Agent Leaderboard
Free public leaderboard ranking LLMs on real-world agentic tasks like planning and tool use
Start here when you need a free, no-signup read on which LLMs survive agentic tasks, then go read the model cards. It is a shortlist generator, not a decision — no latency, cost, or throughput numbers, and the scoring methodology isn't spelled out in the Space. Anything production-critical still needs your own evals before you commit engineering time.
Verified 55m 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 building a shortlist before running their own evals
- Hobbyists exploring open-source agent models at zero cost
- Teams that need private or custom evaluation suites on their own data
- Anyone who needs cost, latency, or throughput metrics
- Production deployment decisions — this is an evaluation resource only
We scan live Reddit threads, YouTube comments, X posts, G2 reviews and other communities — and hand you an honest verdict in under a minute.
- Honest verdict, not marketing
- Real pros & cons from real users
- Attributed quotes with receipts
3 free scans · no card needed
Skip Agent Leaderboard if you need cost, latency, or throughput metrics, private custom evaluation suites, or production-grade benchmarking evidence — it ranks agentic task performance only.
Nothing is charged by this Space, but Hugging Face hosting for your own Spaces and Inference Endpoints sits on separate paid infrastructure if you build on the same platform.
Agent Leaderboard itself is free with no tiers, so pricing isn't the constraint — your time is. It replaces the entry-level look at paid benchmarking services like Artificial Analysis at zero cost, which suits solo developers and small teams. Larger organizations still typically pay for bespoke evaluation or a dedicated benchmarking vendor once a model is under serious consideration.
In short
Agent Leaderboard — Free public leaderboard ranking LLMs on real-world agentic tasks like planning and tool use. Best for ML researchers comparing agent-capable LLMs on planning and tool use, Developers choosing a base model for an agent framework, Enterprise teams building a shortlist before running their own evals. Free to use.
What's new in Agent Leaderboard
Checked todayAcross the latest 5 updates: 5 feature updates.
Granular Feature Access
Feature access can now be controlled per resource group instead of across the whole organization, so Jobs can stay open to everyone while Inference Endpoints are restricted to admins.
Filter Jobs by Label
Jobs can now be filtered by label, with most-used labels shown as clickable chips and a free-form key=value input for any label, on both user and organization jobs pages.
MCP Server Enhancements
The Hugging Face MCP Server gained a unified hf_fs tool covering repositories, storage, documentation, and papers in just over 1,000 tokens, plus Sandboxes for secure agent execution attached to buckets and repositories.
Egress metrics for users and organizations
Users can see their egress usage in the dashboard, and organizations get a per-user egress breakdown showing how much data each member consumes via the Hugging Face CDN.
Build Spaces with AI Agents
The new Space creation page includes an option to build with an AI agent, letting you copy a generated command into your agent to build and iterate on a Space for a 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.
Average across the 2 sources that answered — each source counts once, not each post.
- +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: October 2026
How we score →Key Features
- Public leaderboard ranking LLMs on agentic tasks such as planning and tool use
- Standardized evaluation benchmarks for autonomous agent capabilities
- Per-model breakdown of scores across individual task types
- Filter to hide finetunes and adapters and compare base models only
- Browse the full rankings with no login or account required
- Hosted as a Hugging Face Docker Space with running-app access
- Results surfaced on Hugging Face model pages alongside model cards
- Community likes as a popularity signal on leaderboard entries
- Frequently refreshed as new model evaluations are added
- Compare scores across model families and providers
- Shareable leaderboard URL for citing results to a team
- Open access to the evaluated model list and published scores
- Space creation page now supports building a Space with an AI agent
- Hugging Face MCP Server with a unified hf_fs tool for repositories, storage, documentation and papers
About Agent Leaderboard
Agent Leaderboard is a free, publicly accessible Hugging Face Space from Galileo AI that ranks large language models on agentic capabilities rather than on static text benchmarks. Models are scored while executing multi-step workflows: planning, tool use, instruction following, and interaction with dynamic environments. If you are picking a base model for an agent framework, or sizing up which LLMs can actually carry a workflow to completion, this is a practical first stop. The Space sits inside Hugging Face, which matters more than it sounds. There is no account wall between you and the rankings, and results are also surfaced on Hugging Face model pages, so the scores show up where you already read model cards. A filter lets you hide finetunes and adapters and compare base models cleanly, and per-model task breakdowns show where a model gains or loses points instead of collapsing everything into one number. Community likes add a rough popularity signal on top of the scores. Because the space lists the models it evaluates and their scores in the open, it works as a zero-cost sanity check before you pay for a deeper custom evaluation. Treat it as a shortlist generator: it tells you which models are worth testing, not whether a model will hold up against your own prompts, schemas, or failure modes. It is an evaluation resource, not production monitoring — you will not find cost, latency, or throughput data here. Its closest peer is Artificial Analysis, which bundles operational metrics but does not give you agentic task scores for free. Agent Leaderboard competes on price and openness, and gives up depth: methodology details, model selection criteria, and submission rules are thin in the Space itself.
