{"id":5143,"date":"2026-05-07T22:12:13","date_gmt":"2026-05-07T22:12:13","guid":{"rendered":"https:\/\/suprmind.ai\/hub\/claude\/vs-other-ai\/"},"modified":"2026-10-06T17:47:19","modified_gmt":"2026-10-06T17:47:19","slug":"vs-other-ai","status":"publish","type":"page","link":"https:\/\/suprmind.ai\/hub\/claude\/vs-other-ai\/","title":{"rendered":"Claude vs ChatGPT vs Gemini vs Grok vs Perplexity: 2026 Comparison"},"content":{"rendered":"<div style=\"padding-top: 40px;\">\n<section class=\"hero\">\n<div class=\"hero-content\">\n<div class=\"hero-label\">Claude vs Other AI Models<\/div>\n<h1>Claude vs ChatGPT vs Gemini vs Grok vs Perplexity: 2026 Honest Comparison<\/h1>\n<p class=\"hero-subtitle\" style=\"padding-top: 30px;\">Comparison content for AI models is a swamp. Vendor pages cherry-pick benchmarks. Aggregators copy each other. Headline numbers cite specialized configurations against general-purpose rivals. This page does the work in the open. Every claim cites the benchmark that produced it. Where benchmarks measure different things, we say so. Where Claude wins, we show the win. Where <a href=\"https:\/\/suprmind.ai\/hub\/claude\/pricing\/\" title=\"Anthropic Claude Pricing 2026: Free, Pro, Max, Team, Enterprise, API\"  data-wpil-monitor-id=\"2527\">Claude<\/a> loses, we show the loss. Two findings frame everything below.<\/p>\n<\/p><\/div>\n<\/section>\n<p>    <!-- suprmind-demo-injection --><\/p>\n<h2 style=\"text-align:center; max-width:800px; margin:0 auto 24px;\">See how Claude Works With other Four Frontier AI Models in Multi-AI Orchestrated Business Discussion<\/h2>\n<p>    <style>@media (max-width: 768px){#suprmind-demo{margin-left:calc(-1 * var(--suprmind-demo-mobile-bleed, 8px))!important;margin-right:calc(-1 * var(--suprmind-demo-mobile-bleed, 8px))!important;width:calc(100% + var(--suprmind-demo-mobile-bleed, 8px) * 2)!important;}}<\/style><div id=\"suprmind-demo\" style=\"width:100%; overflow:hidden;\"><\/div><br \/>\n    <!-- \/suprmind-demo-injection --><\/p>\n<section style=\"padding: 100px 48px;\">\n<div style=\"max-width: 900px; margin: 0 auto;\">\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\">First, Claude&#8217;s top model held up best in its line-up. Per the Suprmind <a href=\"https:\/\/suprmind.ai\/hub\/multi-model-ai-divergence-index\/#model-by-model\">Multi-Model AI Divergence Index<\/a> (Q3 2026 Edition), in our top line-up, a blind judge found Claude Opus 5 corrected by another model on 8.7% of its answers. Gemini 3.1 Pro: 21.6%. Grok 4.3: 21.0%. Same 310 questions.<\/p>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\">Second, Claude Opus 4.7 holds an AA-Omniscience hallucination rate of 36% versus GPT-5.5&#8217;s 86% on the same benchmark. The 50-percentage-point gap is the single most consequential benchmark difference for high-stakes use. Claude achieves it by declining to answer more often, not by being smarter at every question &#8211; and the cost is approximately 8 points of raw accuracy on the same benchmark (47% vs Gemini 3.1 Pro&#8217;s 55.3%).<\/p>\n<p style=\"font-size: 14px; color: #9ca3af; margin: 0 0 32px;\">See also: <a href=\"https:\/\/suprmind.ai\/hub\/multi-model-ai-divergence-index\/\" style=\"color: #8b5cf6;\">Suprmind Multi-Model AI Divergence Index \u2192<\/a><\/p>\n<\/p><\/div>\n<\/section>\n<section style=\"padding: 100px 48px;\">\n<div style=\"max-width: 900px; margin: 0 auto;\">\n<h2>Quick Verdict: Where Each Model Wins<\/h2>\n<\/p><\/div>\n<div class=\"comparison-table comparison-table-3\" style=\"max-width: 1100px; margin: 0 auto 32px;\">\n<div class=\"comparison-row\">\n<div class=\"comparison-feature\">Model<\/div>\n<div class=\"comparison-value\">Best at<\/div>\n<div class=\"comparison-value\">Worst at<\/div>\n<\/p><\/div>\n<div class=\"comparison-row\">\n<div class=\"comparison-feature\">**Claude Opus 4.7**<\/div>\n<div class=\"comparison-value\">Multi-file coding (SWE-bench Pro 64.3%); calibration; tool orchestration (MCP-Atlas 77.3%); high-stakes refusal<\/div>\n<div class=\"comparison-value\">Image\/audio\/video generation (none); knowledge breadth; multimodal input ingest<\/div>\n<\/p><\/div>\n<div class=\"comparison-row\">\n<div class=\"comparison-feature\">**GPT-5.5**<\/div>\n<div class=\"comparison-value\">Image generation; voice; plugin breadth; speed on simple queries<\/div>\n<div class=\"comparison-value\">Hallucination calibration (86% AA-Omniscience); SWE-bench Pro<\/div>\n<\/p><\/div>\n<div class=\"comparison-row\">\n<div class=\"comparison-feature\">**Gemini 3.1 Pro**<\/div>\n<div class=\"comparison-value\">Multimodal input (audio + video native); knowledge breadth (55.3% AA-Omni accuracy); BrowseComp; ARC-AGI-2<\/div>\n<div class=\"comparison-value\">Multi-file coding<\/div>\n<\/p><\/div>\n<div class=\"comparison-row\">\n<div class=\"comparison-feature\">**Grok 4.3 (Heavy)**<\/div>\n<div class=\"comparison-value\">Real-time X stream integration, long context (2M)<\/div>\n<div class=\"comparison-value\">Citation accuracy (94% CJR hallucination on Grok-3); calibration<\/div>\n<\/p><\/div>\n<div class=\"comparison-row\">\n<div class=\"comparison-feature\">**Perplexity Sonar Pro**<\/div>\n<div class=\"comparison-value\">Citation grounding (37% CJR best), retrieval freshness (24-48h lag)<\/div>\n<div class=\"comparison-value\">Pure reasoning depth without retrieval; agentic tool use<\/div>\n<\/p><\/div>\n<div class=\"comparison-row\">\n<div class=\"comparison-feature\">**DeepSeek V3.2**<\/div>\n<div class=\"comparison-value\">Cost ($0.28\/$0.42 per million tokens); on-prem deployment (some variants open-weights)<\/div>\n<div class=\"comparison-value\">Agentic tooling maturity; safety architecture; enterprise