{"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":"2026-05-07T22:12:13","modified_gmt":"2026-05-07T22:12:13","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":"
\n
\n
\n
Claude vs Other AI Models<\/div>\n

Claude vs ChatGPT vs Gemini vs Grok vs Perplexity: 2026 Honest Comparison<\/h1>\n

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 Claude loses, we show the loss. Two findings frame everything below.<\/p>\n<\/p><\/div>\n<\/section>\n

\n
\n

First, Claude Opus 4.7’s calibration delta is the largest of any provider tested in production. Per the Suprmind Multi-Model Divergence Index, April 2026 Edition (n=1,324 production turns), Claude’s confidence-contradicted rate drops from 33.9% on all turns to 26.4% on high-stakes turns – a -7.5 point shift no other tested provider matches. The next-best is GPT\/ChatGPT at -3.4 points; Gemini barely moves at -1.1 points. Claude slows down measurably when consequences are real; others do not.<\/p>\n

Second, Claude Opus 4.7 holds an AA-Omniscience hallucination rate of 36% versus GPT-5.5’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 – and the cost is approximately 8 points of raw accuracy on the same benchmark (47% vs Gemini 3.1 Pro’s 55.3%).<\/p>\n

See also: Suprmind Multi-Model Divergence Index \u2192<\/a><\/p>\n<\/p><\/div>\n<\/section>\n

\n
\n

Quick Verdict: Where Each Model Wins<\/h2>\n<\/p><\/div>\n
\n
\n
Model<\/div>\n
Best at<\/div>\n
Worst at<\/div>\n<\/p><\/div>\n
\n
**Claude Opus 4.7**<\/div>\n
Multi-file coding (SWE-bench Pro 64.3%); calibration; tool orchestration (MCP-Atlas 77.3%); high-stakes refusal<\/div>\n
Image\/audio\/video generation (none); knowledge breadth; multimodal input ingest<\/div>\n<\/p><\/div>\n
\n
**GPT-5.5**<\/div>\n
Image generation; voice; plugin breadth; speed on simple queries<\/div>\n
Hallucination calibration (86% AA-Omniscience); SWE-bench Pro<\/div>\n<\/p><\/div>\n
\n
**Gemini 3.1 Pro**<\/div>\n
Multimodal input (audio + video native); knowledge breadth (55.3% AA-Omni accuracy); BrowseComp; ARC-AGI-2<\/div>\n
High-stakes calibration (-1.1 point delta); multi-file coding<\/div>\n<\/p><\/div>\n
\n
**Grok 4.3 (Heavy)**<\/div>\n
Real-time X stream integration; long context (2M); contrarian ideation<\/div>\n
Citation accuracy (94% CJR hallucination on Grok-3); calibration<\/div>\n<\/p><\/div>\n
\n
**Perplexity Sonar Pro**<\/div>\n
Citation grounding (37% CJR best); catch ratio 2.54 (highest); retrieval freshness (24-48h lag)<\/div>\n
Pure reasoning depth without retrieval; agentic tool use<\/div>\n<\/p><\/div>\n
\n
**DeepSeek V3.2**<\/div>\n
Cost ($0.28\/$0.42 per million tokens); on-prem deployment (some variants open-weights)<\/div>\n
Agentic tooling maturity; safety architecture; enterprise compliance<\/div>\n<\/p><\/div>\n<\/p><\/div>\n
\n <\/div>\n<\/section>\n
\n
\n

