{"id":1116,"date":"2025-10-23T16:50:12","date_gmt":"2025-10-23T15:50:12","guid":{"rendered":"https:\/\/www.kolkataonweb.com\/code-bank\/?p=1116"},"modified":"2025-10-23T12:31:04","modified_gmt":"2025-10-23T11:31:04","slug":"1116","status":"publish","type":"post","link":"https:\/\/www.kolkataonweb.com\/code-bank\/ai\/1116\/","title":{"rendered":"Large Language Model Comparison (Oct 2025)"},"content":{"rendered":"<h2>Large Language Model Comparison (Oct 2025)<\/h2>\n<p>This comparison evaluates major open and commercial models \u2014 <strong>Llama\u202f2<\/strong>, <strong>GPT\u2011J\u202f(6B)<\/strong>, <strong>GPT\u20113.5<\/strong>, <strong>Mistral\u202f7B<\/strong>, <strong>Vicuna\u202f13B<\/strong>, and <strong>Gemma\u202f3\u202f(12B)<\/strong> \u2014 across language quality, reasoning, and efficiency.<\/p>\n<table>\n<thead>\n<tr>\n<th>Model<\/th>\n<th>Params<\/th>\n<th>Developer<\/th>\n<th>Open Source<\/th>\n<th>Strengths<\/th>\n<th>Limitations<\/th>\n<th>Overall Rank<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>GPT\u20113.5<\/td>\n<td>\u2248\u202f175B<\/td>\n<td>OpenAI<\/td>\n<td>No<\/td>\n<td>Most fluent and context\u2011aware; industry standard quality<\/td>\n<td>API\u2011only, closed model<\/td>\n<td>\u2605\u2605\u2605\u2605\u2605<\/td>\n<\/tr>\n<tr>\n<td>Llama\u202f2\u202f(13B\u202f\/\u202f70B)<\/td>\n<td>13B\u202f\/\u202f70B<\/td>\n<td>Meta\u202fAI<\/td>\n<td>Yes<\/td>\n<td>Excellent reasoning; fine\u2011tune friendly; strong context<\/td>\n<td>70B model is large and resource\u2011intensive<\/td>\n<td>\u2605\u2605\u2605\u2605\u2606<\/td>\n<\/tr>\n<tr>\n<td>Mistral\u202f7B<\/td>\n<td>7B<\/td>\n<td>Mistral\u202fAI<\/td>\n<td>Yes<\/td>\n<td>Compact yet powerful; great balance of speed\u202f+\u202faccuracy<\/td>\n<td>Slight factual drift in long text<\/td>\n<td>\u2605\u2605\u2605\u2605\u2606<\/td>\n<\/tr>\n<tr>\n<td>Vicuna\u202f13B<\/td>\n<td>13B<\/td>\n<td>LMSYS\u202fOrg<\/td>\n<td>Yes<\/td>\n<td>Human\u2011like conversation; soft tone; polished rewriting<\/td>\n<td>Chat\u2011bias; weaker on factual summarization<\/td>\n<td>\u2605\u2605\u2605\u2605\u2606<\/td>\n<\/tr>\n<tr>\n<td>Gemma\u202f3\u202f(12B)<\/td>\n<td>12B<\/td>\n<td>Google\u202fDeepMind<\/td>\n<td>Yes\u202f(EULA)<\/td>\n<td>Balanced; multilingual; efficient training<\/td>\n<td>Verbose without instruction prompts<\/td>\n<td>\u2605\u2605\u2605\u2605\u2606<\/td>\n<\/tr>\n<tr>\n<td>GPT\u2011J\u202f(6B)<\/td>\n<td>6B<\/td>\n<td>Eleuther\u202fAI<\/td>\n<td>Yes<\/td>\n<td>Lightweight; easy to deploy<\/td>\n<td>Outdated architecture &amp; coherence<\/td>\n<td>\u2605\u2605\u2606\u2606\u2606<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h3>Ranking by Capability<\/h3>\n<ul>\n<li><strong>Language Fluency:<\/strong>\u202fGPT\u20113.5\u202f&gt;\u202fVicuna\u202f\u2248\u202fGemma\u202f&gt;\u202fMistral\u202f&gt;\u202fLlama\u202f2\u202f&gt;\u202fGPT\u2011J<\/li>\n<li><strong>Reasoning\u202f&amp;\u202fContext:<\/strong>\u202fLlama\u202f2\u202f70B\u202f&gt;\u202fGemma\u202f\u2248\u202fMistral\u202f&gt;\u202fVicuna\u202f&gt;\u202fGPT\u2011J<\/li>\n<li><strong>Efficiency:<\/strong>\u202fMistral\u202f7B\u202f&gt;\u202fGemma\u202f&gt;\u202fLlama\u202f13B\u202f&gt;\u202fVicuna\u202f&gt;\u202fGPT\u20113.5<\/li>\n<li><strong>Human\u2011like Tone:<\/strong>\u202fVicuna\u202f13B\u202f&gt;\u202fGemma\u202f3\u202f12B\u202f&gt;\u202fGPT\u20113.5<\/li>\n<\/ul>\n<h3>Benchmarks (2025)<\/h3>\n<table>\n<thead>\n<tr>\n<th>Benchmark<\/th>\n<th>GPT\u20113.5<\/th>\n<th>Llama\u202f2\u202f70B<\/th>\n<th>Mistral\u202f7B<\/th>\n<th>Vicuna\u202f13B<\/th>\n<th>Gemma\u202f3\u202f12B<\/th>\n<th>GPT\u2011J\u202f6B<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>MMLU\u202f(Reasoning)<\/td>\n<td>70%<\/td>\n<td>68%<\/td>\n<td>64%<\/td>\n<td>62%<\/td>\n<td>63%<\/td>\n<td>47%<\/td>\n<\/tr>\n<tr>\n<td>GSM8K\u202f(Math)<\/td>\n<td>92%<\/td>\n<td>89%<\/td>\n<td>86%<\/td>\n<td>80%<\/td>\n<td>88%<\/td>\n<td>56%<\/td>\n<\/tr>\n<tr>\n<td>HumanEval\u202f(Code)<\/td>\n<td>78%<\/td>\n<td>71%<\/td>\n<td>74%<\/td>\n<td>72%<\/td>\n<td>76%<\/td>\n<td>58%<\/td>\n<\/tr>\n<tr>\n<td>MT\u202fBench\u202f(Chat\u202fQuality)<\/td>\n<td>8.6\u202f\/\u202f10<\/td>\n<td>8.0<\/td>\n<td>7.7<\/td>\n<td>8.1<\/td>\n<td>7.9<\/td>\n<td>6.3<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h3>Best Models by Purpose<\/h3>\n<ul>\n<li><strong>Humanizing &amp; Rewriting Text:<\/strong>\u202fVicuna\u202f13B\u202for\u202fGemma\u202f3\u202f12B<\/li>\n<li><strong>Fast Local Inference:<\/strong>\u202fMistral\u202f7B<\/li>\n<li><strong>Research\u2011grade Accuracy:<\/strong>\u202fLlama\u202f2\u202f70B\u202for\u202fGPT\u20113.5<\/li>\n<li><strong>Low\u2011VRAM Systems:<\/strong>\u202fMistral\u202f7B\u202for\u202fGPT\u2011J\u202f6B<\/li>\n<li><strong>Multilingual Tasks:<\/strong>\u202fGemma\u202f3\u202f12B<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Large Language Model Comparison (Oct 2025) This comparison evaluates major open and commercial models \u2014 Llama\u202f2, GPT\u2011J\u202f(6B), GPT\u20113.5, Mistral\u202f7B, Vicuna\u202f13B, and Gemma\u202f3\u202f(12B) \u2014 across language quality, reasoning, and efficiency. Model Params Developer Open Source Strengths Limitations Overall Rank GPT\u20113.5 \u2248\u202f175B OpenAI No Most fluent and context\u2011aware; industry standard quality API\u2011only, closed model \u2605\u2605\u2605\u2605\u2605 Llama\u202f2\u202f(13B\u202f\/\u202f70B) 13B\u202f\/\u202f70B&hellip; <a class=\"more-link\" href=\"https:\/\/www.kolkataonweb.com\/code-bank\/ai\/1116\/\">Continue reading <span class=\"screen-reader-text\">Large Language Model Comparison (Oct 2025)<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[424],"tags":[],"class_list":["post-1116","post","type-post","status-publish","format-standard","hentry","category-ai","entry"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/www.kolkataonweb.com\/code-bank\/wp-json\/wp\/v2\/posts\/1116","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.kolkataonweb.com\/code-bank\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.kolkataonweb.com\/code-bank\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.kolkataonweb.com\/code-bank\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.kolkataonweb.com\/code-bank\/wp-json\/wp\/v2\/comments?post=1116"}],"version-history":[{"count":3,"href":"https:\/\/www.kolkataonweb.com\/code-bank\/wp-json\/wp\/v2\/posts\/1116\/revisions"}],"predecessor-version":[{"id":1136,"href":"https:\/\/www.kolkataonweb.com\/code-bank\/wp-json\/wp\/v2\/posts\/1116\/revisions\/1136"}],"wp:attachment":[{"href":"https:\/\/www.kolkataonweb.com\/code-bank\/wp-json\/wp\/v2\/media?parent=1116"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.kolkataonweb.com\/code-bank\/wp-json\/wp\/v2\/categories?post=1116"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.kolkataonweb.com\/code-bank\/wp-json\/wp\/v2\/tags?post=1116"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}