How AI Actually Performs on Real Work
Academic benchmarks measure the wrong things. We test AI on the tasks your business actually needs: document analysis, code generation, customer support, and more.
The Problem with Academic Benchmarks
Most AI leaderboards use benchmarks like MMLU, HellaSwag, and HumanEval. These tests have serious limitations for business decision-making:
Artificial Tasks
Tests like MMLU and HellaSwag use contrived questions that don't reflect real work. High scores don't mean the model works well in production.
Benchmark Gaming
Models are increasingly trained to score well on specific benchmarks, not to be genuinely capable. Leaderboard positions become misleading.
Missing Context
Academic benchmarks test isolated knowledge, not the ability to handle ambiguous, multi-step business workflows with real constraints.
No Cost/Speed Data
Academic benchmarks only measure accuracy. They ignore latency and cost - critical factors for production deployment decisions.
What Makes Our Benchmarks Different
Actual Business Tasks
Tests derived from real work: summarizing contracts, writing code, handling support tickets. If it's not something a business does, we don't test it.
Human Evaluation
AI judges plus human review for quality assessment. We catch the nuances that automated metrics miss.
Speed Matters
We measure response time for every task. A model that takes 30 seconds isn't useful for real-time applications.
Cost Tracking
Every benchmark includes cost per task. Know exactly what you'll pay before you deploy.
Business Task Categories
Code Generation
Writing, reviewing, and debugging code across languages
Customer Support
Handling customer inquiries with accuracy and empathy
Data Analysis
Processing data and generating business insights
Legal Review
Analyzing contracts and legal documents for key issues
Financial Analysis
Processing financial data and generating insights
Content Creation
Creating marketing copy, emails, and business content
SQL & Databases
Query generation and database optimization
Summarization
Summarizing documents and extracting key information
Task Selection
- Tasks derived from real business workflows
- Multiple difficulty levels per category
- Regular updates to prevent overfitting
Evaluation Process
- Blind evaluation by multiple AI judges
- Human review for edge cases
- Cost and latency tracked per request
Scoring System
Accuracy & completeness of responses
Response latency in milliseconds
Price per 1M tokens processed
See the Results
Stay Updated on AI Performance
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