The most accurate AIin existence.
Other AIs give you one guess. Sup asks 334 models at once, scores every claim based on confidence, and synthesizes a mathematically verified answer. #1 on Humanity's Last Exam by a 7-point lead.
HLE accuracy
Lead vs next best
Active models
Sign up for free to start chatting. $10 in credits, no credit card needed. When they run out, keep chatting with free models.
See It In Action
Watch the demo
Proven Accuracy
#1 on the hardest AI benchmark
Humanity's Last Exam is 2,500 expert-written questions designed to keep getting harder as AI improves. Sup beats every frontier model, and our results are fully reproducible. See the white paper.
Accuracy comparison
Ensemble beats every individual model
Even with our logprob confidence scoring, 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 leader with web search only
All models were evaluated under the same enhanced conditions: custom prompts and web search. No code execution, no calculator, no other tools. Sup AI uses additional tools for everyday use, but the HLE result demonstrates our orchestration with web search alone.
Sup AI achieves 52.15% accuracy with 7+ percentage points ahead of every model in the ensemble (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 and web search) to maximize performance. Comparisons reflect model versions available at the time of testing, including "Preview" builds which are subject to change.
Model Ecosystem
334 models. 56 authors.
More than any platform.
Frontier giants and specialized experts, from 7B to multi-trillion parameters. Sup picks the right combination for each question.
GPT-5.4 Pro
OpenAI
Claude Opus 4.7
Anthropic
MiniMax M2.7
MiniMax
Gemini 3.1 Pro
GLM 5.1
zAI
Kimi K2.5
MoonshotAI
DeepSeek V3.2 Thinking
DeepSeek
Qwen3.6 Plus
Alibaba
Cheaper than it sounds
Running multiple models sounds expensive. Our per-model optimization means you pay nearly the same as one model for a guaranteed better answer.
Per-model prompts
Each model gets a prompt tailored to its strengths — optimized thinking effort, adapted context — so it performs at its best.
Pricing
No limits. Ever.
Free forever, better with credits.
No message caps, no weekly quotas, no rate limits. $10 in free credits to start, no card needed. When they run out, keep chatting with our 18 free models. Credits never expire.
Out of credits? You can keep using Sup AI for free with 18 free models. Add credits to unlock the full frontier ensemble.
Unlike ChatGPT and Claude, your unused credits never expire and roll over every month
How We Stay Accurate
Every claim,
mathematically verified.
We score every chunk of every model's response as it's written. Low-confidence chunks get retried. Disagreements trigger a rerun. Only verified content reaches you.
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.
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
Reason across thousands of pages, hundreds of files, every format. If you work with documents daily, Sup is your only option.
Always Cited
See exactly where every answer comes from
A sources sidebar shows every search, document, and file used to build your response. 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 334 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 8 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.
Eight 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%
Source references trimmed
Only the most relevant source references retained. Even at maximum compression, core knowledge survives.
15%
Sources removed
All source references removed. The model works from conversation state and the current message only.
10%
Conversation state removed
Conversation state dropped. The model sees only the system prompt and the current user message.
6%
Message truncated
User message text proportionally truncated to fit the smallest context windows. The model still receives a valid request.
3%
FAQ