Behind the Verdict
We reach for Agent Leaderboard at one specific moment: the beginning of a model selection cycle for an agent project, when the candidate list is still too long and running your own eval harness on ten models is premature. Twenty minutes there beats a week of internal argument, provided you treat the output as a ranked shortlist and nothing more. Where it bites is the gap between ranking and reality. Your agent's failure modes are not the benchmark's failure modes. A model that tops the planning scores can still choke on your tool schemas or your retrieval layer, and the Space won't tell you that. The missing cost, latency, and throughput numbers matter just as much — an agent that plans well but is too slow or too expensive to run at your request volume is not a real option. Pick this over paid alternatives when budget is zero and you specifically care about agentic capability rather than operational metrics. Pick Artificial Analysis instead when you need inference cost, speed, or a broader picture of provider economics — the two are complements, not substitutes, and there is no reason not to check both. The Hugging Face placement is the quiet advantage. Rankings appearing on model pages means you can bounce from a score straight to the model card and see who published it, what license applies, and what the community says. That loop is faster than any standalone benchmark site. Caveats worth stating plainly: methodology documentation in the Space is sparse, so you cannot audit how tasks are scored, how models are selected, or how to submit one. If you need reproducible evaluation with a methodology your compliance team will sign off on, this will not clear that bar. For a hobbyist exploring open-source agent models, the thin documentation is a minor annoyance. For
Researching Agent Leaderboard? Get your full AI stack in 60 seconds.
Free, no signup — tell us your goal and get tools matched to your budget & existing stack.
Real-world workflow fit
Concrete scenarios for the personas Agent Leaderboard actually fits — and what changes day-one when you adopt it.
You open the Space without logging in, filter to base models only to hide finetunes and adapters, and scan the agentic task rankings for models in your size and hosting budget.
Outcome: A shortlist of two or three candidate models and their relative task scores, produced in minutes and with no signup.
You pull the per-model task score breakdowns and share the leaderboard URL with your team, then cross-check the same models on their Hugging Face model cards where results are surfaced.
Outcome: A defensible shortlist to hand to whoever runs your internal evaluation harness, with the leaderboard's limited scope clearly understood up front.
You re-check the rankings periodically as new evaluations are added and compare a specific model family's position across visits.
Outcome: A coarse read on which models are gaining on planning and tool-use tasks, without any cost or setup.
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.
- Sanity-check a vendor's agentic claims against independent community rankings before a pilot.
Limitations
- The Space does not publish its evaluation benchmarks, model selection criteria, or submission requirements in the evidence available, so the exact methodology and scope of the ranking are unspecified.
- Data freshness and update frequency are also unstated.
- There is no cost, latency, or throughput data, and no way to run a custom or private evaluation.
- The surrounding documentation is thin because it is a community-driven Space rather than a documented product.
- Treat rankings as a shortlist signal, not a certified measurement.
as of 2026-09-14
Verification history
We have re-verified Agent Leaderboard 9 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
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 9 verification passes.
Free to cite with attribution — this page re-verifies continuously.
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.
Agent Leaderboard itself is free with no tiers, so pricing isn't the constraint — your time is. It replaces the entry-level look at paid benchmarking services like Artificial Analysis at zero cost, which suits solo developers and small teams. Larger organizations still typically pay for bespoke evaluation or a dedicated benchmarking vendor once a model is under serious consideration.
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.
Browsing: zero setup — the leaderboard loads without a login. For a developer building a first agent prototype off the shortlist, budget a few hours to wire the chosen model into your framework and replicate a task or two. Teams needing a certified comparison should plan for a separate internal eval pass.
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.
- →From paid benchmarking services like Artificial Analysis: use Agent Leaderboard first for a free agentic-task shortlist, then pay for the deeper operational metrics only if you still need them.
- →From manual model card reading: open the leaderboard for ranked agentic scores, then verify license and size on the model card where results are also surfaced.
- ↗To a custom internal eval harness: export your shortlist from the rankings and rebuild your team's own task suite for private, reproducible numbers.
- ↗To a paid benchmarking service: carry the leaderboard shortlist over and layer on cost, latency, and throughput measurements.
Resources & Guides
- Documentationhuggingface.co
Docs · Agent Leaderboard
Full product docs from huggingface.co
- Documentationhuggingface.co
Spaces · Agent Leaderboard
Full product docs from huggingface.co
- Resourcehuggingface.co
Agent Leaderboard · Agent Leaderboard
Helpful link from huggingface.co
- Documentationhuggingface.co
Jobs · Agent Leaderboard
Full product docs from huggingface.co
Tutorials & Learning
YouTube returned 6 videos for “Agent Leaderboard”, and we withheld 5: 5 did not mention Agent Leaderboard. Showing the 1 we can prove is about Agent Leaderboard.
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.
Arena AI
Arena AI is a free, community-voted LLM leaderboard ranking chat models, agents, and fullstack code on live head-to-head battles.
Matharena
Free benchmark leaderboard that scores LLMs on elite competition math from AIME and IMO to Lean proof datasets
VLMEvalKit
Open-source toolkit for benchmarking vision-language models across 80+ multimodal tasks, with rankings published on the Open VLM Leaderboard.
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.
Alternatives to Agent Leaderboard
View all
Arena AI
Arena AI is a free, community-voted LLM leaderboard ranking chat models, agents, and fullstack code on live head-to-head battles.
Matharena
Free benchmark leaderboard that scores LLMs on elite competition math from AIME and IMO to Lean proof datasets
VLMEvalKit
Open-source toolkit for benchmarking vision-language models across 80+ multimodal tasks, with rankings published on the Open VLM Leaderboard.
Frequently Asked Questions
Categories
Used Agent Leaderboard? Help shape our editorial sentiment research.