compliance<\/div>\n<\/p><\/div>\n<\/p><\/div>\n<div style=\"max-width: 900px; margin: 0 auto;\">\n        <\/div>\n<\/section>\n<section style=\"padding: 100px 48px;\">\n<div style=\"max-width: 900px; margin: 0 auto;\">\n<h2>Benchmark Comparison<\/h2>\n<\/p><\/div>\n<div class=\"comparison-table comparison-table-6\" style=\"max-width: 1100px; margin: 0 auto 32px;\">\n<div class=\"comparison-row\">\n<div class=\"comparison-feature\">Benchmark<\/div>\n<div class=\"comparison-value\">Claude Opus 4.7<\/div>\n<div class=\"comparison-value\">GPT-5.5 \/ 5.4<\/div>\n<div class=\"comparison-value\">Gemini 3.1 Pro<\/div>\n<div class=\"comparison-value\">Grok 4 \/ 4.3<\/div>\n<div class=\"comparison-value\">DeepSeek V3.2<\/div>\n<\/p><\/div>\n<div class=\"comparison-row\">\n<div class=\"comparison-feature\">GPQA Diamond<\/div>\n<div class=\"comparison-value\">94.2%<\/div>\n<div class=\"comparison-value\">GPT-5.4: 94.4%<\/div>\n<div class=\"comparison-value\">94.3%<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<\/p><\/div>\n<div class=\"comparison-row\">\n<div class=\"comparison-feature\">SWE-bench Verified<\/div>\n<div class=\"comparison-value\">87.6%<\/div>\n<div class=\"comparison-value\">not publicly confirmed<\/div>\n<div class=\"comparison-value\">80.6%<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<\/p><\/div>\n<div class=\"comparison-row\">\n<div class=\"comparison-feature\">SWE-bench Pro<\/div>\n<div class=\"comparison-value\">64.3% (industry high)<\/div>\n<div class=\"comparison-value\">GPT-5.4: 57.7%<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<\/p><\/div>\n<div class=\"comparison-row\">\n<div class=\"comparison-feature\">AA Intelligence Index<\/div>\n<div class=\"comparison-value\">57 (3-way tie)<\/div>\n<div class=\"comparison-value\">GPT-5.4: 57<\/div>\n<div class=\"comparison-value\">57<\/div>\n<div class=\"comparison-value\">DeepSeek V3.2: 51.5<\/div>\n<div class=\"comparison-value\">\u2014<\/div>\n<\/p><\/div>\n<div class=\"comparison-row\">\n<div class=\"comparison-feature\">LMArena Elo (Text)<\/div>\n<div class=\"comparison-value\">1504<\/div>\n<div class=\"comparison-value\">~1482<\/div>\n<div class=\"comparison-value\">~1493<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<\/p><\/div>\n<div class=\"comparison-row\">\n<div class=\"comparison-feature\">OSWorld (Computer Use)<\/div>\n<div class=\"comparison-value\">78%<\/div>\n<div class=\"comparison-value\">GPT-5.5: 78.7%<\/div>\n<div class=\"comparison-value\">not published<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<\/p><\/div>\n<div class=\"comparison-row\">\n<div class=\"comparison-feature\">MCP-Atlas<\/div>\n<div class=\"comparison-value\">77.3%<\/div>\n<div class=\"comparison-value\">GPT-5.4: 68.1%<\/div>\n<div class=\"comparison-value\">73.9%<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<\/p><\/div>\n<div class=\"comparison-row\">\n<div class=\"comparison-feature\">HLE (with tools)<\/div>\n<div class=\"comparison-value\">54.7% (1st)<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<div class=\"comparison-value\">51.4%<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<\/p><\/div>\n<div class=\"comparison-row\">\n<div class=\"comparison-feature\">BrowseComp<\/div>\n<div class=\"comparison-value\">79.3%<\/div>\n<div class=\"comparison-value\">not publicly disclosed<\/div>\n<div class=\"comparison-value\">85.9%<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<\/p><\/div>\n<div class=\"comparison-row\">\n<div class=\"comparison-feature\">ARC-AGI-2<\/div>\n<div class=\"comparison-value\">Opus 4.6: 68.8%<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<div class=\"comparison-value\">77.1%<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<\/p><\/div>\n<div class=\"comparison-row\">\n<div class=\"comparison-feature\">AA-Omniscience Hallucination<\/div>\n<div class=\"comparison-value\">36%<\/div>\n<div class=\"comparison-value\">GPT-5.5: 86%<\/div>\n<div class=\"comparison-value\">50%<\/div>\n<div class=\"comparison-value\">Grok 4: 64%<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<\/p><\/div>\n<div class=\"comparison-row\">\n<div class=\"comparison-feature\">AA-Omniscience Index<\/div>\n<div class=\"comparison-value\">26 (2nd overall)<\/div>\n<div class=\"comparison-value\">GPT-5.5: 20<\/div>\n<div class=\"comparison-value\">33<\/div>\n<div class=\"comparison-value\">Grok 4: 64<\/div>\n<div class=\"comparison-value\">\u2014<\/div>\n<\/p><\/div>\n<div class=\"comparison-row\">\n<div class=\"comparison-feature\">HalluHard (Opus 4.5 with web)<\/div>\n<div class=\"comparison-value\">30% (lowest)<\/div>\n<div class=\"comparison-value\">not in same cycle<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<\/p><\/div>\n<div class=\"comparison-row\">\n<div class=\"comparison-feature\">FACTS (Opus 4.5)<\/div>\n<div class=\"comparison-value\">51.3<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<div class=\"comparison-value\">68.8<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<\/p><\/div>\n<\/p><\/div>\n<div style=\"max-width: 900px; margin: 0 auto;\">\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\">Sources: Vellum AI, 2026-04-15; Suprmind Hallucination Rates, 2026-04-26; pricepertoken.com; DataCamp, 2026-04-26; ofox.ai; AA Index. Last verified 2026-05-07.<\/p>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\">A note on saturation: GPQA Diamond has compressed at the frontier &#8211; all three top labs&#8217; flagships score within 0.2 percentage points of each other (94.2-94.4%). Competitive differentiation has structurally shifted to applied task benchmarks (SWE-bench Pro, CursorBench, MCP-Atlas) and hallucination profiling.