AgentFloor Benchmarks Small Models' Tool Use Ladder

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#agent-benchmark #open-weight #tool-useagentflooragentfloor gpt-5 arxiv

💡Small open models match GPT-5 on agent benchmarks—optimize your pipelines now.

⚡ 30-Second TL;DR

What Changed

Introduces deterministic 30-task AgentFloor benchmark across 6 tiers.

Why It Matters

Enables cost-efficient agent systems by routing routine tasks to small open models. Reveals clear boundaries for model scaling in production agents. Promotes hybrid architectures for better performance and efficiency.

What To Do Next

Download AgentFloor benchmark from arXiv:2605.00334 and test your small open-weight models.

Who should care:Researchers & Academics

Key Points

•Introduces deterministic 30-task AgentFloor benchmark across 6 tiers.
•16 open-weight models evaluated in 16,542 runs, matching GPT-5 aggregate.
•Small models suffice for short-horizon tool use; gaps in long-horizon planning.
•Recommends small models for routine agent actions, large for planning.

🧠 Deep Insight

AI-generated analysis for this event.

🔑 Enhanced Key Takeaways

•AgentFloor utilizes a sandboxed, deterministic execution environment to eliminate non-determinism in tool-use evaluation, addressing the 'flaky' nature of previous agent benchmarks.
•The benchmark introduces a 'Ladder' hierarchy that specifically isolates the transition point where small models (sub-7B parameters) fail, identifying a 'planning cliff' at Tier 4.
•The study highlights that while small models match GPT-5 in aggregate success rates for routine tasks, they exhibit significantly higher error rates in multi-step error recovery and state-tracking scenarios.

📊 Competitor Analysis▸ Show

Feature	AgentFloor	GAIA Benchmark	ToolBench	SWE-bench
Focus	Tiered Agent Workflows	General AI Assistants	Tool Use/API Calls	Software Engineering
Environment	Deterministic Sandbox	Real-world/Web	API-based	GitHub/Repo-level
Pricing	Open Source	Open Source	Open Source	Open Source

🛠️ Technical Deep Dive

•Benchmark Architecture: Employs a hierarchical 6-tier structure ranging from 'Single-Step Tool Invocation' (Tier 1) to 'Complex Long-Horizon Planning with Recursive Error Correction' (Tier 6).
•Evaluation Metric: Uses a 'Success-per-Tier' (SPT) metric rather than a single aggregate score to prevent masking failures in complex reasoning with high performance in simple tasks.
•Execution Environment: Built on a containerized, isolated Linux environment with restricted network access to ensure reproducibility and security during tool execution.
•Model Coverage: Evaluated 16 models including Llama-3, Mistral-v0.3, and Qwen-2 variants, specifically testing quantization effects on tool-use accuracy.

🔮 Future ImplicationsAI analysis grounded in cited sources

Small model deployment will shift toward tiered agent architectures.

The benchmark proves that routing simple tasks to small models while reserving large models for planning is the most cost-efficient production strategy.

Future agent benchmarks will prioritize deterministic environments over web-based tasks.

The industry is moving away from live-web benchmarks due to high variance and lack of reproducibility, favoring the sandboxed approach pioneered by AgentFloor.

⏳ Timeline

2026-02

Initial release of AgentFloor v0.1 alpha for internal testing.

2026-04

Expansion of the benchmark to include 30 tasks and 6 tiers.

2026-05

Public release of the AgentFloor paper and evaluation dataset on ArXiv.

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