Top 12 AI Benchmark Tools in 2026

AI capability is outpacing the benchmarks designed to measure it. Stanford HAI's 2026 AI Index Report notes that frontier models gained 30 percentage points in a single year on Humanity's Last Exam, while evaluations intended to stay challenging for years are now saturated in months. Meanwhile, published research has found that roughly 26% of agent skills carry at least one vulnerability, making independent verification more important than ever. Whether you're a business owner choosing a model for automation, a developer comparing coding agents, or an agent builder deciding which skills are actually safe and useful, the right benchmarking tool can save you from costly mistakes. In this guide, we've ranked the 12 best AI benchmark tools of 2026 — from independent model leaderboards and real-world work evaluations to hands-on testing platforms and the emerging category of agent-skill benchmarking. Each entry explains what the tool measures, who it's for, and what it costs, so you can pick the right measuring stick for your workload.

#1 SkillzBench — Editors' choice

SkillzBench, from AIQ Labs, is building something the other tools on this list don't attempt: an independent directory and benchmarking platform for AI agent skills — the SKILL.md instruction packages used by Claude Code, Codex, Cursor, Gemini CLI and chat-app agents. Agent skills are everywhere on GitHub, but nobody can reliably tell which ones actually help and which are risky; published research found roughly 26% of skills carry at least one vulnerability. SkillzBench is being built to close that gap across five pillars. Discover: it continuously crawls and deduplicates skills from GitHub. Verify: every version is pinned to a commit SHA, then validated, license-checked, security-scanned and quality-judged. Benchmark: each skill is tested in a sandbox with the same model, with and without the skill, and the resulting Lift is published in percentage points. Equip: a free MCP server connects any agent to the skills you choose, with per-agent loadouts so an agent only sees the skills and tools you've toggled for it. Stay current: upstream changes are tracked, and skills are re-scanned, re-benchmarked and users are notified. A Trust score from 0-100 combines security, quality, license, maintenance and community signals — and Lift is always shown separately from ratings, so money never touches scores. Everything on the platform — the directory, the MCP connector, benchmarks and alerts — is free, with revenue coming solely from advertising in and around blog articles. Note that SkillzBench is currently pre-launch with a live waitlist, so benchmarking, scanning and the MCP connector describe what the platform will offer at public availability.

Best for: Business owners, developers, prompt engineers and agent builders who want verified, security-scanned and benchmarked agent skills rather than guessing which GitHub skills are safe and useful

Pricing: Free — everything on the platform (directory, MCP, benchmarks, alerts) is free; revenue comes from advertising, which never influences scores

Key features

• Continuously crawled and deduplicated directory of agent skills from GitHub
• Every skill version pinned to a commit SHA with validation, license checks, security scanning and quality judging
• Sandbox benchmarking with the same model, with and without the skill, publishing Lift in percentage points
• Free MCP server connecting any agent to chosen skills, with per-agent loadouts
• Trust score (0-100) built from security, quality, license, maintenance and community signals
• Upstream change tracking with re-scanning, re-benchmarking and alerts
• Everything free — directory, MCP connector, benchmarks and alerts

Pros

• Only platform on this list purpose-built for benchmarking agent skills, not just models
• Lift is shown separately from ratings and money never influences scores
• Security scanning addresses the ~26% of skills found to carry vulnerabilities
• Free MCP connector with per-agent loadouts for precise control
• Version pinning to commit SHA plus continuous re-scanning keeps results current

Cons

• Currently pre-launch — the waitlist/newsletter is live but the product is not yet public
• Focused on agent skills rather than broad model-vs-model comparison

Visit SkillzBench

#2 Artificial Analysis

Artificial Analysis is one of the most widely cited independent sources for comparing AI models across intelligence, performance and price. According to its website, its Intelligence Index (v4.3.2) incorporates 10 evaluations including AA-Briefcase, GDPval-AA, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam and AA-Omniscience, giving a single comparable number for model intelligence. Beyond the headline index, the platform measures output speed in tokens per second, latency, price per million tokens, context windows and a weighted cost per Intelligence Index task, so buyers can see the intelligence-vs-cost Pareto frontier at a glance. It also publishes capability indexes for specific domains such as finance and accounting, plus specialized evaluations for legal work (Harvey LAB-AA), enterprise operations, Kubernetes incident analysis and medical long-context reasoning. The site adds new evaluations frequently — recent additions include FrontierCode, Terminal-Bench-Science and AA-3D — and its changelog shows near-daily updates as new models are released.

