Every frontier model fabricates, and not one of them warns you when it does. Suprmind runs five of them in one conversation where each reads the others and calls out what does not hold.
When one still slips through, an independent verifier catches it and corrects it before it reaches your document.
No credit card. All five frontier models. About twenty seconds.
by Anthropic
0%
AA-Omniscience hallucination rate
Suprmind hallucination hub
Why 0%: AA-Omniscience counts wrong answers among the answers a model actually attempts. Claude Opus 4.1 scores 0% because it abstains rather than guesses – the safest possible behaviour, and not what you hired an AI to do.
Holding the lowest-hallucination title for 5+ months. The average challenger holds just 3-5 weeks – Claude has not let go since February.
Lowest hallucination, by benchmark
Even the champion doesn’t lead every benchmark. No model does. That gap is the whole story.
A single model invents a statistic, a citation, a precedent, a clause reading. The answer looks clean. There is no second voice in the room to say otherwise, so it goes straight into your memo, your filing, your board deck. By the time anyone finds it, the decision is already made.
The rate sits around 5 to 10% on hard questions, higher on anything that needs a source or a real-world fact. And these models are trained on human approval, so they sound most certain exactly when they have the least to stand on.
A user uploaded two books and asked Grok to find a specific passage. What happened next is why single-AI workflows are dangerous.
The Test
The user gave Grok a verifiable task: find a sentence in an uploaded novel and continue the paragraph after it.
“…it was clear that they were not being moved on for strategic reasons – but”
Continue from here. The paragraph should pop up.
Grok
FabricatedGrok produced a fluent, confident paragraph of Warhammer prose. It referenced characters, locations, and themes from the books. It read like a direct quote.
It wasn’t in the book. Grok wrote it and presented it as retrieved text.
Claude
CaughtClaude ran 8 verification searches. Zero results. Then identified four tells proving fabrication: referencing the conversation’s own framework, generic phrasing, no page reference, and blended quote/interpretation.
Verdict: “Silent confabulation dressed up as sourced data.”
This is a real conversation from a real Suprmind session. Not a demo. Not a hypothetical. One AI fabricated. Another caught it. In the same thread, in front of the user.
With a single AI, you’d have a confident lie and no reason to question it.
The interactive 90-second demo runs right here on the page – scroll down to pause, scroll back up to resume. Hit the orange stop button to end it and explore everything that happened across chat, Scribe, Adjutant, and Master Document.
People come looking for the one safe model. The trouble is that the safest model this month is not the safest next month. The title trades between Claude, GPT, Gemini and Grok with almost every release, and which one leads depends entirely on which test you read. Vectara measures faithfulness to a source. AA-Omniscience measures whether a model knows what it does not know. FACTS, HalluHard and CJR each measure something else again. Five tests, five leaderboards.
Scan any column below and watch the leader change.
The proof, model by model – every benchmark crowns a different winner. The same cross-benchmark reference we maintain on our hallucination research page – every frontier model across every major benchmark, refreshed monthly. Scan any column and watch the leader change.