<\/p>\n<\/p><\/div>\n<\/section>\n<section style=\"padding: 100px 48px;\">\n<div style=\"max-width: 900px; margin: 0 auto;\">\n<h2>Hallucination Rates Compared<\/h2>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\">Per Suprmind&#8217;s AI Hallucination Rates and Benchmarks reference (May 2026 update), the AA-Omniscience hallucination cohort spread is:<\/p>\n<\/p><\/div>\n<div class=\"comparison-table comparison-table-4-numeric\" style=\"max-width: 1100px; margin: 0 auto 32px;\">\n<div class=\"comparison-row\">\n<div class=\"comparison-feature\">Model<\/div>\n<div class=\"comparison-value\">AA-Omniscience Hallucination<\/div>\n<div class=\"comparison-value\">AA-Omniscience Accuracy<\/div>\n<div class=\"comparison-value\">Index<\/div>\n<\/p><\/div>\n<div class=\"comparison-row\">\n<div class=\"comparison-feature\">Claude 4.1 Opus (early run)<\/div>\n<div class=\"comparison-value\">0%<\/div>\n<div class=\"comparison-value\">36% (early run)<\/div>\n<div class=\"comparison-value\">4.8<\/div>\n<\/p><\/div>\n<div class=\"comparison-row\">\n<div class=\"comparison-feature\">Claude Opus 4.7<\/div>\n<div class=\"comparison-value\">36%<\/div>\n<div class=\"comparison-value\">~47%<\/div>\n<div class=\"comparison-value\">26<\/div>\n<\/p><\/div>\n<div class=\"comparison-row\">\n<div class=\"comparison-feature\">Claude Opus 4.6<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<div class=\"comparison-value\">46.4%<\/div>\n<div class=\"comparison-value\">14<\/div>\n<\/p><\/div>\n<div class=\"comparison-row\">\n<div class=\"comparison-feature\">Claude Opus 4.5<\/div>\n<div class=\"comparison-value\">58%<\/div>\n<div class=\"comparison-value\">45.7%<\/div>\n<div class=\"comparison-value\">Negative<\/div>\n<\/p><\/div>\n<div class=\"comparison-row\">\n<div class=\"comparison-feature\">Claude Sonnet 4.6<\/div>\n<div class=\"comparison-value\">~38%<\/div>\n<div class=\"comparison-value\">40.0%<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<\/p><\/div>\n<div class=\"comparison-row\">\n<div class=\"comparison-feature\">Claude Haiku 4.5<\/div>\n<div class=\"comparison-value\">25%<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<\/p><\/div>\n<div class=\"comparison-row\">\n<div class=\"comparison-feature\">GPT-5.5<\/div>\n<div class=\"comparison-value\">86%<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<div class=\"comparison-value\">20<\/div>\n<\/p><\/div>\n<div class=\"comparison-row\">\n<div class=\"comparison-feature\">GPT-5.2<\/div>\n<div class=\"comparison-value\">~78%<\/div>\n<div class=\"comparison-value\">43.8%<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<\/p><\/div>\n<div class=\"comparison-row\">\n<div class=\"comparison-feature\">Gemini 3.1 Pro<\/div>\n<div class=\"comparison-value\">50%<\/div>\n<div class=\"comparison-value\">55.3%<\/div>\n<div class=\"comparison-value\">33<\/div>\n<\/p><\/div>\n<div class=\"comparison-row\">\n<div class=\"comparison-feature\">Grok 4<\/div>\n<div class=\"comparison-value\">64%<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<div class=\"comparison-value\">not reported<\/div>\n<\/p><\/div>\n<\/p><\/div>\n<div style=\"max-width: 900px; margin: 0 auto;\">\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\">Source: Suprmind AI Hallucination Rates and Benchmarks, 2026-04-26.<\/p>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\">Three patterns matter. First, Claude&#8217;s calibration-by-refusal architecture produces both the lowest hallucination rates across the cohort and lower raw accuracy than Gemini 3.1 Pro &#8211; Claude answers fewer questions in total but more correctly as a proportion of attempts. Second, GPT-5.5&#8217;s 86% hallucination is the highest in the cohort despite leading the AA Intelligence Index alongside Claude and Gemini. Third, Claude Opus 4.5 with web search posts 30% on HalluHard (the lowest of any model on the realistic-conversation benchmark); without web search, that rises to 60%. The 30-point delta confirms the practical rule: for knowledge-sensitive professional work, always enable web search.<\/p>\n<p style=\"font-size: 14px; color: #9ca3af; margin: 0 0 32px;\">See also: <a href=\"https:\/\/suprmind.ai\/hub\/ai-hallucination-rates-and-benchmarks\/\" style=\"color: #8b5cf6;\">Claude hallucination rates across benchmarks \u2192<\/a><\/p>\n<\/p><\/div>\n<\/section>\n<section style=\"padding: 100px 48px;\">\n<div style=\"max-width: 900px; margin: 0 auto;\">\n<h2>Where Claude Wins<\/h2>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\"><strong>Correcting earlier answers in a chain.<\/strong> In the Suprmind <a href=\"https:\/\/suprmind.ai\/hub\/multi-model-ai-divergence-index\/#last-word\">Multi-Model AI Divergence Index<\/a> (Q3 2026 Edition), we sent 60 real questions through our top AI line-up in every speaking order. Claude Opus 5.5 corrected 24 of every 100 earlier answers it followed: about twice GPT-5.4 (12), three times Gemini 3.1 Pro (8), seven times Grok 4.3 (3).<\/p>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\"><strong>Refusal-over-fabrication on knowledge limits.<\/strong> Claude 4.1 Opus achieved 0% AA-Omniscience hallucination by refusing uncertain queries &#8211; the lowest of any model tested. Claude Opus 4.7 carries this forward with a 36% hallucination rate and an Omniscience Index of 26, second-highest overall and 50 percentage points better than GPT-5.5 on the same benchmark.<\/p>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\"><strong>Realistic-conversation hallucination (HalluHard).<\/strong> Claude Opus 4.5 with web search scored 30% on HalluHard, the lowest of any model. HalluHard tests hallucination in conditions that resemble actual professional use, not curated single-fact queries.<\/p>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\"><strong>Complex multi-file coding (SWE-bench Pro).