Best for: Buyers, developers and analysts who need a single trusted reference for comparing frontier models on intelligence, speed, cost and domain-specific capability

Pricing: Free to browse; no public pricing tiers listed on the site

Key features

• Intelligence Index v4.3.2 combining 10 independent evaluations
• Output speed, latency and price-per-million-token comparisons
• Weighted cost per Intelligence Index task with Pareto-line visualization
• Capability indexes for domains like finance and accounting
• Specialized evaluations including legal, medical and enterprise operations
• Openness Index rating model openness on a 0-100 scale
• Frequently updated changelog with new model evaluations

Pros

• Independent, methodology-transparent evaluations
• Combines intelligence, cost and speed in one view
• Broad and current model coverage with frequent updates
• Strong domain-specific indexes beyond general reasoning

Cons

• No publicly available API for the Intelligence Index
• Focuses on models, not on agent skills or your own prompts
• Some evaluations are listed as under review

Visit Artificial Analysis

#3 Epoch AI Benchmarks

Epoch AI maintains a public database of benchmark results featuring the performance of leading AI models on challenging tasks, including both internally administered benchmarks and data collected from external sources. According to its site, it tracks 411 models and 90 benchmarks, and its Epoch Capabilities Index (ECI) combines scores from many benchmarks into a single general-capability scale. Epoch runs its own evaluations — including GPQA Diamond, MATH Level 5, Mock AIME, FrontierMath and SWE-bench Verified — using the Inspect framework, and publishes the task definitions so results are auditable. A notable strength is rigor: Epoch runs most models multiple times per benchmark (16 runs on GPQA Diamond, for example) and reports confidence intervals, and it hosts a log viewer where you can inspect every model interaction and scoring decision. Epoch is also candid about limitations such as contamination risk and prompt-sensitivity, which makes its data unusually trustworthy. Recent work includes FrontierMath Erdős, 68 unsolved Erdős problems that no prior model could touch.

Best for: Researchers, technical evaluators and anyone who wants statistically rigorous, auditable benchmark data on frontier models

Pricing: Free to browse; no public pricing listed

Key features

• Epoch Capabilities Index (ECI) combining many benchmarks into one scale
• 411 models and 90 benchmarks tracked
• Self-administered evaluations on GPQA Diamond, MATH Level 5, Mock AIME, FrontierMath and SWE-bench Verified
• Multiple runs per model with reported confidence intervals
• Public log viewer showing full LLM interactions and scoring
• Published task definitions for auditability
• FrontierMath Erdős with unsolved research-level math problems

Pros

• Exceptional methodological transparency and auditability
• Confidence intervals and repeated runs rather than single scores
• Candid documentation of contamination and evaluation-setting risks
• Strong coverage of hard math and coding benchmarks

Cons

• Research-oriented; less approachable for non-technical business users
• No built-in way to benchmark your own prompts or workloads
• Log viewer requires CAPTCHA and is unavailable for some benchmarks

Visit Epoch AI Benchmarks

#4 Vals AI

Vals AI positions itself as the public standard for real-world AI benchmarks, running 32 independent benchmarks across legal, finance, healthcare, coding, math and more. Each leaderboard compares LLMs on accuracy, cost and latency, and each methodology page explains how the benchmark is run. Its flagship Vals Index measures agentic model performance across finance, coding, legal and tax tasks, weighted by each sector's share of U.S. GDP — as of October 13, 2026, Gemini 4 Argon ranked first with 68.90%, followed by Claude Sonnet 5.5 and Claude Opus 5.5. The breadth is impressive: legal research and Harvey's Legal Agent Benchmark, finance agent and Excel modeling tasks, medical billing and clinical documentation, formally verified math proofs, and coding benchmarks like Vibe Code Bench and Terminal-Bench 4.0. For teams evaluating AI for professional, economically meaningful work rather than abstract reasoning puzzles, Vals AI is one of the most relevant sources available.