| Model | Provider | Vectara (Old) | Vectara (New) | AA-Omni Acc | AA-Omni Hall | AA-Omni Index | FACTS | HalluHard | CJR Citation |
|---|---|---|---|---|---|---|---|---|---|
| GPT-5.6 Sol (max) | OpenAI | – | – | 59.4% | 92.2% | 22.0 | – | – | – |
| GPT-5.6 Terra | OpenAI | – | – | 46.8% | 87.9% | 0.1 | – | – | – |
| GPT-5.6 Luna | OpenAI | – | – | 42.7% | 92.6% | -10.3 | – | – | – |
| GPT-5.3 Codex | OpenAI | – | – | 51.8% | – | – | – | – | – |
| GPT-5.5 (xhigh) | OpenAI | – | 9.3% | 57% | 86% | 20 | – | – | – |
| GPT-5.4 nano | OpenAI | – | 3.1% | – | – | – | – | – | – |
| GPT-5.2 (xhigh) | OpenAI | – | 10.8% | 43.8% | ~78% | – | 61.8 | 38.2% | – |
| GPT-5 | OpenAI | 1.4% | >10% | 40.7% | – | – | 61.8 | – | – |
| GPT-5.1 | OpenAI | – | – | 37.6% | 81% | Positive | 49.4 | – | – |
| GPT-4.1 | OpenAI | 2.0% | 5.6% | – | – | – | 50.5 | – | – |
| o3-mini-high | OpenAI | 0.8% | 4.8% | – | – | – | 52.0 | – | – |
| Claude Fable 5 (max, Opus 4.8 fallback) |
Anthropic | – | – | 65% | 63.6% | 43 | – | – | – |
| Claude Opus 5 (max) | Anthropic | – | – | 61% | 60.8% | 37 | – | – | – |
| Claude Sonnet 5 (max) | Anthropic | – | – | 40.1% | 39.4% | 16.5 | – | – | – |
| Claude 4.1 Opus | Anthropic | – | 11.8% | – | 0% | – | 46.5 | – | – |
| Claude Opus 4.8 | Anthropic | – | – | 46.6% | 35.9% | 27 | – | – | – |
| Claude Opus 4.7 | Anthropic | – | – | – | 36% | 26 | – | – | – |
| Claude Opus 4.6 | Anthropic | – | 12.2% | 46.4% | – | 14 | – | – | – |
| Claude Opus 4.5 | Anthropic | – | – | 45.7% | 58% | Negative | 51.3 | 30% | – |
| Claude Sonnet 4.6 | Anthropic | – | 10.6% | 40.0% | ~38% | – | – | – | – |
| Claude Sonnet 4.5 | Anthropic | – | 12.0% | – | 48% | – | 49.1 | – | – |
| Claude 3.7 Sonnet | Anthropic | 4.4% | – | – | – | – | – | – | – |
| Claude 4.5 Haiku | Anthropic | – | 9.8% | – | 25% | – | – | – | – |
| Gemini 3.1 Pro | – | 10.4% | 55.3% | 50% | 33 | – | – | – | |
| Gemini 3.7 Flash (high) | – | – | 55.3% | 64.5% | 26.5 | – | – | – | |
| Gemini 3.5 Flash | – | – | – | 61% | – | – | – | – | |
| Gemini 3 Pro | – | 13.6% | 55.9% | 88% | 16 | 68.8 | – | – | |
| Gemini 3 Flash | – | – | 54.0% | 91% | – | – | – | – | |
| Gemini 2.5 Pro | – | 7.0% | – | – | – | 62.1 | – | – | |
| Gemini 2.0 Flash | 0.7% | 3.3% | – | – | – | – | – | – | |
| Grok 4.6 (high) | xAI | – | – | 48.2% | 34.3% | 30.5 | – | – | – |
| Grok 4.5 | xAI | – | – | 52% | 54% | 26 | – | – | – |
| Grok 4.3 (high) | xAI | – | – | 35% | 25% | 18.3 | – | – | – |
| Grok 4.3 (medium) | xAI | – | – | – | 16% | ~17 | – | – | – |
| Grok 4.20 (Reasoning) | xAI | – | – | – | 17% | – | – | – | – |
| Grok 4.1 Fast | xAI | – | 20.2% | – | 72% | – | 36.0 | – | – |
| Grok 4 | xAI | 4.8% | >10% | 41.4% | 64% | Positive | 53.6 | – | – |
| Grok-3 | xAI | 2.1% | 5.8% | – | – | – | – | – | 94% |
| Perplexity Sonar Pro | Perplexity | – | – | – | – | – | – | – | 37% |
| DeepSeek V4 Pro 0813 (GA) | DeepSeek | – | – | 49.1% | 94.1% | 0.8 | – | – | – |
| DeepSeek V4 Pro (preview) | DeepSeek | – | 8.6% | – | 94% | -23 | – | – | – |
| DeepSeek V4 Flash | DeepSeek | – | – | – | 96% | – | – | – | – |
| DeepSeek-V3 | DeepSeek | 3.9% | 6.1% | – | – | – | – | – | – |
| DeepSeek-R1 | DeepSeek | 14.3% | 11.3% | – | 83% | – | – | – | – |
| Kimi K3 | Moonshot | – | – | 46% | 51% | 18 | – | – | – |
| GLM-5.3 (max) | Z.ai | – | – | 33.9% | 29.6% | 14.3 | – | – | – |
| Command A+ | Cohere | – | – | 9% | 14% | -4 | – | – | – |
| Qwen3.8 Max Preview | Alibaba | – | – | 31.9% | 41.7% | 3.4 | – | – | – |
| Qwen3.7 Max | Alibaba | – | – | 30% | 23% | 14 | – | – | – |