Benchmark Comparison<\/h2>\n<\/p><\/div>\n
\n
\n
Benchmark<\/div>\n
Claude Opus 4.7<\/div>\n
GPT-5.5 \/ 5.4<\/div>\n
Gemini 3.1 Pro<\/div>\n
Grok 4 \/ 4.3<\/div>\n
DeepSeek V3.2<\/div>\n<\/p><\/div>\n
\n
GPQA Diamond<\/div>\n
94.2%<\/div>\n
GPT-5.4: 94.4%<\/div>\n
94.3%<\/div>\n
not reported<\/div>\n
not reported<\/div>\n<\/p><\/div>\n
\n
SWE-bench Verified<\/div>\n
87.6%<\/div>\n
not publicly confirmed<\/div>\n
80.6%<\/div>\n
not reported<\/div>\n
not reported<\/div>\n<\/p><\/div>\n
\n
SWE-bench Pro<\/div>\n
64.3% (industry high)<\/div>\n
GPT-5.4: 57.7%<\/div>\n
not reported<\/div>\n
not reported<\/div>\n
not reported<\/div>\n<\/p><\/div>\n
\n
AA Intelligence Index<\/div>\n
57 (3-way tie)<\/div>\n
GPT-5.4: 57<\/div>\n
57<\/div>\n
DeepSeek V3.2: 51.5<\/div>\n
\u2014<\/div>\n<\/p><\/div>\n
\n
LMArena Elo (Text)<\/div>\n
1504<\/div>\n
~1482<\/div>\n
~1493<\/div>\n
not reported<\/div>\n
not reported<\/div>\n<\/p><\/div>\n
\n
OSWorld (Computer Use)<\/div>\n
78%<\/div>\n
GPT-5.5: 78.7%<\/div>\n
not published<\/div>\n
not reported<\/div>\n
not reported<\/div>\n<\/p><\/div>\n
\n
MCP-Atlas<\/div>\n
77.3%<\/div>\n
GPT-5.4: 68.1%<\/div>\n
73.9%<\/div>\n
not reported<\/div>\n
not reported<\/div>\n<\/p><\/div>\n
\n
HLE (with tools)<\/div>\n
54.7% (1st)<\/div>\n
not reported<\/div>\n
51.4%<\/div>\n
not reported<\/div>\n
not reported<\/div>\n<\/p><\/div>\n
\n
BrowseComp<\/div>\n
79.3%<\/div>\n
not publicly disclosed<\/div>\n
85.9%<\/div>\n
not reported<\/div>\n
not reported<\/div>\n<\/p><\/div>\n
\n
ARC-AGI-2<\/div>\n
Opus 4.6: 68.8%<\/div>\n
not reported<\/div>\n
77.1%<\/div>\n
not reported<\/div>\n
not reported<\/div>\n<\/p><\/div>\n
\n
AA-Omniscience Hallucination<\/div>\n
36%<\/div>\n
GPT-5.5: 86%<\/div>\n
50%<\/div>\n
Grok 4: 64%<\/div>\n
not reported<\/div>\n<\/p><\/div>\n
\n
AA-Omniscience Index<\/div>\n
26 (2nd overall)<\/div>\n
GPT-5.5: 20<\/div>\n
33<\/div>\n
Grok 4: 64<\/div>\n
\u2014<\/div>\n<\/p><\/div>\n
\n
HalluHard (Opus 4.5 with web)<\/div>\n
30% (lowest)<\/div>\n
not in same cycle<\/div>\n
not reported<\/div>\n
not reported<\/div>\n
not reported<\/div>\n<\/p><\/div>\n
\n
FACTS (Opus 4.5)<\/div>\n
51.3<\/div>\n
not reported<\/div>\n
68.8<\/div>\n
not reported<\/div>\n
not reported<\/div>\n<\/p><\/div>\n<\/p><\/div>\n
\n

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

A note on saturation: GPQA Diamond has compressed at the frontier – all three top labs’ 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

\n
\n

Hallucination Rates Compared<\/h2>\n

Per Suprmind’s AI Hallucination Rates and Benchmarks reference (May 2026 update), the AA-Omniscience hallucination cohort spread is:<\/p>\n<\/p><\/div>\n

