AI models are evaluated on dozens of benchmarks. MMLU tests broad knowledge, HumanEval measures code generation, GSM8K evaluates grade-school math, ARC-AGI probes abstract reasoning. Each benchmark has its own methodology, scoring system, and leaderboard. Understanding which benchmark measures what—and which models perform best on it—requires navigating multiple research papers, GitHub repos, and evaluation platforms.
NeoSignal Benchmarks Browser with category filters and benchmark cards
NeoSignal Benchmarks Browser organizes evaluation datasets into a navigable catalog. The screenshot shows 35 benchmarks from Epoch AI, filtered by category: All, Reasoning, Math, Coding, Agents, Language, and Specialized. Benchmark cards display the name, category badge, description, source indicator (Frontier, etc.), and difficulty score. FrontierMath-Tier-4 measures research-level math, Terminal Bench evaluates agent capabilities, DeepResearch Bench tests multi-document synthesis. Click any card to see the full leaderboard. The integrated chat helps you understand which benchmark best measures a specific capability.
The benefit: benchmark discovery without research papers. Filter by what you want to measure, find the relevant benchmarks, explore leaderboards, understand which models lead.
Detailed Walkthrough
The Benchmark Discovery Problem
AI benchmarks proliferate faster than anyone can track:
New Benchmarks Monthly: Researchers continuously create new evaluation datasets. FrontierMath appeared recently for research-level math. The Agent Company tests workplace automation. Cybench measures security analysis.
Scattered Information: Each benchmark lives in its own repo, paper, or website. Finding "which benchmarks measure reasoning" requires reading multiple papers.
Unclear Difficulty: MMLU and GPQA both test knowledge—but at vastly different difficulty levels. Understanding relative difficulty requires expertise.
Leaderboard Fragmentation: Benchmark results appear on Papers with Code, HuggingFace, individual project sites, and research papers. No single view shows which models lead on which benchmarks.
NeoSignal Benchmarks Browser aggregates benchmark metadata from Epoch AI's comprehensive evaluation database, organizing it for efficient discovery.
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Browser Interface
The Benchmarks Browser provides a catalog-style interface:
Page Header
- "Benchmarks" title with graduation cap icon
- "AI model evaluation datasets from Epoch AI" subtitle
- Search bar for filtering by name or description
- Result count display
Category Navigation Horizontal filter bar with category pills:
- All (35 benchmarks)
- Reasoning (4)
- Math (5)
- Coding (3)
- Agents (4)
- Language (10)
- Specialized (9)
Each pill shows the count of benchmarks in that category. Click to filter.
View Toggle Switch between grid view (cards) and list view (compact rows).
Benchmark Grid Cards displaying benchmark information with click-through to detail pages.
Attribution Epoch AI attribution at the bottom acknowledging the data source.
Category System
Benchmarks are organized into six categories matching common evaluation needs:
Reasoning Benchmarks testing multi-step logical reasoning:
- GPQA_diamond: Graduate-level science questions
- BBH: Big-Bench Hard with 23 challenging tasks
- ARC AI2: Science questions requiring reasoning
- SimpleBench: Fundamental reasoning tasks
Mathematics Benchmarks testing mathematical capability:
- MATH level 5: Hardest problems from MATH dataset
- GSM8K: Grade-school math word problems
- OTIS Mock AIME: Competition-level math
- FrontierMath: Research-level mathematics
- FrontierMath-Tier-4: Hardest FrontierMath problems
Coding Benchmarks testing code generation and understanding:
- SWE-Bench Verified: Real GitHub issues
- Aider polyglot: Multi-language code editing
- CadEval: CAD and technical code
Agents Benchmarks testing autonomous agent capabilities:
- The Agent Company: Workplace automation tasks
- OSWorld: Operating system interaction
- Terminal Bench: Command-line tasks
- Cybench: Security CTF challenges
Language Benchmarks testing language understanding:
- LAMBADA: Broad context understanding
- TriviaQA: Knowledge questions
- MMLU: 57-subject multitask understanding
- HellaSwag: Commonsense NLI
- Winogrande: Coreference resolution
- And more...
Specialized Benchmarks testing specific capabilities:
- ARC-AGI: Abstract visual reasoning
- DeepResearch Bench: Multi-document synthesis
- VideoMME: Video understanding
- VPCT: Visual perception
- Balrog: Logic games
- And more...
Benchmark Cards
Each benchmark displays as a card with key information:
Card Structure
- Benchmark name (prominent heading)
- Category badge (color-coded pill)
- Description (brief explanation of what's measured)
- Source indicator (Frontier, External, etc.)
- Difficulty score (based on EDI)
Visual Design Cards use dark surface backgrounds with category-specific accent colors. Hover reveals a subtle highlight effect. Click anywhere on the card to open the detail page.
Difficulty Indicators The EDI (Estimated Difficulty Index) appears as a visual meter. Higher EDI means the benchmark is harder—fewer models achieve high scores.
Search Functionality
The search bar filters benchmarks by text matching:
Search Targets
- Benchmark name
- Description text
Real-Time Filtering Results update as you type. No need to press enter.
Search Examples
- "math" - shows MATH level 5, GSM8K, FrontierMath, etc.