| Muse Spark 1.2 (xhigh) | Meta | – | – | 45.4% | 33.3% | 27.2 | – | – | – |
| Muse Spark 1.1 | Meta | – | – | 41% | 38% | 18 | – | – | – |
| Llama 4 Maverick | Meta | 4.6% | 8.2% | – | 87.6% | – | – | – | – |
Sources: Vectara HHEM Leaderboard (archived original dataset, last updated October 16, 2025, and the new 7,700-article dataset, snapshot May 11, 2026, re-checked August 27, 2026 - no 2026 flagship has been added to Vectara's board since May) [1][100], Artificial Analysis AA-Omniscience [2][64][72][88], Google DeepMind FACTS Suite, December 2025 launch snapshot [3], HalluHard Benchmark (2025) [5], Columbia Journalism Review (March 2025) [6]. AA-Omniscience rows for Claude Fable 5, Claude Opus 5, Claude Sonnet 5, GPT-5.6 Sol / Terra / Luna, Gemini 3.7 Flash, Grok 4.6, Muse Spark 1.2, GLM-5.3, Qwen3.8 Max Preview and DeepSeek V4 Pro 0813 are the August 26-27, 2026 snapshot of the AA-synced board (167 models). Artificial Analysis's own page confirms Fable 5 at Index 43 / 65% accuracy and Opus 5 at 37 / 61%. Every August row was checked for internal consistency (Index equals correct minus incorrect). Rows that pre-date August keep the launch figures first cited. On the August re-pull Gemini 3.1 Pro reads 54.9% / 50.9% / 31.9, Claude Opus 4.8 48.8% / 39.3% / 28.8, Grok 4.5 51.6% / 54.1% / 25.3 and Kimi K3 47.6% / 53.2% / 19.7, within a few points and in the same rank order. Muse Spark 1.1 is shown in its July configuration. The board also lists a reasoning configuration at 52.1% / 50.0% / 28.1, which is the fair comparison for the 1.2 (xhigh) row. Claude 4.1 Opus's 0% is the November 2025 launch snapshot, achieved by refusing uncertain questions. Grok 4.3 has a standalone AA profile in two configurations - high (35% accuracy / 25% hallucination / Index 18.3) and medium (16% hallucination, a lower-attempt build). DeepSeek V4 Pro is shown twice because the April preview and the August 13 GA build are different checkpoints. Dashes indicate no published data on that benchmark for that model.
Re-picking a model every few weeks is not a strategy. The durable answer is to stop betting on one. Run the question through all five at once and let their different training, different sources and different blind spots cancel each other out. Inside Suprmind, these benchmark scores decide which model fills which seat. That is a platform you run, not a model you gamble on.
What we treat external benchmarks as: inputs to model selection inside Suprmind, not proof that any single model is infallible. The full benchmark methodology and 2026 leaderboard breakdowns live in our AI hallucination research and benchmarks page.
Ask your next hard question in one thread where Grok, GPT, Claude and Gemini read each other’s answers and flag what does not hold. The lowest-hallucination setup is not a model. It is a platform.
Start the Free Trial7 days free. No credit card. Name, email, password – about twenty seconds.
A dual-layer hallucination mitigation system. One layer is the five models catching each other. The other is an independent verifier catching what they miss. Both run on every question.
Five frontier models share one thread. Each one reads every answer before it. When GPT states a figure Claude knows is wrong, Claude corrects it in the same turn and rebuilds the conclusion from the right number. When Perplexity’s sourced research contradicts Grok’s real-time read, that clash lands in front of you instead of hiding in a tab you never opened.