\n
\n
Model<\/div>\n
AA-Omniscience Hallucination<\/div>\n
AA-Omniscience Accuracy<\/div>\n
Index<\/div>\n<\/p><\/div>\n
\n
Claude 4.1 Opus (early run)<\/div>\n
0%<\/div>\n
36% (early run)<\/div>\n
4.8<\/div>\n<\/p><\/div>\n
\n
Claude Opus 4.7<\/div>\n
36%<\/div>\n
~47%<\/div>\n
26<\/div>\n<\/p><\/div>\n
\n
Claude Opus 4.6<\/div>\n
not reported<\/div>\n
46.4%<\/div>\n
14<\/div>\n<\/p><\/div>\n
\n
Claude Opus 4.5<\/div>\n
58%<\/div>\n
45.7%<\/div>\n
Negative<\/div>\n<\/p><\/div>\n
\n
Claude Sonnet 4.6<\/div>\n
~38%<\/div>\n
40.0%<\/div>\n
not reported<\/div>\n<\/p><\/div>\n
\n
Claude Haiku 4.5<\/div>\n
25%<\/div>\n
not reported<\/div>\n
not reported<\/div>\n<\/p><\/div>\n
\n
GPT-5.5<\/div>\n
86%<\/div>\n
not reported<\/div>\n
20<\/div>\n<\/p><\/div>\n
\n
GPT-5.2<\/div>\n
~78%<\/div>\n
43.8%<\/div>\n
not reported<\/div>\n<\/p><\/div>\n
\n
Gemini 3.1 Pro<\/div>\n
50%<\/div>\n
55.3%<\/div>\n
33<\/div>\n<\/p><\/div>\n
\n
Grok 4<\/div>\n
64%<\/div>\n
not reported<\/div>\n
not reported<\/div>\n<\/p><\/div>\n<\/p><\/div>\n
\n

Source: Suprmind AI Hallucination Rates and Benchmarks, 2026-04-26.<\/p>\n

Three patterns matter. First, Claude’s calibration-by-refusal architecture produces both the lowest hallucination rates across the cohort and lower raw accuracy than Gemini 3.1 Pro – Claude answers fewer questions in total but more correctly as a proportion of attempts. Second, GPT-5.5’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

See also: Claude hallucination rates across benchmarks \u2192<\/a><\/p>\n<\/p><\/div>\n<\/section>\n

\n
\n

Where Claude Wins<\/h2>\n

Calibration under high stakes<\/strong> is Claude’s best-documented advantage. Per the Suprmind Multi-Model Divergence Index (April 2026, n=1,324 production turns), Claude’s confidence-contradicted rate drops from 33.9% on all turns to 26.4% on high-stakes turns – a -7.5 point delta no other provider matches. ChatGPT drops 3.4 points; Gemini barely moves at -1.1. This is the single most defensible empirical distinction for Claude in a multi-model context.<\/p>\n

Refusal-over-fabrication on knowledge limits.<\/strong> Claude 4.1 Opus achieved 0% AA-Omniscience hallucination by refusing uncertain queries – 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

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

Complex multi-file coding (SWE-bench Pro).<\/strong> Claude Opus 4.7’s 64.3% on SWE-bench Pro is the current industry high – 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

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

Unique professional analysis insights.<\/strong> Per the Suprmind Multi-Model Divergence Index, Claude generated 631 unique insights (24.5% share, second only to Perplexity’s 636\/24.7%) with 268 rated critical-severity. Claude is the second-best engine for novel insight generation in a multi-model ensemble.<\/p>\n

See also: AI catch ratio data \u2192<\/a><\/p>\n<\/p><\/div>\n<\/section>\n

\n
\n

Where Claude Loses<\/h2>\n

Knowledge breadth.<\/strong> Claude Opus 4.7’s AA-Omniscience accuracy of approximately 47% trails Gemini 3.1 Pro’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

Multimodal coverage.<\/strong> Claude accepts only text and image. Gemini 3 Pro accepts text, image, audio, and video natively. Claude’s FACTS multi-dimensional factuality score (Opus 4.5: 51.3) trails Gemini 3 Pro (68.8) by 17 points – 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

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

ARC-AGI-2.<\/strong> Gemini 3.1 Pro leads at 77.1% versus Claude Opus 4.6’s 68.8%.<\/p>\n

BrowseComp.<\/strong> Gemini 3.1 Pro at 85.9% leads Claude Opus 4.7 at 79.3%.<\/p>\n

Self-consistency in iterative research.<\/strong> Per the Suprmind Multi-Model Divergence Index (April 2026), Claude vs Claude is the top combative pair in the ResearchAnalysis domain – 10 contradictions across 74 turns, a 13.5% intra-model contradiction rate. The Claude-vs-Claude contradiction pattern is the single most important orchestration signal for users deploying Claude on iterative research workflows.<\/p>\n

See also: Suprmind’s AI Hallucination Rates and Benchmarks reference \u2192<\/a><\/p>\n<\/p><\/div>\n<\/section>\n