<\/strong> Claude Opus 4.7&#8217;s 64.3% on SWE-bench Pro is the current industry high &#8211; 6.6 percentage points ahead of GPT-5.4 (57.7%) and 10.9 points above Opus 4.6 (53.4%). SWE-bench Pro is the benchmark most clearly correlated with real-world coding agent performance on hard, multi-repository tasks.<\/p>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\"><strong>Tool orchestration (MCP-Atlas).<\/strong> Claude Opus 4.7 scores 77.3% on MCP-Atlas, leading Gemini 3.1 Pro (73.9%) by 3.4 points and GPT-5.4 (68.1%) by 9.2 points.<\/p>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\"><strong>Who the judge sides with.<\/strong> Per the Suprmind <a href=\"https:\/\/suprmind.ai\/hub\/multi-model-ai-divergence-index\/#pecking-order\">Multi-Model AI Divergence Index<\/a> (Q3 2026 Edition), in three of our four main line-ups, the judge sided most often with the Claude model when it found one answer correcting another. In the fourth, it sided most often with GPT-5.6 Luna.<\/p>\n<p style=\"font-size: 14px; color: #9ca3af; margin: 0 0 32px;\">See also: <a href=\"https:\/\/suprmind.ai\/hub\/multi-model-ai-divergence-index\/#corrected-least\" style=\"color: #8b5cf6;\">Which AI models get corrected least \u2192<\/a><\/p>\n<\/p><\/div>\n<\/section>\n<section style=\"padding: 100px 48px;\">\n<div style=\"max-width: 900px; margin: 0 auto;\">\n<h2>Where Claude Loses<\/h2>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\"><strong>Knowledge breadth.<\/strong> <a href=\"https:\/\/suprmind.ai\/hub\/claude\/free-trial\/\" title=\"Claude Free Trial\"  data-wpil-monitor-id=\"2844\">Claude<\/a> Opus 4.7&#8217;s AA-Omniscience accuracy of approximately 47% trails Gemini 3.1 Pro&#8217;s 55.3% by 8 points. Claude answers fewer questions correctly in total because the architecture prefers refusal over fabrication. Users who need maximum coverage over maximum precision should pair Claude with a higher-coverage model.<\/p>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\"><strong>Multimodal coverage.<\/strong> Claude accepts only text and image. Gemini 3 Pro accepts text, image, audio, and video natively. Claude&#8217;s FACTS multi-dimensional factuality score (Opus 4.5: 51.3) trails Gemini 3 Pro (68.8) by 17 points &#8211; and the gap is partly structural because FACTS measures inputs Claude cannot read. In text-grounded sub-domains where Claude competes on equal architecture (Law, Software Engineering, Humanities), Claude 4.1 Opus leads or matches Gemini.<\/p>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\"><strong>Image, audio, and video generation.<\/strong> Claude has none. ChatGPT has all three (image, voice, video via Sora until April 2026 when it was discontinued). Gemini has all three.<\/p>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\"><strong>ARC-AGI-2.<\/strong> Gemini 3.1 Pro leads at 77.1% versus Claude Opus 4.6&#8217;s 68.8%.<\/p>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\"><strong>BrowseComp.<\/strong> Gemini 3.1 Pro at 85.9% leads Claude Opus 4.7 at 79.3%.<\/p>\n<p style=\"font-size: 14px; color: #9ca3af; margin: 0 0 32px;\">See also: <a href=\"https:\/\/suprmind.ai\/hub\/ai-hallucination-rates-and-benchmarks\/\" style=\"color: #8b5cf6;\">Suprmind&#8217;s AI Hallucination Rates and Benchmarks reference \u2192<\/a><\/p>\n<\/p><\/div>\n<\/section>\n<section style=\"padding: 100px 48px;\">\n<div style=\"max-width: 900px; margin: 0 auto;\">\n<h2>Claude vs ChatGPT<\/h2>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\">Claude leads on autonomous multi-file coding (SWE-bench Pro 64.3% vs GPT-5.4&#8217;s 57.7%), hallucination calibration (AA-Omniscience 36% vs GPT-5.5&#8217;s 86%), and tool orchestration (MCP-Atlas 77.3% vs GPT-5.4&#8217;s 68.1%). ChatGPT leads on image generation (Claude has none), plugin ecosystem breadth, voice mode, broader integration surface (Apple Intelligence, Microsoft Copilot, GitHub Copilot, VS Code), and raw speed on simple queries.<\/p>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\">In the Suprmind <a href=\"https:\/\/suprmind.ai\/hub\/multi-model-ai-divergence-index\/#corrected-least\">Multi-Model AI Divergence Index<\/a> (Q3 2026 Edition), GPT and Claude sit in the same group: 3.4% and 4.3% of answers judged corrected by another model when at least two of three AI judges had to agree. No judge went easy on its own maker&#8217;s models. Anthropic&#8217;s judge was harder on Claude than the OpenAI and Google judges were.<\/p>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\">Pricing comparison: Claude Opus 4.7 is $5\/$25 per million input\/output tokens. GPT-5.5 is reported at approximately $5\/~$30 (GPT-5.5 was a 2x pricing bump from GPT-5.4). For multi-million-token coding workloads, Claude is currently competitive on both performance and cost. For high-volume routine workloads, GPT-4o mini at $0.15 per million input is the cheapest path; Claude Haiku 4.5 at $1\/$5 is the closest comparator.<\/p>\n<p style=\"font-size: 14px; color: #9ca3af; margin: 0 0 32px;\">See also: <a href=\"https:\/\/suprmind.ai\/hub\/chatgpt\/\" style=\"color: #8b5cf6;\">ChatGPT 2026 overview \u2192<\/a><\/p>\n<\/p><\/div>\n<\/section>\n<section style=\"padding: 100px 48px;\">\n<div style=\"max-width: 900px; margin: 0 auto;\">\n<h2>Claude vs Gemini<\/h2>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\">On coding, agentic tooling, and hallucination calibration, Claude leads: SWE-bench Verified 87.6% vs Gemini 3.1 Pro&#8217;s 80.6%, AA-Omniscience hallucination 36% vs 50%, MCP-Atlas 77.3% vs 73.9%.