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LMArena vs Mouse-1: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of LMArena and Mouse-1 — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

LMArena logo

LMArena

LMArena

Free

Open platform for crowdsourced benchmarking and live leaderboards that ranks chatbots and LLMs using user votes and automated evaluations.

Key features

  • Crowdsourced Pairwise Voting: Users can interact with multiple chatbots and cast pairwise votes; aggregated human preferences are used to compute model win-rates and power the live leaderboard.
  • Bradley–Terry Ranking Engine: Uses the Bradley–Terry statistical model to convert pairwise user votes into continuous rankings and win-rate metrics for robust comparison between models.
  • Arena-Hard-Auto Evaluation Suite: Provides an automated benchmark (Arena-Hard-Auto) with curated hard prompts, style-control features, and the ability to use GPT-4.1/Gemini judges for pre-deployment model assessment.
  • Public Datasets and Preference Collections: Hosts multiple datasets (e.g., search-arena-24k, arena-human-preference-140k) and preference data on Hugging Face for training, evaluation, and replication of leaderboard results.
  • Hugging Face Spaces & Model Repos: Maintains interactive leaderboards and example apps as Hugging Face Spaces and publishes model and dataset repositories for community use and reproducibility.
  • FastChat Integration for Serving: Commonly integrated with FastChat to serve and evaluate chatbots in live comparisons and crowdsourced matches, enabling scalable interactive evaluations.
  • Open Tooling & Scripts: Provides open-source scripts and configuration (e.g., config YAMLs, result display scripts) to run evaluations, add style attributes, and compute win rates under different judge configurations.
  • Crowdsourced pairwise voting system driving live leaderboards (Bradley-Terry ranking)
  • Public leaderboard and web chat interface (lmarena.ai) to try and compare models
  • Arena-Hard-Auto: automated evaluation toolkit and benchmark with configurable judges (supports GPT-4.1/Gemini as judges)
  • Integration with FastChat for training, serving, and evaluating chatbots
  • Hugging Face presence: publishes datasets, benchmark suites, models, and Spaces (leaderboard Space)
  • Open datasets for benchmarking (e.g., search-arena-24k, arena-hard datasets)
  • Support for custom model evaluation via config YAML (model_list) and Python tooling (show_result.py, add_markdown_info.py)
  • Model formats and training artifacts compatible with PyTorch/transformers (AutoTokenizer usage, model repo examples)
  • Support for multi-modal evaluation and specialized arenas (e.g., VisionArena)
  • Plugins/compatibility with external APIs (OpenAI API for GPT judges) and community model repos

Best for

  • Pre-deployment Model Evaluation: Run Arena-Hard-Auto to estimate how a candidate model will perform on LMArena-style human preference comparisons before public release.
  • Live Comparative Benchmarking: Publish a chatbot endpoint and compare it against other models on the live LMArena leaderboard to measure relative win rates from real user votes.
  • Research on Human Preferences: Use the arena-human-preference datasets to study preference patterns, fine-tune models on preference data, or reproduce published leaderboard outcomes.
  • Automated Stress Testing: Evaluate robustness and style-control behavior of models using Arena-Hard-Auto’s hard prompts and judge ensembles (GPT-4.1/Gemini) to surface failure modes.
  • Dataset-driven Fine-tuning: Leverage LMArena-hosted datasets (search-arena-24k, others) to fine-tune conversational models for better performance on human-preference metrics.
  • Community Benchmarking & Transparency: Host community challenges and transparent leaderboards via Hugging Face Spaces and GitHub repos to encourage reproducible, open comparisons.
  • Evaluate and compare chatbot/LLM performance with real user votes and automated judges
  • Pre-deployment validation: run Arena-Hard-Auto to estimate likely performance on the public leaderboard
  • Publish research models, datasets, and leaderboards for community benchmarking and reproducibility
  • Build and serve chatbots using FastChat integration and measure user preference on LMArena
  • Run automated, configurable evaluations using ensemble judges (GPT-4.1, Gemini, etc.)
View LMArena details
Mouse-1 logo

Mouse-1

Mouse

Paid

Mouse-1 is a fast, low-cost decision model that plugs into agent harnesses to handle routing, gating and verification.

Key features

  • Decision Layer for Agents: Takes agent state and options and returns probabilities so the harness can route, gate and verify.
  • About 100 ms Latency: Answers faster than a chat model's first token.
  • Batch Decisions: One request answers up to 128 questions.
  • Prompt-Injection Resistance: Chooses only from supplied options; planted instructions changed 0% of decisions in vendor tests.
  • Zero Data Retention: Requests are scored and dropped, never stored or used for training.
  • Image and Document Classification: Handles content moderation, document routing and browser-state detection.

Best for

  • Agent Routing: Deciding which model or tool an agent should use next.
  • Action Gating: Approving or blocking risky agent actions cheaply.
  • Content Moderation: Classifying images or text as safe or unsafe.
  • Document Routing: Sending invoices, contracts and IDs to the right workflow.
  • Browser Agents: Detecting screen state such as login required or error.
  • Cost Reduction: Offloading small decisions from expensive frontier models.
View Mouse-1 details