AI has the intelligence. We give it infrastructure.
One MCP connection. Claude, ChatGPT, or any AI gains real Python execution, live web search, database access, and parallel processing — all pre-computed before touching your token budget. 96.5% fewer tokens. Provably accurate.
96.5%
Token reduction verified Anthropic API
$42k
Saved / year at 10k queries/day
1 URL
To connect any MCP-compatible AI
LIVE EXECUTION
universalbench — execution runtime
Python
Search
DB
Tokens saved
96.5%
Accuracy
100%
0%
Token reduction real Anthropic API test
$0
Saved per year at 10k queries/day
0%
ROI on enterprise tier
1 URL
That's all it takes. Any MCP-compatible AI.
The real problem
Your AI is guessing. UB makes it compute.
Every enterprise team, every funded startup, every developer paying for Claude or GPT-4 faces the same silent problem: LLMs hallucinate on data, hit token limits on context, and can't execute real code. UniversalBench pre-computes every answer before it reaches your model. Facts. Not guesses.
Same prompt. Different path. Different outcome.
Prompt
Raw to LLM
Hallucinated answer
WRONG
Prompt
UniversalBench
Computed answer
CORRECT
⚡
UB pre-computes and sends only the result to your LLM. 96.5% fewer tokens. Every time.
Integration
Three steps. Everything changes.
01
Sign up, get your key
30 seconds. No credit card. 50 free executions included — enough to run a real benchmark against your current workflow.
ubk_a3f9c21e84d0...
02
Paste one URL into your AI
Claude, ChatGPT, Gemini, Cursor — any MCP-compatible client. One URL in your integration settings. Two minutes of work.
mcp.universalbench.dev/sse
03
Your AI gains real execution
Code runs. Data queries. Search verifies. LLM routing activates. Every result pre-computed — your model gets facts, not prompts.
web_search, code, db, github...
Capabilities
Not just another tool list.
These are the capabilities that separate AI infrastructure from AI toys. Every one of these was identified by watching enterprise AI workflows fail — and building the fix into the platform.
Core differentiator
Pre-computation token filter
Every other MCP sends your raw data to the AI. UniversalBench runs computation first and only sends the result to your LLM. That's the entire reason for the 96.5% token reduction. This is the architecture. Not a feature.
◆ No other MCP does this
WITHOUT UB
WITH UB
96.5% of tokens never touch your AI
Verifiable accuracy
Python computes it. LLM confirms it.
LLMs are extraordinary language models. They are terrible calculators. When UniversalBench runs it in Python, the answer is deterministic and auditable. Run the same query a thousand times. Get the same answer.
◆ Audit-ready execution logs
😤
LLM guesses: "There appear to be approximately 37 errors in this log file..." 44% wrong.
100% accurate · 96.5% fewer tokens · Audit log generated
No vendor lock-in
Works with Claude, GPT-4, Gemini, all of them.
Connect once. Works everywhere. Every MCP-compatible AI client gets the same execution infrastructure. Switch providers without re-integrating. Add models without new API keys.
◆ Truly provider-agnostic
Claude (Anthropic)
✓ connected
ChatGPT (OpenAI)
✓ connected
Gemini (Google)
✓ connected
Any MCP-compatible client
✓ connected
One URL → all of them, forever
Enterprise-grade throughput
8 parallel threads. No timeout anxiety.
Single-threaded MCPs with 60-second hard limits are a ceiling. UniversalBench runs 8 concurrent execution threads per session with async background jobs that have no timeout. Your pipeline doesn't wait.
◆ 8x parallel throughput
8 threads — concurrent execution
Thread 1
DONE ✓
Thread 2
RUNNING
Thread 3
DONE ✓
Thread 4
RUNNING
5–8
QUEUED
Async background jobs · no timeout limit
Cost visibility
ROI dashboard. Real dollars. Every session.
Finance teams, CTOs, founders — everyone asks "what are we getting for our AI spend?" UniversalBench answers that question automatically. Token savings, cost delta, and ROI calculated in real time.
◆ Built-in cost justification
TOKENS SAVED THIS MONTH
184,000
COST SAVED (CLAUDE PRICING)
$5.52
PLAN COST THIS MONTH
$19.00
ANNUALISED PROJECTION
$66.24 saved
LLM routing layer
Any model. One call. Cheapest path, auto-selected.