\n
\n

Claude vs ChatGPT<\/h2>\n

Claude leads on autonomous multi-file coding (SWE-bench Pro 64.3% vs GPT-5.4’s 57.7%), hallucination calibration (AA-Omniscience 36% vs GPT-5.5’s 86%), tool orchestration (MCP-Atlas 77.3% vs GPT-5.4’s 68.1%), and high-stakes calibration (-7.5 point Divergence Index delta vs ChatGPT’s -3.4). 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

Per the Suprmind Multi-Model Divergence Index (April 2026, n=1,324 production turns), Claude’s high-stakes confidence-contradiction rate of 26.4% is 9.8 points lower than ChatGPT’s 36.2%. ChatGPT’s catch ratio of 0.38 is the lowest in the five-provider cohort versus Claude’s 2.25.<\/p>\n

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

See also: ChatGPT 2026 overview \u2192<\/a><\/p>\n<\/p><\/div>\n<\/section>\n

\n
\n

Claude vs Gemini<\/h2>\n

On coding, agentic tooling, and hallucination calibration, Claude leads: SWE-bench Verified 87.6% vs Gemini 3.1 Pro’s 80.6%; AA-Omniscience hallucination 36% vs 50%; MCP-Atlas 77.3% vs 73.9%; high-stakes calibration delta -7.5 vs -1.1.<\/p>\n

Gemini leads on price (Gemini 3.1 Pro is approximately $2.50\/$15 per million tokens vs Claude Opus 4.7’s $5\/$25 – 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

Per the Suprmind Multi-Model Divergence Index, Financial domain analysis is the highest-disagreement domain at 72.1%, and Claude vs Gemini is the top combative pair in Financial at 37 contradictions. This positions Claude as the necessary calibration partner against Gemini’s higher-coverage approach in financial reasoning.<\/p>\n<\/p><\/div>\n<\/section>\n

\n
\n

Claude vs Grok<\/h2>\n

Claude leads on calibration and hallucination rate. Claude Opus 4.7 holds AA-Omniscience hallucination at 36% versus Grok 4’s 64% – a 28 percentage-point gap. Claude’s catch ratio of 2.25 in production is over 3x Grok’s 0.72.<\/p>\n

Grok leads on real-time X integration (no other frontier model has direct access to the X content stream), speed on simple queries, and contrarian ideation in business strategy contexts. Per the Suprmind Multi-Model Divergence Index, Gemini vs Grok is the most combative pair in Business Strategy with 59 contradictions – a domain where Claude can serve as the validator on the Gemini-Grok output to reduce volatility.<\/p>\n

Pricing: Grok API is approximately $1.25\/$2.50 per million tokens for the standard model – 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 rate of any model tested (Grok-3: 94% on the Columbia Journalism Review citation accuracy test).<\/p>\n

See also: Grok complete guide \u2192<\/a><\/p>\n<\/p><\/div>\n<\/section>\n

\n
\n

Claude vs Perplexity<\/h2>\n

Claude and Perplexity are the two strongest verification-layer models in production. Per the Suprmind Multi-Model Divergence Index, the catch ratio cohort is: Perplexity 2.54, Claude 2.25, Grok 0.72, ChatGPT 0.38, Gemini 0.26. Combined, Claude and Perplexity account for 60.7% of all corrections in the n=1,324-turn study.<\/p>\n

Where they differ structurally: Perplexity Sonar Pro is a search-integrated model purpose-built for citation grounding – 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’s CJR-equivalent performance is meaningfully worse. With Claude Opus 4.5 and web search, HalluHard hits 30%; without web search, 60%.<\/p>\n

The orchestration recommendation: pair Claude’s reasoning-and-calibration with Perplexity’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

\n
\n

Claude vs DeepSeek<\/h2>\n

The primary difference is cost. DeepSeek V3.2 costs $0.28\/$0.42 per million tokens versus Claude Opus 4.7’s $5\/$25 – 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

Claude’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’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

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

\n
\n

What the Divergence Index Shows<\/h2>\n

The Suprmind Multi-Model Divergence Index, April 2026 Edition, measured five providers (Claude, ChatGPT, Gemini, Grok, Perplexity) across 1,324 production turns from 700 sessions across 299 external users. Every turn was scored for contradictions, corrections, and unique insights. The findings most relevant to Claude positioning:<\/p>\n