<\/p>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\">Gemini leads on price (Gemini 3.1 Pro is approximately $2.50\/$15 per million tokens vs Claude Opus 4.7&#8217;s $5\/$25 &#8211; 50% cheaper input, 40% cheaper output), knowledge breadth (AA-Omniscience accuracy 55.3% vs 47%), multimodal inputs (audio and video native; Claude has neither), ARC-AGI-2 (77.1% vs 68.8%), BrowseComp (85.9% vs 79.3%), and AA-Omniscience Index (33 vs 26).<\/p>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\">In the Suprmind <a href=\"https:\/\/suprmind.ai\/hub\/multi-model-ai-divergence-index\/#corrected-least\">Multi-Model AI Divergence Index<\/a> (Q3 2026 Edition), the two land in different groups. When at least two of three AI judges had to agree, Claude answers were judged corrected by another model on 4.3% of answers and Gemini answers on 6.5%. Tested pair by pair, every gap between the two groups is larger than chance.<\/p>\n<\/p><\/div>\n<\/section>\n<section style=\"padding: 100px 48px;\">\n<div style=\"max-width: 900px; margin: 0 auto;\">\n<h2>Claude vs Grok<\/h2>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\">Claude leads on calibration and hallucination rate. Claude Opus 4.7 holds AA-Omniscience hallucination at 36% versus Grok 4&#8217;s 64%, a 28 percentage-point gap. Per the Suprmind <a href=\"https:\/\/suprmind.ai\/hub\/multi-model-ai-divergence-index\/#model-by-model\">Multi-Model AI Divergence Index<\/a> (Q3 2026 Edition), in our mid-tier line-up, Grok 4.3 was judged corrected on 14.8% of its answers. Claude Sonnet 5: 7.8%. GPT-5.2: 9.0%. Same 778 questions.<\/p>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\">Grok leads on real-time X integration (no other <a href=\"https:\/\/suprmind.ai\/hub\/ai-models-knowledge-hub\/\">frontier model<\/a> has direct access to the X content stream) and speed on simple queries.<\/p>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\">Pricing: Grok API is approximately $1.25\/$2.50 per million tokens for the standard model &#8211; significantly cheaper than Claude Opus. For real-time event-recall workflows, Grok plus a calibration model (Claude or Perplexity) is the documented orchestration pattern; Grok alone has the highest documented citation hallucination <a href=\"https:\/\/suprmind.ai\/hub\/ai-models-index\/\" title=\"AI Models Index\" data-wpil-monitor-id=\"3378\">rate of any model<\/a> tested (Grok-3: 94% on the Columbia Journalism Review citation accuracy test).<\/p>\n<p style=\"font-size: 14px; color: #9ca3af; margin: 0 0 32px;\">See also: <a href=\"https:\/\/suprmind.ai\/hub\/grok\/\" style=\"color: #8b5cf6;\">Grok complete guide \u2192<\/a><\/p>\n<\/p><\/div>\n<\/section>\n<section style=\"padding: 100px 48px;\">\n<div style=\"max-width: 900px; margin: 0 auto;\">\n<h2>Claude vs Perplexity<\/h2>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\">In the Suprmind <a href=\"https:\/\/suprmind.ai\/hub\/multi-model-ai-divergence-index\/#corrected-least\">Multi-Model AI Divergence Index<\/a> (Q3 2026 Edition), Claude and Perplexity both sit with GPT in the group corrected least: 4.3% and 3.7% of answers judged corrected by another model when at least two of three AI judges had to agree. Tested pair by pair, no gap inside that group is larger than chance.<\/p>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\">Where they differ structurally: Perplexity Sonar Pro is a search-integrated model purpose-built for citation grounding &#8211; it scored 37% on the Columbia Journalism Review citation accuracy test, the lowest (best) of any model. Claude is a parametric reasoning model with optional web search; without web search enabled, Claude&#8217;s CJR-equivalent performance is meaningfully worse. With Claude Opus 4.5 and web search, HalluHard hits 30%; without web search, 60%.<\/p>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\">The orchestration recommendation: pair Claude&#8217;s reasoning-and-calibration with Perplexity&#8217;s citation-and-retrieval for high-stakes factual research where both deep analysis and verifiable sources matter. Claude alone produces strong analysis but cannot guarantee citation accuracy without web search; Perplexity alone produces strong citations but trails on reasoning depth.<\/p>\n<\/p><\/div>\n<\/section>\n<section style=\"padding: 100px 48px;\">\n<div style=\"max-width: 900px; margin: 0 auto;\">\n<h2>Claude vs DeepSeek<\/h2>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\">The primary difference is cost. DeepSeek V3.2 costs $0.28\/$0.42 per million tokens versus <a href=\"\/hub\/claude\/pricing\/claude-max-pricing\/\" title=\"Claude Max Pricing\"  data-wpil-monitor-id=\"2806\">Claude<\/a> Opus 4.7&#8217;s $5\/$25 &#8211; a 17-59x price difference. DeepSeek V3.2 scores 88.5 on MMLU and 51.5 on AA Intelligence Index, competitive with general-purpose models but trailing the frontier.<\/p>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\">Claude&#8217;s advantages: safety architecture (Constitutional AI), agentic tooling maturity (Claude Code, Computer Use, MCP), calibration behavior, and enterprise compliance features (SOC2, SAML, HIPAA-ready, data residency). DeepSeek&#8217;s advantages: open-weights variants (some, not all) enabling on-premises deployment, dramatically lower API cost, and competitive performance on standard knowledge benchmarks.<\/p>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\">For cost-sensitive high-volume work where the safety architecture is not the deciding factor, DeepSeek is the documented cheap path. For enterprise deployments where compliance, calibration, and agentic capability matter, Claude remains the more capable choice despite the price difference.