Agent Leaderboard
Free community leaderboard ranking LLMs on real-world agentic tasks
The quickest route to a free, community-vetted sanity check on which LLMs actually execute agentic tasks. It won't replace a bespoke eval pipeline, but for comparing base models on planning and tool use, it's the most useful zero-cost starting point we've seen.
Verified 8d ago · liveness 70/100 · cite: rightaichoice.com/tools/agent-leaderboard
- ML researchers comparing agent-capable LLMs on planning and tool use
- Developers choosing a base model for an agent framework
- Enterprise teams benchmarking models for automation workflows
- Hobbyists exploring open-source agent models
- Teams needing private or custom evaluation suites
- Beginners unfamiliar with agent benchmarks and terminology
- Users who need cost, latency, or throughput metrics
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- Real pros & cons from real users
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Skip the Agent Leaderboard if you need private, custom evaluation suites or an API to integrate benchmarking into your own pipeline, or if you're looking for cost or latency comparisons.
Free for everyone, with no login required to browse. Unlike paid eval services such as Artificial Analysis or private benchmarking platforms, the Agent Leaderboard costs nothing—ideal for individual developers, researchers, and startups on a budget. Larger teams may eventually outgrow it and need paid solutions with custom evals.
In short
Agent Leaderboard — Free community leaderboard ranking LLMs on real-world agentic tasks. Best for ML researchers comparing agent-capable LLMs on planning and tool use, Developers choosing a base model for an agent framework, Enterprise teams benchmarking models for automation workflows. Free to use.
What's new in Agent Leaderboard
Checked 6 days agoAcross the latest 5 updates: 5 feature updates.
Granular Feature Access on Hugging Face Hub
Feature access is now controlled per resource group, not org-wide. Jobs open to all, Inference Endpoints admin-only, blog publishing limited to specific group.
Job Filtering by Label
Jobs can now be filtered by label via clickable chips with counts and free-form key=value input. Works on user and org job pages.
MCP Server Enhancements
Hugging Face MCP Server updated: new hf_fs tool provides unified repo/storage/docs/papers access in ~1,000 tokens. Sandboxes enable secure execution environments for repos and buckets.
Egress Metrics for Users and Organizations
Users see egress usage in dashboard; orgs receive per-user egress breakdown. Currently covers traffic via Hugging Face CDN, more to come.
Build Spaces with AI Agents
New Space creation page includes option to build with an AI agent. Copy command into agent to iterate a Space for model, paper, or local folder.
What people actually say about Agent Leaderboard — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
29 mentions across 2 sources (Hacker News, Lemmy) · researched Jul 3, 2026.
- +Free and open access on Hugging Face — no login required.
- +Focuses on agentic capabilities, not just language understanding.
- +Transparent methodology and public evaluation data.
- +Frequently updated with new model evaluations.
- +Supports filtering and per-model task score breakdowns.
- −No support channels or way to ask questions.
- −Limited to pre-run benchmarks — no custom evaluation.
- −Competing leaderboards offer more domain-specific depth.
- −No API for integration into external workflows.
- −Less useful for non-coding agent tasks like customer service.
- • No hidden costs — everything is free. But there's no premium tier for deeper analytics.
Viability Score
How well maintained and how widely used is Agent Leaderboard? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: August 2026
How we score →Key Features
- Public leaderboard ranking LLMs on agentic tasks
- Standardized evaluation benchmarks for agent capabilities
- Per-model breakdown of task scores
- Community voting (likes) for visibility
- Open access — no login required to view
- Hosted on Hugging Face Spaces
- Frequently updated with new model evaluations
- Supports comparison across model families
- Transparent methodology and data
- Results featured on Hugging Face model pages
- Filter models by hardware and share via URL
- Filter by base models only (hide finetunes, adapters)
- Run Spaces built with AI agents on Hugging Face
- Filter Model page by hardware type (GPU, CPU, Apple Silicon)
About Agent Leaderboard
Agent Leaderboard, hosted by Galileo AI on Hugging Face Spaces, is a free public benchmark that ranks large language models on agentic tasks — those measuring planning, tool use, multi-step instruction following, and environment interaction. Unlike traditional NLP leaderboards that focus on static knowledge or reasoning, this one zeroes in on how well models execute autonomous workflows in dynamic settings. It's built for ML researchers validating agent-capable LLMs, developers choosing a base model for an agent framework, and enterprise teams assessing models for automation use cases. The interface is no-frills and open: no login required to browse, filter models, or share a filtered view via URL. You get a per-model breakdown of task scores, see community voting in the form of likes, and can hide finetunes or adapters to compare only base models. The leaderboard updates frequently as new evaluations are added, and results are also surfaced directly on Hugging Face model pages, so you encounter rankings right where you're already checking model cards. Methodology is transparent — data is openly accessible, and the evaluation protocols are standardized. As of mid-2026 it has amassed over 450 likes, a sign of community trust. It's a community-driven alternative to paid benchmarking services like Artificial Analysis, freely available to anyone who wants a quick, credible read on which models handle agentic tasks best. Beyond the leaderboard itself, recent Hugging Face platform updates improve the surrounding ecosystem: Spaces can now be built with AI agents, the Models page supports hardware filtering (GPU, CPU, Apple Silicon), and new fine-grained token presets make access management more precise. These changes don't alter the leaderboard's core, but they make the environment around it more usable.