This is correction, not a flag to chase down later. The bad claim gets challenged and replaced while you watch it happen.
1,401 cross-model corrections across 1,324 production turns. 99.1% of multi-AI turns surfaced at least one contradiction, correction, or insight a single model missed.
Some errors survive layer one. A confident first answer anchors the four that follow. Several models lean on the same stale source. Sometimes nobody catches it. True North is built for exactly those cases, and it does not wait to be asked.
As each model works, True North checks every claim that carries risk in real time – numbers, dates, named entities, citations, legal and financial statements – against live sources on the web and the documents in your project. It runs outside the five models, so nothing is grading its own homework.
ANALYZE
Selects the claims worth checking
RESEARCH
Gathers live external evidence
JUDGE
Rules supported, contradicted, or unverifiable
When True North catches a fabrication, it does not just flag it. It injects the correction into the next model in the thread, so the false claim gets overwritten before it spreads. A single hallucination, left alone, tends to poison everything downstream, because the models trust each other too readily to re-check what came before. True North cuts that chain the moment it starts.
Five AIs keep each other honest.
Evidence keeps all five honest.
Not a lab benchmark. 45 days of real production decisions across finance, legal, medical, strategy, and technical work – scored for contradictions, corrections, and unique insights across Claude, GPT, Gemini, Grok, and Perplexity.
ORIGINAL RESEARCH
April 2026 Edition – The Confidence Trap
Suprmind’s own production data. 1,324 multi-AI turns across 299 users, scored for contradiction, correction, and unique insight per provider. The first systematic measurement of where five frontier AIs disagree, who catches whom, and how often confident answers don’t survive peer review.
9.77×
Perplexity vs Gemini catch ratio
51.3%
Of Gemini’s confident answers contradicted
72.1%
Disagreement on financial questions
LIVE BENCHMARK
September 2026 Edition – updated monthly
A continuously updated aggregator of every major AI hallucination benchmark – Vectara, AA-Omniscience, FACTS, HalluHard, CJR Citation – cross-referenced and enriched with Suprmind’s production findings. The most-cited single page on hallucination rates anywhere.
$4.4M
Average loss per organization from AI-related incidents (EY, Oct 2025)
88%
Gemini 3 Pro hallucination when uncertain
73-86%
Hallucination reduction with web search enabled
AI models learn from human feedback. Helpful, agreeable responses get rewarded. Pushback gets penalized. The result: when you ask a single AI whether your investment thesis holds up, whether your contract clause protects you, whether your strategy makes sense – it tends to find reasons you’re right. It smooths over the parts that should make you pause.
A multi-AI platform built around disagreement works differently. When GPT agrees with your framing but Claude flags the assumption underneath, you see both. When Perplexity’s sourced research contradicts Grok’s real-time read, that contradiction surfaces in the thread. Agreement becomes a signal, not a default. Disagreement becomes the most useful output a decision-maker can get.
Traditional LLM chats smooth over conflict.
Suprmind highlights it.
When the world’s smartest AIs disagree, that disagreement is telling you where your problem actually lives.
The category is crowded with tools that call themselves multi-AI platforms. Poe. ChatHub. OpenRouter. TypingMind. They solve one legitimate problem: one subscription instead of four. You pick a model from a dropdown, send your prompt, read the answer, switch models, start over.
That’s access, not orchestration. You still talk to one model at a time. You still reconcile contradictions manually. You still lose context every time you switch tabs. At the end, you have four isolated answers and no way to know which one missed the thing that mattered.
That is the difference the table above shows. Five frontier models answer in the same conversation and read each other – when one invents a fact, the next one flags it before it reaches your decision.
Start a Shared Thread Free7 days free. No credit card. Grok, GPT, Claude and Gemini in the trial – Perplexity joins on Pro.
Not all questions need the same structure. Suprmind runs models both in parallel (fast multi-perspective reads) and in sequence (deep iterative analysis) – inside the same platform, in the same thread.
Start in Sequential to build the case.
Switch to Super Mind for a fast consensus read.