<\/p>\n<\/p><\/div>\n<\/section>\n<section style=\"padding: 100px 48px;\">\n<div style=\"max-width: 900px; margin: 0 auto;\">\n<h2>What the Divergence Index Shows<\/h2>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\">The Suprmind <a href=\"https:\/\/suprmind.ai\/hub\/multi-model-ai-divergence-index\/#methods\">Multi-Model AI Divergence Index<\/a> measures how often AI models contradict and correct each other on real questions. Its Q3 2026 Edition re-checked 17,885 real questions answered by several AI models between July 30 and September 10, 2026, with model names hidden and answers in random order. The findings most relevant to Claude:<\/p>\n<ul class=\"feature-list\" style=\"margin: 0 0 32px;\">\n<li><span class=\"check\"><\/span><strong>Who gets corrected least:<\/strong> when at least two of three AI judges, from OpenAI, Anthropic and Google, had to agree, GPT, Perplexity and Claude answers were judged corrected by another model on 3 to 4% of answers. Gemini and Grok answers: 6.5% and 7.4%.<\/li>\n<li><span class=\"check\"><\/span><strong>No home advantage:<\/strong> no judge went easy on its own maker&#8217;s models. Anthropic&#8217;s judge was harder on Claude than the OpenAI and Google judges were.<\/li>\n<li><span class=\"check\"><\/span><strong>Stakes:<\/strong> on high-stakes questions, AI models draw more corrections, and their answers read about as sure: a blind judge found a correction in 45% of high-stakes turns, against 28% of low-stakes ones, at the same point in the conversation.<\/li>\n<li><span class=\"check\"><\/span><strong>Topics:<\/strong> AI models contradict each other about twice as often on financial questions as on medical ones: 40% against 20% of turns under a blind judge, at the same point in the conversation.<\/li>\n<li><span class=\"check\"><\/span><strong>Real agreement is rare:<\/strong> in 32 of the 13,563 questions our production judge read (0.2%), no model added, corrected or contradicted anything.<\/li>\n<\/ul>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\">A judge&#8217;s call is a reading, not proof, and judges from different companies read the same answers differently. So judges from OpenAI, Anthropic and Google all read the same 3,262 questions, and the index says which results hold under all three.<\/p>\n<p style=\"font-size: 14px; color: #9ca3af; margin: 0 0 32px;\">See also: <a href=\"https:\/\/suprmind.ai\/hub\/multi-model-ai-divergence-index\/#findings\" style=\"color: #8b5cf6;\">The Divergence Index findings at a glance \u2192<\/a><\/p>\n<\/p><\/div>\n<\/section>\n<section style=\"padding: 100px 48px;\">\n<div style=\"max-width: 900px; margin: 0 auto;\">\n<h2>When to Use Claude Alone vs When to Pair It<\/h2>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\">Five orchestration patterns follow. Each names a specific gap where single-model Claude use produces weaker outputs than a paired approach.<\/p>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\"><strong>High-stakes factual research.<\/strong> Pair Claude&#8217;s calibration with Perplexity&#8217;s citation-grounded retrieval. Claude&#8217;s HalluHard 30% with web search is the lowest of any model on realistic-conversation hallucination, but only with web search enabled. Perplexity&#8217;s citation accuracy (37% CJR error rate, the lowest tested) makes it the verifiable-source backstop.<\/p>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\"><strong>Financial domain analysis.<\/strong> Pair Claude with Gemini. In the Suprmind <a href=\"https:\/\/suprmind.ai\/hub\/multi-model-ai-divergence-index\/#topics\">Multi-Model AI Divergence Index<\/a> (Q3 2026 Edition), AI models contradict each other about twice as often on financial questions as on medical ones: 40% against 20% of turns under a blind judge, at the same point in the conversation. Gemini&#8217;s higher coverage catches answers Claude declines, and Claude&#8217;s calibration catches Gemini&#8217;s higher-coverage fabrications.<\/p>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\"><strong>Multi-modal document pipelines.<\/strong> Pair Claude&#8217;s reasoning with Gemini&#8217;s multimodal ingest. Claude reads only text and image; Gemini reads text, image, audio, and video natively. The Claude FACTS deficit (Opus 4.5: 51.3 vs Gemini 3 Pro 68.8) directly reflects this multimodal coverage gap.<\/p>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\"><strong>Business strategy with live signals.<\/strong> Pair Claude with Grok. Grok brings the live X stream. Claude validates that output before it reaches a decision.<\/p>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\"><strong>Iterative research analysis.<\/strong> Check the last answer, not only the first. In a chain of AI answers, the last word is rarely challenged: whatever the last model says goes unchallenged unless you ask again (<a href=\"https:\/\/suprmind.ai\/hub\/multi-model-ai-divergence-index\/#last-word\">Multi-Model AI Divergence Index<\/a>, Q3 2026). On long research threads, ask a second model to review Claude&#8217;s conclusions before you use them.<\/p>\n<p style=\"font-size: 14px; color: #9ca3af; margin: 0 0 32px;\">See also: <a href=\"https:\/\/suprmind.ai\/hub\/platform\/\" style=\"color: #8b5cf6;\">Multi-AI orchestration on Suprmind \u2192<\/a><\/p>\n<\/p><\/div>\n<\/section>\n<section style=\"padding: 100px 48px;\">\n<div style=\"max-width: 900px; margin: 0 auto;\">\n<h2>Sources<\/h2>\n<ul class=\"feature-list\" style=\"margin: 0 0 32px;\">\n<li><span class=\"check\"><\/span>Suprmind Multi-Model AI Divergence Index, Q3 2026 Edition (how often AI models contradict and correct each other, by model family, line-up, stakes and topic, 17,885 real questions)<\/li>\n<li><span class=\"check\"><\/span>Suprmind AI Hallucination Rates and Benchmarks (per-model hallucination data, May 2026 update)<\/li>\n<li><span class=\"check\"><\/span>Vellum AI &#8211; Claude Opus 4.7 benchmarks coverage<\/li>\n<li><span class=\"check\"><\/span>DataCamp &#8211; Claude vs Gemini comparison<\/li>\n<li><span class=\"check\"><\/span>pricepertoken.com &#8211; HLE leaderboard<\/li>\n<li><span class=\"check\"><\/span>ofox.ai &#8211; LLM leaderboard April 2026<\/li>\n<li><span class=\"check\"><\/span>Artificial Analysis &#8211; AA Index, AA-Omniscience methodology<\/li>\n<li><span class=\"check\"><\/span>Anthropic, OpenAI, Google DeepMind, xAI, DeepSeek, Perplexity official documentation<\/li>\n<\/ul>\n<p style=\"font-size: 18px; line-height: 1.8; color: rgba(255,255,255,0.9); margin: 0 0 24px;\">Last verified 2026-05-07.