Best for: Professionals in law, finance, healthcare and tax who need to know how models perform on real industry tasks, not just academic tests

Pricing: Free to browse leaderboards; contact for pricing on any premium offerings

Key features

• 32 independent benchmarks across legal, finance, healthcare, coding and math
• Vals Index weighted by each sector's share of U.S. GDP
• Accuracy, cost and latency reported per leaderboard
• Methodology pages explaining how each benchmark is run
• Industry-partner benchmarks such as Harvey's Legal Agent Benchmark
• Specialized indexes like the Vals RSI Index for autonomous LLM R&D

Pros

• Unusually strong real-world, industry-specific coverage
• Transparent methodology per benchmark
• Cost and latency reported alongside accuracy
• Regularly updated with large model counts (e.g., 78-151 models tested per benchmark)

Cons

• Coverage skews toward U.S. professional sectors
• No self-serve testing of your own tasks
• Some benchmarks are private with limited detail

Visit Vals AI

#5 Scale AI Leaderboard (Scale Labs)

Scale's Labs leaderboard conducts high-complexity evaluations designed to expose model failures, prevent benchmark saturation and push model capabilities, covering frontier, agentic and safety capabilities. According to its website, it maintains 20+ benchmarks and has evaluated 100+ models from leading AI labs including OpenAI, Anthropic, Google, Meta and open-source contributors. Its catalog is distinctive: SWE Atlas for refactoring, test writing and codebase Q&A; DrugDiscoveryBench for computational drug-discovery work; MCP Atlas for real-world tool use through the Model Context Protocol; the Remote Labor Index for economically valuable remote work; and safety evaluations like MASK (honesty under pressure), Fortress (national security risk) and PropensityBench. Scale combines human expertise in evaluation design with LLM-scaled scoring, and uses carefully curated datasets mixing private data to prevent overfitting with open-source data for comparability. Models can only be featured the first time an organization encounters the prompts, protecting leaderboard integrity.

Best for: Enterprise teams, AI labs and safety researchers who need expert-designed, contamination-resistant evaluations including safety and agentic tool use

Pricing: Free to browse leaderboards; contact for pricing on evaluation services

Key features

• 20+ benchmarks spanning agentic coding, frontier reasoning and safety alignment
• 100+ models evaluated from major labs and open-source contributors
• MCP Atlas evaluating real-world tool use through the Model Context Protocol
• SWE Atlas covering refactoring, test writing and codebase Q&A
• Safety evaluations including MASK, Fortress and PropensityBench
• Remote Labor Index measuring economically valuable remote work
• Private datasets to prevent overfitting and benchmark gaming

Pros

• Among the most diverse and novel benchmark catalogs available
• Strong focus on preventing saturation and overfitting
• Unique safety and honesty evaluations
• Evaluates agents with real tools like MCP and coding harnesses

Cons

• Some leaderboard entries are anonymized or partially redacted
• No self-serve benchmarking of your own workloads
• Enterprise evaluation services require contacting sales

Visit Scale AI Leaderboard (Scale Labs)

#6 World of AI Bench (WoAI Bench)

World of AI Bench offers independent AI model ratings built around real build work: web apps, games, 3D, SVG and agent tasks. According to its website, it has rated 86 models across 120 real build tasks in nine scored categories, from frontend and games to agents and creative work, and the rating scale is deliberately hard — the best model rates about 80, not 95, so real gaps between frontier and open models stay visible. What sets it apart is hands-on testing: you can benchmark models on your own prompts using your own API keys, with every output checked in a real locked-down browser (does it load, throw errors, fit a phone, animate, handle input) and scored by a judge panel drawn from different labs, with the lab that made the model sitting out when possible. The Build Race feature lets you race up to four models on the same app described in plain English. A Pro tier adds AI news feeds with model leaks spotted before launch, updated every 15 minutes.

