Mouse-1 vs Nova Act by Amazon: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Mouse-1 and Nova Act by Amazon — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Mouse-1
Mouse
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.
Nova Act by Amazon
Amazon Web Services, Inc.
A browser-focused agent model and AWS service that automates UI workflows from natural language and escalates to humans when needed.
Key features
- Natural-Language Browser Control: Converts conversational instructions into deterministic browser actions (click, type, scroll, navigate) to automate UI workflows without writing low-level automation scripts.
- Python SDK and IDE Integration: Provides a Python SDK plus IDE extensions (VS Code, Cursor, etc.) for chat-to-script generation, live debugging, step-by-step builders, and action viewers to iterate on agents inside developer tools.
- Human-in-the-Loop Escalation: Built-in patterns and reference implementations to escalate complex or uncertain steps to human supervisors and integrate notification and HITL workflows into production.
- Fleet Deployment and Management on AWS: Deploy, scale, and manage fleets of Nova Act agents through AWS integration (Bedrock/other AWS services) for running production UI automation at scale with monitoring and logs.
- QA and Test Automation Support: Plugins and sample projects (pytest integration, parallel execution, HTML reporting) for end-to-end QA automation and regression testing in real browser sessions.
- Observability and Logging: Structured logs, user-data directories per test/session, and reporting artifacts to trace agent actions, diagnose failures, and audit automated workflows.
- Chat-to-Script & Step Builder: Interactive chat-driven generation of automation scripts and step-by-step workflow builders that let non-experts create or refine browser tasks quickly.
- Action Viewer and Live Debugging: Visual tools to inspect, replay, and debug agent-performed actions during development to improve reliability and reproducibility.
- Natural-language to browser-action translation (click, type, scroll, navigate)
- Human-in-the-loop escalation and human intervention service reference implementation
- Python SDK for building and running agents and workflows
- IDE extensions (Visual Studio Code, Kiro, Cursor) with chat-to-script, step-by-step builder, live debugging, and action viewer
- Web playground at nova.amazon.com/act for experimentation
- Deploy agents to AWS and integrate with Bedrock and AWS monitoring/console
- QA and end-to-end testing integrations (pytest plugins, parallel test execution, HTML reporting)
- Environment-driven auth and configuration (NOVA_ACT_API_KEY, AWS_PROFILE, AWS_REGION)
- Logging, user-data directories, and organized per-test/session logs for debugging and audit
- Sample repositories and reference implementations on GitHub for HITL patterns and notifications
Best for
- Automating repetitive web UI tasks (form filling, data entry, routine admin workflows) by translating business instructions into browser actions without manual scripting.
- End-to-end QA and regression testing: run parallel browser tests with Nova Act SDK and pytest integration to validate web application behavior and generate HTML reports.
- Data extraction and structured scraping inside authenticated sessions where agents mimic human browser interactions while following escalation and audit rules.
- Customer support and operations automation: have agents navigate web consoles, gather diagnostics, or perform standard support procedures, escalating to human operators when needed.
- Business process automation across SaaS apps: coordinate cross-application sequences (download reports, upload records, reconcile data) using natural language workflows combined with Python logic.
- Human-in-the-loop compliance flows: automate most steps of compliance checks while routing ambiguous or high-risk decisions to supervisors through the provided HITL reference service.
- Developer productivity: quickly prototype and debug browser automation in the Nova web playground or IDE extension, then deploy reliable agents to AWS for production use.
- Automating repetitive UI workflows in production web apps (data entry, form submission, navigation)
- End-to-end QA and browser-based testing with parallel execution and custom reporting
- Building agent fleets to run scheduled or event-driven browser tasks at scale
- Human-in-the-loop supervision for sensitive or ambiguous automation steps
- Rapid prototyping and debugging of browser agents inside an IDE or web playground