<\/p>\n<\/p><\/div>\n<\/section>\n<section id=\"faq\" aria-labelledby=\"faq-heading\" style=\"padding: 100px 48px; background: rgba(0,0,0,0.4);\">\n<div style=\"max-width: 900px; margin: 0 auto;\">\n<p class=\"section-label\">FAQ<\/p>\n<h2 id=\"faq-heading\">Frequently Asked Questions<\/h2>\n<div class=\"faq-accordion\">\n<details class=\"faq-item\" open>\n<summary class=\"faq-question\">\n                        <span>Is Claude better than ChatGPT?<\/span><br \/>\n                        <span class=\"faq-icon\" aria-hidden=\"true\">+<\/span><br \/>\n                    <\/summary>\n<p style=\"font-size: 16px;\">Depends on the task. Claude leads on autonomous multi-file coding (SWE-bench Pro 64.3% vs GPT-5.4&#8217;s 57.7%), hallucination calibration (AA-Omniscience 36% vs GPT-5.5&#8217;s 86%), and tool orchestration (MCP-Atlas 77.3% vs 68.1%). ChatGPT leads on image generation, plugin ecosystem, voice mode, and broader integrations (Apple Intelligence, Microsoft Copilot). In the Suprmind <a href=\"https:\/\/suprmind.ai\/hub\/multi-model-ai-divergence-index\/#corrected-least\">Multi-Model AI Divergence Index<\/a> (Q3 2026 Edition), GPT and Claude answers sit in the same group: judged corrected by another model on 3.4% and 4.3% of answers when at least two of three AI judges had to agree.<\/p>\n<\/details>\n<details class=\"faq-item\">\n<summary class=\"faq-question\">\n                        <span>Is Claude better than Gemini?<\/span><br \/>\n                        <span class=\"faq-icon\" aria-hidden=\"true\">+<\/span><br \/>\n                    <\/summary>\n<p style=\"font-size: 16px;\">On coding and calibration, Claude leads: SWE-bench Verified 87.6% vs Gemini 3.1 Pro 80.6%; AA-Omniscience hallucination 36% vs 50%; MCP-Atlas 77.3% vs 73.9%. <a href=\"https:\/\/suprmind.ai\/hub\/gemini\/pricing\/\" title=\"Gemini Pricing 2026: Free, AI Plus, AI Pro, AI Ultra, and API Costs\"  data-wpil-monitor-id=\"2518\">Gemini leads on price<\/a> (50% cheaper input, 40% cheaper output), knowledge breadth (AA-Omniscience accuracy 55.3% vs 47%), multimodal inputs (audio and video native; Claude has neither), ARC-AGI-2 (77.1% vs 68.8%), and BrowseComp (85.9% vs 79.3%).<\/p>\n<\/details>\n<details class=\"faq-item\">\n<summary class=\"faq-question\">\n                        <span>Is Claude better than Grok?<\/span><br \/>\n                        <span class=\"faq-icon\" aria-hidden=\"true\">+<\/span><br \/>\n                    <\/summary>\n<p style=\"font-size: 16px;\">On calibration and hallucination rate, Claude leads: AA-Omniscience hallucination 36% vs Grok 4&#8217;s 64%. In the Suprmind <a href=\"https:\/\/suprmind.ai\/hub\/multi-model-ai-divergence-index\/#model-by-model\">Multi-Model AI Divergence Index<\/a> (Q3 2026 Edition), in our mid-tier line-up, Grok 4.3 was judged corrected on 14.8% of its answers and Claude Sonnet 5 on 7.8%, over the same 778 questions. Grok leads on real-time X integration and speed on simple queries. For real-time event recall, Grok plus a calibration model (Claude or Perplexity) is the documented orchestration pattern. Grok alone has the highest documented citation hallucination rate of any model tested (Grok-3 at 94% on the CJR test).<\/p>\n<\/details>\n<details class=\"faq-item\">\n<summary class=\"faq-question\">\n                        <span>Is Claude better than Perplexity for research?<\/span><br \/>\n                        <span class=\"faq-icon\" aria-hidden=\"true\">+<\/span><br \/>\n                    <\/summary>\n<p style=\"font-size: 16px;\">Different strengths. Perplexity Sonar Pro scored 37% on the Columbia Journalism Review citation accuracy test &#8211; the lowest (best) of any model &#8211; because it is purpose-built for citation grounding. Claude is a parametric reasoning model that needs web search enabled to compete on citation accuracy. With Claude Opus 4.5 and web search enabled, HalluHard hits 30%; without web search, 60%. Pair them for high-stakes research.<\/p>\n<\/details>\n<details class=\"faq-item\">\n<summary class=\"faq-question\">\n                        <span>Is Claude better than DeepSeek?<\/span><br \/>\n                        <span class=\"faq-icon\" aria-hidden=\"true\">+<\/span><br \/>\n                    <\/summary>\n<p style=\"font-size: 16px;\">Different use cases. DeepSeek V3.2 costs $0.28\/$0.42 per million tokens versus Claude Opus 4.7&#8217;s $5\/$25 &#8211; 17-59x cheaper. DeepSeek scores 88.5 on MMLU but trails on agentic tooling, calibration, and enterprise compliance. Claude leads on safety architecture (Constitutional AI), agentic capability (Claude Code, Computer Use, MCP), and compliance features. For cost-sensitive volume work, DeepSeek; for high-stakes enterprise work, Claude.<\/p>\n<\/details>\n<details class=\"faq-item\">\n<summary class=\"faq-question\">\n                        <span>Which AI is most accurate?