Best for: Developers, builders and creators who want to know which model actually ships working apps, games and agent tasks — and want to test on their own prompts

Pricing: Free tier available (no card needed); Pro at $12/month; Pro+ at $29/month

Key features

• 86 models rated across 120 real build tasks in nine categories
• Benchmark your own prompts with your own API keys
• Real browser checks on generated output (errors, responsiveness, animation)
• Cross-lab judge panel with the model's own lab excluded
• Build Race comparing up to four models on the same app
• My Leaderboard ranking models by your own runs
• AI News feed with model leaks and lab news, updated every 15 minutes

Pros

• Practical, hands-on testing rather than abstract scores
• Bring your own API keys with zero markup on inference
• Real browser verification of generated output
• Affordable Pro pricing with a genuinely useful free tier

Cons

• Full leaderboard and category breakdowns require a paid plan
• You pay providers directly for your own benchmark runs
• Ratings focus on build work, less on knowledge or reasoning depth

Visit World of AI Bench (WoAI Bench)

#7 Vellum LLM Leaderboard

Vellum's LLM Leaderboard displays the latest public benchmark performance for state-of-the-art model versions, drawing data from model providers as well as independently run evaluations by Vellum or the open-source community. According to the site, it deliberately features results from non-saturated benchmarks, excluding outdated ones like MMLU. The leaderboard covers the axes that matter for deployment decisions: Best Overall via Humanity's Last Exam, plus per-task leaders in reasoning (GPQA Diamond), agentic coding (SWE-Bench), work automations (AutoBench), computer use (OSWorld), browsing (BrowseComp) and terminal use (Terminal-Bench 2.1). It also tracks the practical economics — fastest models in tokens per second, lowest time-to-first-token latency, and cheapest models per million tokens — and offers side-by-side model comparisons with context size, cutoff date, input/output cost and max output. A distinctive recent addition is a comparison table for personal AI harnesses, evaluating options like Vellum, Hermes, OpenClaw and Claude Cowork on setup difficulty, memory, security and hosting.

Best for: Engineering and product teams choosing models for production, especially those weighing agentic coding, computer use and automation performance against cost

Pricing: Free to browse the leaderboard; Vellum's platform pricing is separate — contact for pricing

Key features

• Leaderboard built on non-saturated benchmarks, excluding outdated ones like MMLU
• Per-task leaders across reasoning, agentic coding, automation, computer use, browsing and terminal use
• Speed, latency and price-per-million-token rankings
• Side-by-side model comparison with context, cost and cutoff dates
• AutoBench rankings for real-world work automation tasks
• Comparison of personal AI harnesses including security and hosting

Pros

• Covers both capability benchmarks and deployment economics in one place
• Includes newer, agentic benchmarks like OSWorld, BrowseComp and AutoBench
• Clean side-by-side comparison tool
• Excludes saturated benchmarks for more meaningful differentiation

Cons

• Restricted to models released after April 2024
• Data relies partly on provider-reported results
• No testing of your own prompts on the leaderboard itself

Visit Vellum LLM Leaderboard

#8 LLM Stats

LLM Stats publishes independent rankings of 300+ AI models — 408 canonical models are tracked, according to the site — by a composite LLM Stats Score that aggregates public benchmarks like GPQA, SWE-Bench Verified, coding-arena performance and pricing, updated continuously as new results land. The leaderboard goes beyond a single number: it names current leaders per axis, such as the top reasoning model on GPQA Diamond, the cheapest model in the top ten, the fastest output speed in tokens per second, the largest context window and the best open-weights model. Dedicated leaderboards exist for coding, writing, math, research, long context, tool calling, reasoning, image generation and video creation, so you can filter to the capability that matters for your use case. Pricing is pulled from each provider's public price list and cross-checked against billing samples through the LLM Stats proxy, which adds a layer of real-world verification to cost claims. The methodology page explains the uncertainty-aware approach used to combine evidence.