Behind the Verdict
When you're down to two or three candidate models for an agent project, the Agent Leaderboard is the fastest filter we know. It is free, open, and grounded in tasks that actually resemble what an agent does — planning, tool calls, multi-step execution. You can check a model's score in minutes, no signup, no email. We use it as a first-pass triage before digging into deeper evals. Where it falls short: it's a leaderboard, not a lab. There's no way to run your own custom tasks, no private benchmarking, and no cost or latency metrics. If your decision hinges on price per token or inference speed, this won't answer that. It's a quality-of-execution snapshot, not a full procurement checklist. Compared to a paid service like Artificial Analysis, which benchmarks broader capabilities and adds performance-per-dollar analytics, the Agent Leaderboard is deliberately narrower. It focuses on agentic ability alone, which is both its strength and its limitation. For a purely agent-focused comparison among open-weight models, we'd trust it over a general-purpose leaderboard carrying the same baggage. Real-world caveat: the model-to-task mapping changes as new evaluations roll in, so a rank shift may reflect a re-run, not a model update. Also, community likes can inflate visibility, not necessarily quality. Stick to the base-model filter if you want to avoid finetune noise. In practice, we'd treat it as a strong, current signal — not gospel. It's free, and it's hosted by a vendor (Galileo AI) that also sells evaluation tools, so mind the subtle conflict of interest. The data is transparent enough to audit, but you're seeing results produced under protocols the host controls. For most buyers, that's acceptable — you just shouldn't treat this as an independent third-party
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Real-world workflow fit
Concrete scenarios for the personas Agent Leaderboard actually fits — and what changes day-one when you adopt it.
You're writing a paper on agentic LLMs and need to compare recent models on standardized benchmarks.
Outcome: You visit the leaderboard, filter for relevant task types, and download the per-task scores for all models you're citing, saving hours of manual evaluation.
You're deciding which base LLM to integrate into your open-source agent framework.
Outcome: You quickly scan the leaderboard to shortlist the top performers on tool use and planning, then run your own evals to confirm the fit.
Your team is choosing a model for an internal automation project and needs a high-level comparison.
Outcome: You share the leaderboard link with your team; they see the breakdown by task and make a data-informed shortlist without any cost.
Use Cases
- Compare Gemini 2.5 Pro and Claude 4 Sonnet on tool-use benchmarks before selecting a model for your agent.
- Identify which open-source model (e.g., Llama 4, Qwen 2.5) performs best on multi-step planning tasks.
- Track performance improvements of a specific model over time as new evaluations are added.
- Share leaderboard results with your team to guide model selection for an internal automation project.
- Discover emerging models that excel in agentic tasks even if they are less well-known.
Limitations
- The evidence does not specify the evaluation benchmarks, submission requirements, or limitations of the leaderboard.
- It only indicates that it is a community leaderboard ranking LLMs on agentic tasks, hosted on Hugging Face Spaces, with community voting and a recent update mentioning running Spaces built with AI agents.
- No API documentation or programmatic access details are provided, so the absence of an API cannot be confirmed but also no evidence contradicts the existing claim.
- The platform is primarily web-based, as it is a Hugging Face Space.
as of 2026-08-06
Verification history
We have re-verified Agent Leaderboard 5 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where Agent Leaderboard's pricing actually pencils out — and where peers do it cheaper.
Free for everyone, with no login required to browse. Unlike paid eval services such as Artificial Analysis or private benchmarking platforms, the Agent Leaderboard costs nothing—ideal for individual developers, researchers, and startups on a budget. Larger teams may eventually outgrow it and need paid solutions with custom evals.
Setup time & first value
How long it actually takes to get something useful out of Agent Leaderboard — broken out by persona, not the marketing-page minute.
Setup is instant — no account or login required. You can start browsing the leaderboard and viewing model scores within seconds. For per-model detail or filtering, it's still just a matter of clicks.
Switching to or from Agent Leaderboard
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- ↗To Galileo Evaluate: If you need custom benchmark creation, private rankings, and API access, you can migrate your evaluation workflows to Galileo's own platform, which offers these features for teams.
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with Agent Leaderboard
Common stack mates teams adopt alongside Agent Leaderboard, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Agent Leaderboard vs Presto Voice
If you're a QSR chain seeking to automate drive-thru ordering and boost revenue, Presto Voice is the purpose-built tool with proven ROI. If you're an ML researcher or developer evaluating LLMs for agentic tasks, the Agent Leaderboard provides free, transparent benchmarks. These tools serve entirely different needs—choose based on your domain.
Agent Leaderboard vs Truleo
If you're a law enforcement agency drowning in siloed data, Truleo is the only purpose-built solution—its automated jail call analysis, BWC processing, and report writing cut hours per case. But if you're evaluating LLMs for agentic tasks, Agent Leaderboard is free, open, and up-to-date with the latest benchmarks. These tools serve completely different audiences; choose based on your role, not feature overlap.
Agent Leaderboard vs Praktika
These tools serve completely different needs. Praktika is ideal for language learners wanting AI speaking practice; Agent Leaderboard is for ML professionals benchmarking LLMs on agent tasks. Choose based on your goal: improve fluency or evaluate models.
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