[et_pb_section fb_built=\u201e1\u201c _builder_version=\u201e4.16\u201c custom_padding=\u201e0px||0px||true\u201c da_disable_devices=\u201eoff|off|off\u201c locked=\u201eoff\u201c global_colors_info=\u201c{}\u201c da_is_popup=\u201eoff\u201c da_exit_intent=\u201eoff\u201c da_has_close=\u201eon\u201c da_alt_close=\u201eoff\u201c da_dark_close=\u201eoff\u201c da_not_modal=\u201eon\u201c da_is_singular=\u201eoff\u201c da_with_loader=\u201eoff\u201c da_has_shadow=\u201eon\u201c][et_pb_row _builder_version=\u201e4.27.4\u201c custom_padding=\u201e0px||||false|false\u201c global_colors_info=\u201c{}\u201c][et_pb_column type=\u201e4_4\u201c _builder_version=\u201e4.16\u201c custom_padding=\u201c|||\u201c global_colors_info=\u201c{}\u201c custom_padding__hover=\u201c|||\u201c][et_pb_post_title author=\u201eoff\u201c date=\u201eoff\u201c categories=\u201eoff\u201c comments=\u201eoff\u201c _builder_version=\u201e4.27.4\u201c _module_preset=\u201edefault\u201c title_font=\u201c|800|||||||\u201c global_colors_info=\u201c{}\u201c][\/et_pb_post_title][et_pb_text _builder_version=\u201e4.27.4\u201c header_font=\u201c|700|||||||\u201c header_4_letter_spacing=\u201e12px\u201c module_alignment=\u201ecenter\u201c saved_tabs=\u201eall\u201c locked=\u201eoff\u201c global_colors_info=\u201c{}\u201c]<\/p>\n
The market for large language models (LLMs) remains in motion\u2014faster, denser, and more diverse than ever. With the Insiders LLM Bench\u00admar\u00adking for Q4 2025, we once again provide clarity in an envi\u00adron\u00adment where new models emerge every month and existing variants continue to be refined.<\/strong><\/p>\n [\/et_pb_text][et_pb_text _builder_version=\u201e4.27.4\u201c _module_preset=\u201edefault\u201c header_font=\u201c|700|||||||\u201c header_4_letter_spacing=\u201e12px\u201c module_alignment=\u201ecenter\u201c global_colors_info=\u201c{}\u201c]<\/p>\n For this edition, we nearly doubled the dataset and made the documents signi\u00adfi\u00adcantly more complex. This allows the bench\u00admar\u00adking to reflect real pro\u00adduc\u00adtive IDP workflows even more accu\u00adra\u00adtely \u2014 although the higher dif\u00adfi\u00adculty level slightly lowers the average scores.<\/p>\n <\/p>\n <\/p>\n The current bench\u00admar\u00adking covers 24 models, including new entrants such as Claude 4.5 Sonnet, Gemini 3 Pro, and GPT\u20115.1. Models whose suc\u00adces\u00adsors now offer com\u00adpa\u00adrable per\u00adfor\u00admance at similar cost were removed.<\/p>\n <\/p>\n Once again, dedicated reasoning models deliver strong results in clas\u00adsi\u00adfi\u00adca\u00adtion and extra\u00adc\u00adtion. At the same time, the same struc\u00adtural drawbacks seen in the previous benchmark reappear: longer pro\u00adces\u00adsing times, higher token costs, and less pre\u00addic\u00adtable operation in pro\u00adduc\u00adtion. For example, GPT\u20115 and GPT\u20114.1 achieve excellent overall per\u00adfor\u00admance scores of 87.3 and 84.7, respec\u00adtively \u2014 but come with notable dis\u00adad\u00advan\u00adtages when it comes to data pro\u00adtec\u00adtion or pro\u00adces\u00adsing speed.<\/p>\n <\/p>\n Compared to last quarter, the number of EU-hosted models in our selection has increased \u2014 though they remain scarce in the overall market.