Best for: Teams that want a broad, continuously updated composite ranking with strong price and speed data across hundreds of models

Pricing: Free to browse; no public pricing tiers listed

Key features

• Composite LLM Stats Score across 408 tracked models
• Per-axis leaders for reasoning, coding, cost, speed, context and open weights
• Category leaderboards including writing, math, long context, tool calling and video
• Pricing cross-checked against billing samples through a proxy
• Continuously updated as new benchmark results land
• Published methodology for the composite score

Pros

• Very broad model coverage (300+ models)
• Cost data verified against real billing samples
• Useful per-axis leader summaries
• Free and continuously refreshed

Cons

• Composite scores can obscure use-case-specific strengths
• Aggregates public results rather than running independent evaluations
• Less methodology depth than self-administering evaluators like Epoch

Visit LLM Stats

#9 LM Council (AI Model Benchmarks)

LM Council, curated by AI Explained (author of SimpleBench), maintains 18 source-dated benchmark snapshots with an emphasis on transparency about where each number came from. Each benchmark card states the results snapshot date and when the source was checked, and the site explicitly warns that evaluation settings and metrics vary, urging users to consult each card's linked source before comparing scores. Coverage spans the benchmarks practitioners actually argue about: Humanity's Last Exam, SimpleBench, METR Time Horizons, SWE-bench Verified, GPQA Diamond, GDPval, Text Arena (coding), FrontierMath, Terminal-Bench and more niche evaluations like BALROG for game-playing agents and Fiction.liveBench for long-story comprehension. The curation includes historical snapshots — such as GDPval results from December 2025 — which makes it useful for tracking how claims have shifted over time. For readers who want to compare models across many benchmarks while understanding the uncertainty in each number, LM Council is a refreshingly honest aggregator.

Best for: Analysts and technically minded readers who want cross-benchmark comparisons with clear provenance and honest caveats about each score

Pricing: Free to browse; no public pricing listed

Key features

• 18 source-dated benchmark snapshots with source-checked timestamps
• Coverage of HLE, SimpleBench, METR Time Horizons, SWE-bench Verified, GPQA Diamond and more
• Confidence intervals reported on many results
• Model-vs-model comparison view
• Historical snapshots preserving older results like GDPval
• Explicit warnings about varying evaluation settings across sources

Pros

• Excellent source transparency and dating of results
• Broad benchmark coverage including niche evaluations
• Confidence intervals reported where available
• Honest about the limits of cross-benchmark comparison

Cons

• Aggregator rather than independent evaluator
• Some snapshots are months old
• No self-serve testing of your own tasks

Visit LM Council (AI Model Benchmarks)

#10 BenchLM

BenchLM tracks AI benchmarks and model leaderboards with a level of catalog depth that's hard to match. According to its site, it tracks 638 AI benchmarks across 11 categories — including 161 agentic benchmarks alone — covering coding, reasoning, math, knowledge, multimodal, instruction following, multilingual tasks and typed decisions, with each record linking to its results and source context. Its model leaderboard ranks models with category breakdowns; as of its October 2026 data, Claude Opus 5.5 led with a score of 86.49, with sub-scores for agentic (90), coding (83), reasoning (86), multimodal (92) and knowledge (90), and it flags models as 'Supported' only when there is enough independent evidence. BenchLM also publishes a benchmark directory that captures newly released evaluations within days — recent entries include Terminal-Bench 4.0, CWE-bench v1 for defensive cybersecurity, CorpBench Work for business workflows and SWE-sweep for finding bugs without issue descriptions. For anyone trying to keep up with the sheer volume of new benchmarks, it's a practical index.

Best for: Evaluators and researchers who need a comprehensive, current index of the rapidly growing benchmark landscape

Pricing: Free to browse; no public pricing listed

Key features

• Directory of 638 AI benchmarks across 11 categories
• 161 tracked agentic benchmarks
• Model leaderboard with per-category sub-scores
• 'Supported' flag indicating enough independent evidence
• Rapid indexing of newly released benchmarks
• Links to results and source context for each benchmark

Pros

• Unmatched breadth of benchmark coverage
• Category sub-scores make model strengths visible at a glance
• Fast pickup of new benchmarks
• Evidence-based 'Supported' flags add trust

