun, Zhangyang Qi, Ziwei Liu","submitted_at":"2023-12-22T18:59:58Z","abstract_excerpt":"The rapidly evolving sector of Multi-modal Large Language Models (MLLMs) is at the forefront of integrating linguistic and visual processing in artificial intelligence. This paper presents an in-depth comparative study of two pioneering models: Google's Gemini and OpenAI's GPT-4V(ision). Our study involves a multi-faceted evaluation of both models across key dimensions such as Vision-Language Capability, Interaction with Humans, Temporal Understanding, and assessments in both Intelligence and Emotional Quotients. The core of our analysis delves into the distinct visual comprehension abilities "},"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":"2312.15011","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-12-22T18:59:58Z","cross_cats_sorted":[],"title_canon_sha256":"f5bfd03faee633229aa9bdb08669690e91c407a6ab0423ee2a65ea43c598101e","abstract_canon_sha256":"2e6446bcfb6799c6aca5e4de15b74e84dd6e5b00e1df83e2f0404d13e2fb9352"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:27:29.713930Z","signature_b64":"CikxuMxseQzOQlGV1QpamVoMRUzBN93unbp88U0z4F/LrKfpZILtfS0W1choBSIPSLlsz1ZdAuhsfFdOHTfmBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d71b4dbc323be57c4342b161e466b398b0fefd9f222b0fb5d938d9bf7dca6b38","last_reissued_at":"2026-07-05T07:27:29.713479Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:27:29.713479Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Gemini vs GPT-4V: A Preliminary Comparison and Combination of Vision-Language Models Through Qualitative Cases","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dahua Lin, Hengshuang Zhao, Jiaqi Wang, Mengchen Zhang, Tong Wu, Ye Fang, Zeyi Sun, Zhangyang Qi, Ziwei Liu","submitted_at":"2023-12-22T18:59:58Z","abstract_excerpt":"The rapidly evolving sector of Multi-modal Large Language Models (MLLMs) is at the forefront of integrating linguistic and visual processing in artificial intelligence. This paper presents an in-depth comparative study of two pioneering models: Google's Gemini and OpenAI's GPT-4V(ision). Our study involves a multi-faceted evaluation of both models across key dimensions such as Vision-Language Capability, Interaction with Humans, Temporal Understanding, and assessments in both Intelligence and Emotional Quotients. The core of our analysis delves into the distinct visual comprehension abilities "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.15011","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/2312.15011/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":"2312.15011","created_at":"2026-07-05T07:27:29.713549+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.15011v1","created_at":"2026-07-05T07:27:29.713549+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.15011","created_at":"2026-07-05T07:27:29.713549+00:00"},{"alias_kind":"pith_short_12","alias_value":"24NU3PBSHPSX","created_at":"2026-07-05T07:27:29.713549+00:00"},{"alias_kind":"pith_short_16","alias_value":"24NU3PBSHPSXYQ2C","created_at":"2026-07-05T07:27:29.713549+00:00"},{"alias_kind":"pith_short_8","alias_value":"24NU3PBS","created_at":"2026-07-05T07:27:29.713549+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/24NU3PBSHPSXYQ2CWFQ6IZVTTC","json":"https://pith.science/pith/24NU3PBSHPSXYQ2CWFQ6IZVTTC.json","graph_json":"https://pith.science/api/pith-number/24NU3PBSHPSXYQ2CWFQ6IZVTTC/graph.json","events_json":"https://pith.science/api/pith-number/24NU3PBSHPSXYQ2CWFQ6IZVTTC/events.json","paper":"https://pith.science/paper/24NU3PBS"},"agent_actions":{"view_html":"https://pith.science/pith/24NU3PBSHPSXYQ2CWFQ6IZVTTC","download_json":"https://pith.science/pith/24NU3PBSHPSXYQ2CWFQ6IZVTTC.json","view_paper":"https://pith.science/paper/24NU3PBS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.15011&json=true","fetch_graph":"https://pith.science/api/pith-number/24NU3PBSHPSXYQ2CWFQ6IZVTTC/graph.json","fetch_events":"https://pith.science/api/pith-number/24NU3PBSHPSXYQ2CWFQ6IZVTTC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/24NU3PBSHPSXYQ2CWFQ6IZVTTC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/24NU3PBSHPSXYQ2CWFQ6IZVTTC/action/storage_attestation","attest_author":"https://pith.science/pith/24NU3PBSHPSXYQ2CWFQ6IZVTTC/action/author_attestation","sign_citation":"https://pith.science/pith/24NU3PBSHPSXYQ2CWFQ6IZVTTC/action/citation_signature","submit_replication":"https://pith.science/pith/24NU3PBSHPSXYQ2CWFQ6IZVTTC/action/replication_record"}},"created_at":"2026-07-05T07:27:29.713549+00:00","updated_at":"2026-07-05T07:27:29.713549+00:00"}