- "agent" - shows The Agent Company, OSWorld, Terminal Bench
- "reasoning" - shows GPQA, BBH, ARC, SimpleBench
- "code" - shows SWE-Bench, Aider, CadEval
Combined Filtering Search works with category filters. Select "Agents" then search "terminal" to find Terminal Bench specifically.
Difficulty Metrics
Each benchmark includes metrics that help understand its difficulty:
EDI (Estimated Difficulty Index) A normalized difficulty score. Higher values mean harder benchmarks:
- Below 100: Easier benchmarks (older, well-saturated)
- 100-130: Moderate difficulty
- 130-150: Hard
- Above 150: Frontier difficulty (few models score well)
Slope How fast models improve on this benchmark:
- High slope: Rapid improvement, benchmark may saturate soon
- Low slope: Progress is slow, benchmark remains challenging
For example:
- Winogrande: EDI 109, Slope 1.0 (anchor benchmark)
- FrontierMath-Tier-4: EDI 165, Slope 3.5 (very hard, improving)
- GSO-Bench: EDI 165, Slope 2.8 (frontier difficulty)
Source Indicators
Benchmarks have different data sources:
Frontier Benchmarks tracked in Epoch AI's frontier model evaluation. These typically measure the most challenging capabilities.
External Benchmarks from external sources integrated into Epoch AI's database. Includes established benchmarks like MMLU and HumanEval.
Click the source link on the benchmark detail page to access the original data.
Grid vs List Views
Toggle between two display modes:
Grid View
- Cards arranged in responsive grid
- Full card display with description
- Better for browsing and discovery
- Default view
List View
- Compact rows with key info
- Name, category, description truncated
- Better for scanning many benchmarks
- Efficient for experienced users
Chat Integration
The Benchmarks Browser integrates with NeoSignal AI Chat:
Contextual Questions The chat understands you're browsing benchmarks. Ask:
- "Which benchmark best measures reasoning ability?"
- "What's the difference between MMLU and MMLU-Pro?"
- "Which benchmarks test agent capabilities?"
Smart Responses The chat draws from NeoSignal's knowledge base to explain:
- Benchmark methodologies
- Differences between similar benchmarks
- Which models perform best on specific benchmarks
- Historical context of benchmark creation
Example Interaction In the screenshot, the chat responds to "Which benchmark best measures reasoning ability?" by explaining that MMLU-Pro appears to be the most comprehensive, with additional context about multi-step logical reasoning and which benchmarks are specifically rated.
Benchmark Detail Pages
Click any benchmark card to access the full detail page (covered in the Benchmark Leaderboard blog post):
What You'll Find
- Full description and methodology
- EDI and slope metrics
- Complete leaderboard with model rankings
- Score and stderr for each model
- Links to source data
Real-World Usage Patterns
Capability Exploration: You want to understand how models are evaluated on coding. Click "Coding" filter, see SWE-Bench Verified, Aider polyglot, and CadEval. Click through to see which models lead.
Benchmark Selection: You're building an evaluation suite for your use case. Browse categories relevant to your application, note benchmark names and methodologies, select ones that match your requirements.
Model Scouting: A new model was announced with impressive benchmark results. Search for those specific benchmarks to understand what they measure and see the full leaderboards.
Research Context: You're reading a paper that mentions a benchmark. Search for it in the browser to understand its category, difficulty, and current state-of-the-art.
Agent Evaluation: Your team is building an AI agent. Filter to "Agents" category to discover The Agent Company, OSWorld, Terminal Bench, and Cybench. Understand what each measures to choose relevant evaluations.
Data Attribution
All benchmark data comes from Epoch AI:
Epoch AI Attribution Clear attribution block at the bottom of the page acknowledging Epoch AI as the data source.
Source Links Each benchmark links to epoch.ai/benchmarks for original data access.
License Data is provided under CC-BY 4.0 license.
Mobile Experience
The Benchmarks Browser adapts to mobile devices:
Responsive Grid Cards reflow from multi-column on desktop to single-column on mobile.
Sticky Category Bar Category filters remain accessible while scrolling.
Touch Targets All interactive elements meet minimum touch sizes.
Search Accessibility Search bar appears prominently at the top for easy access.
Future Benchmark Coverage
The browser currently includes 35 benchmarks across 6 categories. As Epoch AI adds new benchmarks to their evaluation suite, NeoSignal automatically includes them:
Automatic Updates New benchmarks appear in the browser without manual intervention.
Category Assignment Benchmarks are categorized based on their evaluation focus.
Leaderboard Tracking New benchmark results populate leaderboards as models are evaluated.
From Browser to Understanding
NeoSignal Benchmarks Browser transforms benchmark discovery from a research task into a browsing experience. Instead of reading papers to understand what benchmarks exist and what they measure, you filter by category, search by name, and click through to leaderboards.
The category system organizes benchmarks by what they measure. The difficulty metrics tell you how hard they are. The leaderboards show which models lead. Together, they make the benchmark landscape navigable.
That's the NeoSignal approach: aggregate benchmark metadata, organize it logically, connect it to leaderboards. You focus on understanding model capabilities; the browser handles the discovery.