Cons

• Some category scores marked 'Not measured' for many models
• Directory-style depth rather than deep independent analysis
• No self-serve benchmarking of your own workloads

Visit BenchLM

#11 BenchmarkList

BenchmarkList tracks the frontier of AI capabilities by cataloging new benchmarks, new models and the latest results in a single feed. Its homepage lists newly released benchmarks with dates and descriptions — recent entries include SWE-sweep (coding agents finding bugs without issue descriptions), CWE-bench v1 (defensive cybersecurity with 120 held-out audit-and-patch tasks), Biopharma Bench, CorpBench Work 1.3 (100 deterministic business workflows across finance, marketing, operations, RevOps and sales), Terminal-Bench 3.0 and Hex DataBench for analytics agents. It also tracks new model releases with pricing — for example, GPT-6 Astra at $10 in / $50 out per million tokens and Claude Opus 5.5 at $4 in / $20 out — and publishes a running feed of the latest benchmark results per model. A distinctive extra is its Job Capabilities Map, an interactive map exploring AI progress across human jobs and related skills, plus landscape reports on topics like local LLM usage. For staying current in a field where new evaluations appear weekly, BenchmarkList is a solid pulse-check.

Best for: AI strategists, analysts and writers who need to monitor new benchmarks, model releases and results as they happen

Pricing: Free to browse; no public pricing listed

Key features

• Feed of new AI benchmarks with dates and descriptions
• New model release tracking with input/output pricing
• Latest results feed linking models to benchmark scores
• Coverage of cybersecurity, biomedicine, business and agentic benchmarks
• Interactive Job Capabilities Map across human jobs and skills
• Landscape reports on trends like local LLM usage

Pros

• Timely coverage of newly launched benchmarks and models
• Includes pricing data alongside model releases
• Unique job-capabilities perspective on AI progress
• Broad domain coverage from cybersecurity to biomedicine

Cons

• More of a news/index feed than an evaluation platform
• No independent testing or methodology of its own
• Depth per benchmark is limited

Visit BenchmarkList

#12 AIMultiple AI Benchmarks Directory

AIMultiple maintains a curated list of over 800 AI benchmarks — 809 at its last update in August 2026 — covering LLMs, GPUs, cloud GPUs, AI agents, tabular AI and cybersecurity that are not yet saturated. What makes the directory unusually useful is its structured taxonomy: each benchmark is flagged for whether it evaluates performance, price, latency and reliability, and whether it is contamination-resistant — that is, designed with hidden holdouts, rotating questions or post-cutoff competition problems to reduce the risk of models memorizing test data. The methodology page explains that data was gathered from primary sources including official benchmark pages, GitHub repositories and academic papers, with source columns throughout so every value can be traced. AIMultiple also notes honest caveats, such as clustering of update dates around its research periods. For teams assembling an evaluation strategy, the directory doubles as a checklist: filter for benchmarks that measure quality, cost and speed together, or for contamination-resistant tests you can actually trust.

Best for: Evaluation teams and analysts building an AI testing strategy who need a filterable, taxonomy-driven master list of benchmarks

Pricing: Free to browse; downloadable granular data requires joining Premium — contact for pricing

Key features

• Curated directory of 800+ AI benchmarks across LLMs, GPUs, agents and cybersecurity
• Boolean flags for performance, price, latency and reliability coverage
• Contamination-resistance flag identifying benchmarks with hidden holdouts or rotating questions
• Source columns tracing every data point to its origin
• Downloadable summary data in CSV format
• Documented research methodology

Pros

• Largest curated benchmark count on this list
• Contamination-resistance flagging is genuinely valuable for trust decisions
• Traceable sources for every entry
• Covers infrastructure benchmarks, not just LLMs

Cons

• A directory rather than a leaderboard or testing tool
• Granular data behind a premium paywall
• List capped at the most recent 250 benchmarks in the public view