Pivot to Debate to stress-test it. Red Team it before you commit.
The context persists across every mode switch. The models don’t forget.
Use Cases
Some of the use cases where multi-AI orchestration pays off.
Strategy Consultants
Walk into the partner meeting with five frontier AIs already disagreeing on your behalf. Each fabrication caught before slides leave your laptop.
Verdict
Do not acquire at $42M. Revisit at $26M with NRR turnaround proof.
Founders & Operators
Run a $79 vs $149 split through Debate mode. Watch Claude argue retention, Grok argue elasticity, Perplexity ground both in 2026 benchmarks.
Retention curve flattens past $99. The $50 of headroom buys you Frontier-buyer signaling.
Elasticity at this stage is brutal. You’ll lose 31% of conversions for ~22% revenue lift.
2026 SaaS prosumer benchmarks: 38% of $99+ tools see >40% trial-to-paid lift after price reduction.
AI Power Users
Cancel ChatGPT Pro, Claude Pro, Perplexity Pro, Gemini Advanced. One conversation. Five models. Shared context. $95/mo all-in.
Suprmind Frontier
All five models · one thread · shared context
$95
Investment Analysts
Five knowledge bases reference the same question. Build the strongest case for and against before capital gets committed.
How a multi-model AI platform catches what one AI misses – and why the most accurate AI setup is a workflow, not a model.
When Claude runs next in a Suprmind thread, it isn’t reading your question in a vacuum. It’s reading your question plus everything Grok, Perplexity, and GPT wrote before it. If one of those models fabricated a source, Claude can verify. If one of them smoothed over a weak assumption, Claude can flag it. The shared thread is what makes cross-checking possible.
Gemini closes the chain with synthesis. It sees every response and produces an output that’s structurally different from any single model’s answer. This is what “compounding intelligence” actually means – not five copies of the same response, but a response that evolved through five frontier models shaping each other.
Different problems need different orchestration. Switch modes mid-conversation without losing context. This is what makes Suprmind a multi-LLM orchestration platform rather than a model switcher.
AIs respond one after another. Each reads everything before it. The default and the deepest.
Best for:
Complex analysis, research, architecture decisions
All five respond simultaneously. A sixth AI synthesizes one unified answer with consensus and divergence mapped.
Best for:
Quick decisions, fact verification, time-sensitive calls
AIs argue assigned positions in sequence. Rebuttals and counter-arguments. Minority views preserved.
Best for:
Strategy validation, thesis stress-testing
AIs attack your plan from six angles in sequence: financial, technical, reputational, regulatory, operational, edge cases.
Best for:
Pre-launch validation, risk assessment, investment pre-mortems
Automated research pipeline that retrieves sources, analyses, fact-checks, challenges, and synthesises. Produces 10,000+ word reports with citations.
Best for:
Deep research, comprehensive reports
Strips a question to its fundamentals. Each model names its assumptions, identifies the underlying axioms, then rebuilds the analysis from the ground up.
Best for:
Highest-stakes decisions where convention is suspect
Sequential, Debate, Red Team, and First Principles all use sequential orchestration – each AI builds on what came before. Super Mind mode runs in parallel with a synthesis layer. Chain any combination mid-conversation.
Start in Sequential – four frontier AIs reading each other on your actual question. If they catch one thing you would have missed, the week paid for itself.
Run a Real Decision Free7 days free. No credit card. Sequential and Super Mind in the trial – Debate and Red Team join on Pro.
“5 AIs were a go-to resource in setting up our new business venture in NYC. From red teaming the initial idea (with harsh feedback), studio market and competitors analysis, to day to day brainstorming about launch phases and website setup. Being able to bounce any idea off 5 AIs, get a clear filtered answer and a todo list in 10 minutes helps a lot.”
CEO, OFF Studio NYC & Funduck Production
“I started using it for competitor research and it just kept expanding – new markets, risk reviews, compliance docs. Five different angles on the same question catches things I would have missed.”
CEO & Co-founder, Miss Amara
“We run everything through Suprmind now – new business ideas, client contracts, marketing strategies. Having five AIs push back on each other in one thread replaced hours of second-guessing between tools.”