<\/p>\n <\/p>\n <\/p>\n Our own model again shows the strongest progress: OvAItion Private LLM improves by more than two per\u00adcen\u00adtage points despite the more demanding test data and, for the first time, approa\u00adches well-known models like Claude 4.5 Haiku. This is no coin\u00adci\u00addence \u2014 our current Private LLM is being merged with the announced OvAItion LLM to form the \u201cOvAItion Private LLM,\u201d combining maximum security with steadily improving quality and spe\u00adcia\u00adliza\u00adtion for the IDP envi\u00adron\u00adment of our customers and partners.<\/p>\n <\/p>\n The takeaway is clear: spe\u00adcia\u00adliza\u00adtion beats size. While large foun\u00adda\u00adtion models make only incre\u00admental advances, domain-specific models deliver the meaningful quality gains.<\/p>\n <\/p>\n <\/p>\n In regulated envi\u00adron\u00adments in par\u00adti\u00adcular, operating a self-hosted LLM is becoming incre\u00adasingly important. Orga\u00adniza\u00adtions benefit from full data control, C5-certified security, pre\u00addic\u00adtable costs, and maximum adap\u00adta\u00adbi\u00adlity. The trend is rein\u00adforced: high per\u00adfor\u00admance and regu\u00adla\u00adtory com\u00adpli\u00adance rarely coexist in global models \u2014 but are achie\u00advable in private deploy\u00adments.<\/p>\n <\/p>\n <\/p>\n Large foun\u00adda\u00adtion models operate at a high level, but progress slows noti\u00adce\u00adably in the IDP context<\/p>\n<\/li>\n Reasoning models achieve strong scores but are often inef\u00adfi\u00adcient in practice<\/p>\n<\/li>\n Under real IDP con\u00addi\u00adtions, benefits remain limited: overhead outweighs added quality<\/p>\n<\/li>\n High per\u00adfor\u00admance and regu\u00adla\u00adtory security seldom go hand in hand<\/p>\n<\/li>\n<\/ul>\n <\/p>\n <\/p>\n Insiders con\u00adsis\u00adt\u00adently pursues a best-of-breed approach: we con\u00adti\u00adnuously test all relevant models, integrate them through the OvAItion Engine, and enable customers to flexibly use exactly the models that best meet their requi\u00adre\u00adments. Com\u00adple\u00admen\u00adtary mecha\u00adnisms such as Green Voting auto\u00adma\u00adti\u00adcally safeguard result quality and reduce manual post-pro\u00adces\u00adsing.<\/p>\n <\/p>\n This keeps the Insiders LLM Bench\u00admar\u00adking a reliable point of ori\u00aden\u00adta\u00adtion in a market that evolves faster than any single provider can keep up with.<\/p>\n [\/et_pb_text][et_pb_button button_url=\u201ehttps:\/\/insiders.next-kmu.de\/wp-content\/uploads\/2025\/12\/PDF_Benchmarking_Dezember_Q4_2025_EN_4.pdf\u201c url_new_window=\u201eon\u201c button_text=\u201eRead LLM com\u00adpa\u00adrison\u201c button_alignment=\u201eleft\u201c _builder_version=\u201e4.27.4\u201c _module_preset=\u201e2cc0db31-42dd-4110\u20139790-25b7e462eb3b\u201c hover_enabled=\u201e0\u201c locked=\u201eoff\u201c global_colors_info=\u201c{%22gcid-e57f936a-e1ef-478a-a91c-6dc2f7bf0652%22:%91%22button_text_color__hover%22%93,%22gcid-a1ce49c7-18bb-4621\u20138275-487db4ef4ea2%22:%91%22button_text_color%22%93}\u201c button_text_color__hover_enabled=\u201eon|hover\u201c button_text_color__hover=\u201e#000000\u201c button_bg_color__hover_enabled=\u201eon|hover\u201c sticky_enabled=\u201e0\u201c][\/et_pb_button][et_pb_text disabled_on=\u201eoff|off|off\u201c _builder_version=\u201e4.27.4\u201c _module_preset=\u201edefault\u201c header_font=\u201c|700|||||||\u201c header_4_letter_spacing=\u201e12px\u201c module_alignment=\u201ecenter\u201c global_colors_info=\u201c{}\u201c]<\/p>\n For indi\u00advi\u00addual bench\u00admar\u00adkings, our AI experts are happy to advise you per\u00adso\u00adnally:<\/p>\n [\/et_pb_text][et_pb_button button_url=\u201emailto:llm-benchmarking@insiders-technologies.de\u201c url_new_window=\u201eon\u201c button_text=\u201eBenchmark my use case\u201c button_alignment=\u201eleft\u201c disabled_on=\u201eoff|off|off\u201c _builder_version=\u201e4.27.4\u201c _module_preset=\u201e2cc0db31-42dd-4110\u20139790-25b7e462eb3b\u201c hover_enabled=\u201e0\u201c locked=\u201eoff\u201c global_colors_info=\u201c{%22gcid-e57f936a-e1ef-478a-a91c-6dc2f7bf0652%22:%91%22button_text_color__hover%22%93,%22gcid-a1ce49c7-18bb-4621\u20138275-487db4ef4ea2%22:%91%22button_text_color%22%93}\u201c button_text_color__hover_enabled=\u201eon|hover\u201c button_text_color__hover=\u201e#000000\u201c button_bg_color__hover_enabled=\u201eon|hover\u201c sticky_enabled=\u201e0\u201c][\/et_pb_button][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":" The Insiders LLM bench\u00admar\u00adking in September 2025 continues the series and builds directly on the insights from Q2. To ensure com\u00adpa\u00adra\u00adbi\u00adlity, the same dimen\u00adsions and test data as in the previous bench\u00admar\u00adking are used.<\/p>\n","protected":false},"author":28,"featured_media":88166,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","wp_typography_post_enhancements_disabled":false,"_mbp_gutenberg_autopost":false,"footnotes":""},"categories":[677,2,605],"tags":[],"class_list":["post-88171","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-artificial-intelligence","category-blog-en","category-ovaition-en"],"acf":[],"_links":{"self":[{"href":"https:\/\/insiders.next-kmu.de\/en\/wp-json\/wp\/v2\/posts\/88171","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/insiders.next-kmu.de\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/insiders.next-kmu.de\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/insiders.next-kmu.de\/en\/wp-json\/wp\/v2\/users\/28"}],"replies":[{"embeddable":true,"href":"https:\/\/insiders.next-kmu.de\/en\/wp-json\/wp\/v2\/comments?post=88171"}],"version-history":[{"count":0,"href":"https:\/\/insiders.next-kmu.de\/en\/wp-json\/wp\/v2\/posts\/88171\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/insiders.next-kmu.de\/en\/wp-json\/wp\/v2\/media\/88166"}],"wp:attachment":[{"href":"https:\/\/insiders.next-kmu.de\/en\/wp-json\/wp\/v2\/media?parent=88171"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/insiders.next-kmu.de\/en\/wp-json\/wp\/v2\/categories?post=88171"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/insiders.next-kmu.de\/en\/wp-json\/wp\/v2\/tags?post=88171"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}A REALISTIC COM\u00adPA\u00adRISON UNDER TOUGHER CON\u00adDI\u00adTIONS<\/h3>\n
SPE\u00adCIA\u00adLIZA\u00adTION MAKES THE REAL DIF\u00adFE\u00adRENCE<\/h3>\n
DATA SOVE\u00adREIGNTY AS A STRATEGIC ADVANTAGE<\/h3>\n
KEY INSIGHTS FROM THE Q4 BENCHMARK<\/h3>\n
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BEST-OF-BREED AS A LONG-TERM STRATEGY<\/h3>\n