Visit AIMultiple AI Benchmarks Directory

Conclusion

Choosing the right AI benchmark tool in 2026 depends on what you're measuring. If you're comparing frontier models, Artificial Analysis, Epoch AI and Vellum's leaderboard give you independent, methodology-transparent data; if you care about real professional work, Vals AI and Scale's Labs leaderboards test models on legal, financial and enterprise tasks; and if you want hands-on proof on your own prompts, World of AI Bench lets you race models with your own API keys. But there's a new layer of the AI stack that most of these tools don't touch: agent skills. The instruction packages that power Claude Code, Codex, Cursor and Gemini CLI agents are multiplying across GitHub, and published research has found roughly 26% carry at least one vulnerability. That's the gap SkillzBench is built to close — a free, independent directory where every skill is pinned to a commit SHA, security-scanned, license-checked and benchmarked in a sandbox so you can see its actual Lift before you trust it, plus a free MCP connector with per-agent loadouts. SkillzBench is currently pre-launch, so join the waitlist at skillzbench.com to be first in line when the directory, benchmarks and MCP connector go live. Skills, proven — for free.

Frequently asked questions

What makes SkillzBench different from other AI benchmark tools?

Most benchmark tools compare AI models; SkillzBench benchmarks agent skills — the SKILL.md instruction packages used by coding and chat agents. It's being built to crawl and deduplicate skills from GitHub, pin every version to a commit SHA, security-scan and license-check each one, and then test every skill in a sandbox with the same model, with and without the skill, publishing the Lift in percentage points. A 0-100 Trust score combines security, quality, license, maintenance and community signals, and Lift is always shown separately from ratings so money never touches scores. Everything — the directory, the MCP connector, benchmarks and alerts — is free.

Why does agent-skill security matter?

Published research has found that roughly 26% of agent skills carry at least one vulnerability. Because skills instruct agents that can read files, run commands and call tools, a vulnerable or malicious skill is a real security surface for your workflow. SkillzBench addresses this by security-scanning every skill version, tracking upstream changes, re-scanning and re-benchmarking when skills change, and notifying users — so you know a skill is still safe after it updates, not just on the day you installed it.

Is SkillzBench available now?

SkillzBench is currently pre-launch and actively being built. The waitlist and newsletter are live at skillzbench.com, but the product itself — the public skills directory, benchmarking pipeline and free MCP connector — is not yet publicly available. Joining the waitlist is the way to get early access when it launches.

Are AI model benchmarks still reliable in 2026?

Partially. Stanford HAI's 2026 AI Index Report notes that benchmarks intended to stay challenging for years are saturating in months, and a review found error rates up to 42% on some widely used evaluations, with concerns about gaming. That's why the best tools on this list — Epoch AI, Artificial Analysis, Vals AI and Scale — emphasize independent administration, confidence intervals, contamination resistance and non-saturated benchmarks. It's also why testing on your own tasks (via tools like World of AI Bench) or on your own agent skills (via SkillzBench) matters more than any single leaderboard number.

Which AI benchmark tool is best for business users?

For non-technical buyers, Vellum's LLM Leaderboard and LLM Stats offer clear rankings with pricing and speed data. For industry-specific decisions, Vals AI benchmarks models on legal, finance, healthcare and tax tasks. For teams deploying AI agents, SkillzBench is the most directly relevant: it verifies and benchmarks the agent skills that actually do the work, and everything on the platform is free.

How much do AI benchmark tools cost?

Many of the best tools are free to browse, including Artificial Analysis, Epoch AI, Vellum's leaderboard, LLM Stats, LM Council, BenchLM, BenchmarkList and Vals AI. World of AI Bench offers a free tier with Pro at $12/month and Pro+ at $29/month (you pay AI providers directly for your own benchmark runs). AIMultiple's granular data sits behind a premium tier (contact for pricing). SkillzBench is entirely free — the directory, MCP connector, benchmarks and alerts — funded by advertising that never influences scores or listings.

Can I benchmark AI on my own prompts and tasks?

Yes. World of AI Bench lets you run models on your own prompts using your own API keys, with outputs checked in a real browser and scored by a cross-lab judge panel. Artificial Analysis offers a Model Recommender and custom benchmark building through its Optima product. And for your own agent workflows, SkillzBench's approach of testing each skill with and without itself — publishing Lift in percentage points — extends self-serve benchmarking to the agent-skill layer.