100+ models via OpenRouter, called directly from within your execution session. No separate API keys. No separate integrations. Route analytically to Claude, creatively to GPT-4o, multimodally to Gemini. All from one session.
◆ Intelligent model routing
invoke_llm("analyse Q3 revenue", model="auto")
↓ UB selects cheapest capable model
claude-4 selected
gpt-4o
gemini-2
llama-3
mistral
+95
OpenRouter · One API key · Best price per task
Verified results
Three tests. Run live. Verified in Anthropic console.
Not benchmarks. Not simulations. These were run against the real Anthropic API on real data. Every number is reproducible. The console logs exist. We are inviting you to verify them.
Logs verifiable in Anthropic console · claude-sonnet-4-20250514
Test 01 — Web Search Capability
The AI that said "I can't search" suddenly searches everything.
Without UniversalBench: Claude returned 30 tokens — "I cannot search the web." Task failed completely. With UB connected, the exact same prompt returned live 2026 data with citations. 5,228 tokens. Complete answer.
This is not a performance improvement. This is the difference between a task being impossible and it being done.
◆ Capability unlock — impossible becomes done
Token comparison — Test 01
Without UB — task failed30 tokens
With UB — real live data5,228 tokens
VerdictImpossible → done
Test 02 — Mathematical Accuracy
Cheaper. And every answer is provably correct.
Claude was asked for the largest prime gap under 10,000. Answered confidently. Was 44% wrong. The same task through UB ran Python, got every answer correct, and used 30% fewer tokens in the process.
Confident wrong answers in enterprise data analysis are not an inconvenience. They are a liability.
◆ 30% cheaper · 100% accurate · auditable
Token comparison — Test 02
Without UB — 2 wrong answers773 tokens
With UB — all correct540 tokens
Token saving30%
Test 03 — Log Analysis · The Proof
96.5% fewer tokens. The wrong answer cost 28× more.
4,024 tokens sent for log analysis. Claude returned 37 errors. UB ran Python: 141 tokens, 41 errors — correct. At 10,000 queries/day this one test is $42,519 saved per year. Enterprise tier costs $5,988/year. That is a 610% ROI.
◆ 96.5% reduction · 610% ROI at enterprise scale
Token comparison — Test 03
Without UB — wrong (37)4,024 tokens
With UB — correct (41)141 tokens
Token reduction96.5%
Who this is built for
Serious AI spend deserves serious infrastructure.
For developers & AI engineers
The execution layer you'd build yourself. Already built.
Code execution, web search, database connectors, LLM routing, session state, parallel threads — it would cost $100,000+ and 6 months to build this. Or one URL and a free account.
Python + Bash, 60s sync, async jobs with no timeout
8 parallel threads per session, session state persistence
LLM routing via OpenRouter — 100+ models, one key
Works with any MCP client. No platform lock-in.
For enterprises & AI-forward startups
Your AI spend becomes a cost centre that justifies itself.
When every AI query costs money, accuracy and token efficiency are not product features. They are business requirements. UniversalBench makes the numbers work — automatically.
96.5% token reduction — ROI visible from month one
Provably accurate answers — audit-ready execution logs
Usage dashboard showing savings in real dollars monthly
No vendor lock-in — switch LLM providers without re-integrating
Pricing
Start free. Scale without surprises.
🛡️
Zero-risk guarantee: sign up, use it, and if you don't see measurable ROI — we refund you. No questions, no forms. That's how confident we are in the numbers.
Free
$0
no credit card required
Enough to run a real benchmark against your current workflow.
50 executions/month
Web search
Code & Bash
LLM routing
Database
Pay as you go
$0.008
per execution · credits roll over
For variable workloads. Only pay for what runs.
No monthly minimum
All core tools
Unused credits refunded
Pro-rata billing
Database
MOST POPULAR
Starter
$19
per month
You will save more in tokens in the first week than this costs.
2,000 executions/month
Everything in Free
Database connector
Parallel execution
Email support
Pro
$49
per month
Unlimited execution for teams and production AI pipelines.
Unlimited executions
Everything in Starter
GitHub connector
Secrets vault
Usage ROI dashboard
Get your free API key.
50 executions/month. No credit card. Under 2 minutes to connect.