The most accurate AIin existence.
331 models. Real-time logprob scoring. Disagreement detection. Lossless context compaction. Ensemble search across every retrieval method. The only AI that mathematically verifies every claim before it reaches you.
HLE Accuracy (no tools)
Lead vs Next Best
Active Models
Proven Accuracy
The new leader on the world's
most challenging AI benchmark
Humanity's Last Exam (HLE) is 3,000 questions across 100+ subjects, created by 1,000+ domain experts. It's designed to remain difficult as AI advances. Our results are fully reproducible with complete traces. Read our in-depth white paper for detailed analysis.
Accuracy comparison
Ensemble beats every individual model
Even with our logprob confidence scoring and automatic retries, the best individual model in our ensemble scores ~45%. The ensemble reaches 52.15%, a 7+ point lead over its own constituent models. It even solves questions that zero individual models answered correctly, by piecing together partially correct fragments from different models and using low confidence scores to identify which pieces to trust.
HLE score achieved without tools
The benchmark score above used pure reasoning only: no code execution, no calculator, no external tools. Sup AI uses tools for everyday use, but the HLE result demonstrates the raw intelligence of our orchestration and confidence scoring alone.
Sup AI achieves 52.15% accuracy with 14+ percentage points ahead of the next best model (p<0.001).
If you need accurate answers, fewer hallucinations, or research-grade work that must be correct, Sup AI is your only option.
Disclaimer: These results are from an independent evaluation conducted by Sup AI (Dec 2025) and are not officially endorsed by the Center for AI Safety or Scale AI. Accuracy scores were calculated on a random sample of 1,369 questions from Humanity's Last Exam. All models, including competitors, were evaluated using enhanced settings (custom instructions, web search, and low-confidence retries) to maximize performance. Comparisons reflect model versions available at the time of testing, including "Preview" builds which are subject to change.
Our Secret Weapon
Real-time logprob
confidence scoring
We intercept probability distributions from every model at every token. We score each chunk independently. We detect disagreement between models. We retry when confidence is low. The confidence threshold adapts based on the mode selected by the orchestrator. Only mathematically verified chunks make your answer.
Adaptive confidence thresholds
The orchestrator selects a mode based on your query. Higher-stakes modes demand higher confidence before a chunk is accepted. Anything below the threshold is automatically retried.
Chunk-level scoring
Individual model responses
Verified output
Low-confidence chunks are discarded and retried. Only verified content reaches you.
Real-time cross-model disagreement detection
Model A
Model B
Model C
Each model structures its response differently. We search across all outputs in real time, matching chunks by meaning. The models agree on the date and sovereignty, but conflict on which war the treaty ended. That disagreement triggers an automatic retry of the affected chunks.
Emergent intelligence
Sometimes every single model in the ensemble returns an incorrect answer. But each wrong answer is wrong in a different way, and each model is uncertain about different parts. Because we track confidence at the chunk level, we can identify the low-confidence fragments in each response, discard them, and piece together the correct answer from the high-confidence fragments that remain. The result is a correct answer that no individual model produced. This is why Sup AI holds a 7+ point lead over every individual model in its own ensemble.
File Intelligence & Ensemble Search
10 GB uploads. Perfect memory.
The most thorough search in AI.
No single retrieval technique works best for every query. Keyword search misses semantic meaning. Embedding search misses exact phrases. Visual search misses text. We apply the same ensemble principle to search that we apply to models: run every method in parallel, fuse the results, and let the best answer emerge.
Query decomposition
Your query is rewritten for clarity, then decomposed into focused sub-questions that target different aspects of what you need
Triple-method parallel search
Each sub-question is searched three ways: by text meaning, by visual content, and by a hypothetical ideal answer we generate first
Hypothetical document embedding
We generate what the perfect answer would look like, then search for documents that match it. This finds results that keyword search misses entirely
Fusion and reranking
Results from all search methods are merged using rank fusion, then reranked by relevance to surface the best matches across every method
Context-aware boosting
Recent documents, attached files, and your active project context all receive priority boosts so the most relevant results always surface first
Deduplication and decay
Identical content found via text and visual search is deduplicated, and older conversation context is progressively down-weighted
This is what allows the most accurate AI in existence to reason across thousands of pages, hundreds of files, and every format you throw at it. If you work with documents, images, and files on a daily basis, Sup is your only option.
Complete Transparency
See exactly where every answer comes from
A sources sidebar displays every web search, document, and file we used to construct your response. Nothing is hidden. Everything is verifiable.
Web searches
Every web search performed by the AI is visible with full URLs and search queries used.
Document citations
Every document referenced is cited with page numbers and relevant excerpts highlighted.
File references
Every file page used to construct your response is referenced and clickable.
Inline citations
Click any inline citation to jump directly to the source material and verify the claim.
Q3 Financial Report 2025.pdf
Revenue grew 23% YoY driven primarily by enterprise expansion. Operating margin improved to 18.4%, reflecting efficiency gains from the restructured sales organization...
earnings-chart.png
Open filecompetitor-analysis.pdf
Infinite Context
Recursive lossless
context compaction
We support 331 models, and our ensemble runs up to 9 in parallel on every query. Some frontier models have 2 million token context windows. Some of the best specialized models have only 8,000. Fitting the same conversation into every model in the ensemble without losing information is a hard problem. We built the best solution.
The problem
A 50-page PDF, 20 uploaded images, and a long conversation can easily exceed 200K tokens. Other platforms either truncate (silently dropping the beginning of your conversation) or summarize (lossy compression that changes meaning). Either way, information is lost.
Our approach
We progressively compress your context through 5 levels. At each level, the information is restructured into a more compact form, but nothing is discarded that could change the answer. Goals, facts, decisions, constraints, and open questions are all preserved in structured form.
What this means for you
Your conversations never hit a wall. Upload hundreds of pages, have conversations that span weeks, and every model in the ensemble still sees your full context. Responses cost less, come back faster, and are just as accurate as if every model had unlimited memory.
Five levels of compression
Full context
Full conversation, files, and context. No compression needed.
100%
Structured extraction
Conversation distilled into structured state: goals, facts, decisions, open questions. Nothing lost.
70%
Context text removed
Retrieved text dropped, source references preserved. Models can still cite where information came from, and request full content if needed.
50%
File text removed
File text dropped, manifests kept. The AI still knows what files exist, what they contain, and can request full file content on demand.
30%
Maximum compression
Only the most relevant source references retained. Even at maximum compression, core knowledge survives.
15%
Model Ecosystem
331 models. 50+ providers.
More than any platform.
More active models than OpenRouter. From 7B parameters to multi-trillion. 32 of the best are preselected for you. Our intelligent orchestration layer automatically selects the optimal combination for your task.
GPT-5.4 Pro
OpenAI
Claude Opus 4.6
Anthropic
MiniMax M2.5
MiniMax
Gemini 3.1 Pro
GLM 5
zAI
Kimi K2.5
MoonshotAI
DeepSeek V3.2 Thinking
DeepSeek
Qwen3.5 Plus
Alibaba
Cost-optimized ensemble
Running multiple models sounds expensive, but we optimize thinking effort, model selection, and prompt adaptation per model to keep costs low. You get a guaranteed better answer at nearly the same price of running a single model.
Per-model prompt adaptation
Each model in the ensemble receives a prompt tailored to its strengths. We choose the optimal thinking effort, adjust formatting, and adapt the entire context for each model so it performs at its best.
Pricing
Simple pricing
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FAQ
Questions?
Everything you need to know about Sup AI.