Co-founder & COO, Global Digital Marketing Agency
“For analyzing business plans and evaluating client processes, the depth you get from five models reading each other is genuinely different. The Master Document export with custom prompt alone saves me hours on final reports.”
Senior International Adviser, EBRD – European Bank for Reconstruction and Development
Disagreement is the feature.
Run your next hard question through five frontier models in one conversation. Watch them fact-check each other, disagree with each other, and leave you with a deliverable you can actually defend.
7 days free. No credit card. Grok, GPT, Claude and Gemini in the trial – Perplexity joins on Pro.
FAQ
No single AI model wins across every task. Benchmarks rank different models highest depending on whether you’re testing summarization faithfulness, citation accuracy, grounded factuality, or general reasoning – Vectara puts one model on top, AA-Omniscience another, FACTS a third. The practical answer for real work is not one model with the lowest hallucination rate. It is running five frontier models in one thread where they correct each other, backed by an independent verifier that checks the claims none of them questioned. Suprmind calls that the AI Anti-Hallucinogen. See the full 2026 benchmark breakdown.
On any single benchmark, you will see a leaderboard with one model on top. Those numbers are real for that specific test – and they don’t generalize to every business question. Vectara HHEM measures faithfulness to a source document. AA-Omniscience measures whether a model knows what it doesn’t know. FACTS measures grounded factuality across four different slices. A model that scores best on one routinely falls mid-pack on another. Suprmind treats benchmarks as inputs to model selection inside the platform, not as proof that one LLM is infallible on your specific work.
For high-stakes work – acquisitions, IC memos, compliance review, legal interpretation, strategy validation – the most accurate AI setup is a multi-AI system that surfaces disagreement, not a single AI optimized for a benchmark. In 1,324 production turns measured by Suprmind, 99.1% of multi-AI turns surfaced at least one contradiction, correction, or unique insight that a single model would have missed. That is the category Suprmind occupies – the workflow that catches what one AI alone cannot.
No system built on current large language models can eliminate hallucinations. Every frontier AI fabricates at some rate, especially on questions requiring citation, retrieval, or real-world grounding. Suprmind doesn’t fix that at the model level, because it cannot be fixed there. It works structurally: five models in one thread means each answer can be contradicted by the next, and an independent verifier checks every risky claim against live sources and corrects what is wrong before it reaches your document. The fabrication still happens. It just does not survive.
AI models fail in different ways. GPT, Claude, Gemini, Grok, and Perplexity were trained on different data with different reasoning patterns, different tool access, and different guardrails. When all five process the same question in a shared thread, their failure modes collide visibly instead of compounding privately. In Suprmind’s research dataset, Perplexity caught 9.77 times more cross-model errors than Gemini – which means whichever single model you’d have picked, the others were positioned to catch what it missed. That is the lowest hallucination AI workflow in practice: not a “best model” bet, but five-model cross-verification.
For compliance work, the risk is not just invented facts – it is overstated certainty. A single AI will read an ambiguous regulatory clause and produce a confident interpretation without flagging that the interpretation is contested. Suprmind’s Red Team mode assigns models to six attack vectors specifically including regulatory exposure – one model is tasked with finding where the output is more confident than the underlying regulation supports. Where the five models diverge on interpretation is exactly where you have real ambiguity, and exactly where a single AI would have hidden it.
Spark starts at $19/month with a 7-day free trial and no credit card required – four frontier AI models (Perplexity joins on Pro), Sequential and Super Mind orchestration. Pro is $45/month and adds Perplexity, Debate, Red Team, and First Principles modes plus the full decision intelligence layer. Frontier is $95/month with the full model lineup and Master Project cross-workspace memory. Power is $195/month with bring-your-own-keys and the highest usage capacity of any self-serve plan. Enterprise pricing is per-seat plus a managed AI allocation, sized on a discovery call. One subscription covers every model in your tier – no separate ChatGPT Plus, Claude Pro, or Perplexity Pro fees layered on top. See all plans.
Disagreement is the feature.
A multi-AI platform for professionals who need more than one perspective.