<\/span><br \/>\n                        <span class=\"faq-icon\" aria-hidden=\"true\">+<\/span><br \/>\n                    <\/summary>\n<p style=\"font-size: 16px;\">Depends on the metric. On AA-Omniscience accuracy (raw correct answers), Gemini 3.1 Pro leads at 55.3% versus Claude Opus 4.7&#8217;s 47%. On AA-Omniscience hallucination (errors as a proportion of attempts), Claude leads at 36% versus Gemini&#8217;s 50%. Claude 4.1 Opus achieves 0% hallucination by refusing uncertain queries &#8211; the lowest of any model. The trade-off is structural: Claude answers fewer questions but more correctly per attempt.<\/p>\n<\/details>\n<details class=\"faq-item\">\n<summary class=\"faq-question\">\n                        <span>Which <a href=\"https:\/\/suprmind.ai\/hub\/strongest-ai\/\" title=\"Strongest AI\"  data-wpil-monitor-id=\"2533\">AI is best<\/a> for coding?<\/span><br \/>\n                        <span class=\"faq-icon\" aria-hidden=\"true\">+<\/span><br \/>\n                    <\/summary>\n<p style=\"font-size: 16px;\">Claude Opus 4.7 currently leads on multi-file coding: SWE-bench Verified 87.6%, SWE-bench Pro 64.3% (industry high), CursorBench 70% (first model crossing 70%). For inline assistance, Cursor (using Claude or <a href=\"https:\/\/suprmind.ai\/hub\/chatgpt\/pricing\/\" title=\"ChatGPT Pricing 2026: What You Actually Get on Each Tier\"  data-wpil-monitor-id=\"2663\">GPT<\/a>) is the most-used IDE replacement. For basic integration, GitHub Copilot. For complex multi-repository refactoring and autonomous agentic coding, Claude Code.<\/p>\n<\/details>\n<details class=\"faq-item\">\n<summary class=\"faq-question\">\n                        <span>Which AI has the longest context window?<\/span><br \/>\n                        <span class=\"faq-icon\" aria-hidden=\"true\">+<\/span><br \/>\n                    <\/summary>\n<p style=\"font-size: 16px;\">Claude Opus 4.7, Claude Opus 4.6, Claude Sonnet 4.6, GPT-5.5, GPT-4.1, and Grok all support 1 million token context windows. Grok extends to 2 million tokens on the Fast variants. Most models output is capped at 128K-300K tokens regardless of input size. Per Suprmind benchmark notes, Claude Opus 4.7&#8217;s MRCR v2 long-context retrieval dropped to 32.2% on 1M from Opus 4.6&#8217;s 78.3% &#8211; Anthropic attributes this to error-reporting behavior rather than fabricating answers.<\/p>\n<\/details>\n<details class=\"faq-item\">\n<summary class=\"faq-question\">\n                        <span>Should I use one AI or multiple?<\/span><br \/>\n                        <span class=\"faq-icon\" aria-hidden=\"true\">+<\/span><br \/>\n                    <\/summary>\n<p style=\"font-size: 16px;\">For high-stakes professional work, multiple. In the Suprmind <a href=\"https:\/\/suprmind.ai\/hub\/multi-model-ai-divergence-index\/#methods\">Multi-Model AI Divergence Index<\/a> (Q3 2026 Edition), real agreement is rare: in 32 of the 13,563 questions our production judge read (0.2%), no model added, corrected or contradicted anything. The exception is low-stakes routine work where speed matters more than accuracy.<\/p>\n<\/details>\n<details class=\"faq-item\">\n<summary class=\"faq-question\">\n                        <span>What&#8217;s the best AI for financial analysis?<\/span><br \/>\n                        <span class=\"faq-icon\" aria-hidden=\"true\">+<\/span><br \/>\n                    <\/summary>\n<p style=\"font-size: 16px;\">Claude with Gemini paired. In the Suprmind <a href=\"https:\/\/suprmind.ai\/hub\/multi-model-ai-divergence-index\/#topics\">Multi-Model AI Divergence Index<\/a> (Q3 2026 Edition), AI models contradict each other about twice as often on financial questions as on medical ones: 40% against 20% of turns under a blind judge, at the same point in the conversation. Gemini&#8217;s higher coverage catches answers Claude declines, and Claude&#8217;s calibration catches Gemini&#8217;s higher-coverage fabrications.<\/p>\n<\/details><\/div>\n<\/p><\/div>\n<\/section>\n<section style=\"padding: 100px 48px; text-align: center;\">\n<div style=\"max-width: 800px; margin: 0 auto;\">\n<h2 style=\"font-size: 36px; margin-bottom: 24px;\">Stop guessing. Start cross-checking.<\/h2>\n<p style=\"font-size: 18px; color: rgba(255,255,255,0.85); margin: 0 auto 40px; max-width: 700px;\">\n                Suprmind runs your prompt across ChatGPT, Claude, Gemini, Grok, and Perplexity in parallel. See where they agree, where they disagree, and which insights only one model surfaced \u2014 before you act.\n            <\/p>\n<div style=\"display: flex; gap: 16px; justify-content: center;\">\n                <a href=\"\/signup\/spark\" class=\"btn-white\">Start Your Free Trial<\/a><br \/>\n                <a href=\"https:\/\/suprmind.ai\/hub\/platform\/\" class=\"btn-outline\">See How It Works<\/a>\n            <\/div>\n<\/p><\/div>\n<\/section>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Claude vs Other AI Models Claude vs ChatGPT vs Gemini vs Grok vs Perplexity: 2026 Honest Comparison Comparison content for AI models is a swamp. Vendor pages cherry-pick benchmarks. Aggregators copy each other. Headline numbers cite specialized configurations against general-purpose rivals. This page does the work in the open. Every claim cites the benchmark that [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":5140,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"wpai_generated_summary":"","wpai_meta_description":"","footnotes":""},"tags":[],"class_list":["post-5143","page","type-page","status-publish","hentry"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO Pro 5.0.4 - aioseo.com -->\n\t<meta name=\"description\" content=\"Honest comparison of Anthropic Claude vs ChatGPT, Gemini, Grok, Perplexity, and DeepSeek in 2026. 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