Nova Act by Amazon vs SWE-2: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Nova Act by Amazon and SWE-2 — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
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
SWE-2
Cognition
Cognition's coding model that scores 50.0% on FrontierCode 1.1 Main at 64% lower cost than comparable frontier models.
Key features
- Pareto-Frontier Cost Efficiency: Matches GPT-5.6 Sol and Fable 5/5.1 on coding benchmarks at a fraction of their price and comes within a few points of GPT-6 Astra at roughly a quarter of the cost.
- Single-Run Multi-Effort RL: A reinforcement learning algorithm trains all reasoning-effort levels in one run, applying a per-level linear cost penalty derived from the base model's local frontier slope.
- Focused Codebase Exploration: Stronger engineering judgment lets the model decide which parts of a repository matter, cutting mean steps per run from 127 to 53 at medium effort.
- Selectable Effort Levels: Ships medium, high and max reasoning settings so teams can trade additional steps and cost for accuracy on harder tasks.
- End-to-End Test Writing: Produces tests that validate an implementation end to end, catching regressions and edge cases more reliably than previous SWE models.
- Resourceful Task Recovery: When an expected route is blocked — an unavailable MCP integration, for example — it finds an alternative path to the same answer within the user's stated boundaries.
- Efficient Training and Serving Stack: NVFP4/FP8 kernels, quantization-aware training and an online draft model cut memory use and train-inference mismatch despite nearly 3x the base parameters of SWE-1.7.
- Hardened Verifier Flywheel: Triples the number of RL environments, adds instruction-following overlays, and uses earlier SWE-2 checkpoints to iteratively strengthen verifiers.
Best for
- Agentic Software Engineering: Powering Devin sessions that plan, edit, build and test changes across a real repository with minimal supervision.
- Cost-Sensitive Coding at Scale: Teams running large volumes of automated coding tasks pick a model that holds frontier-adjacent accuracy at a materially lower per-task cost.
- Terminal and Tooling Workflows: Strong Terminal-Bench results suit tasks driven through shell commands, build systems and command-line tooling.
- Regression Test Generation: Generating end-to-end tests for existing implementations to catch edge cases before a release.
- Effort-Tiered Task Routing: Routing simple tickets to medium effort and hard migrations to high or max effort within the same model deployment.
- Benchmark and Model Evaluation: Engineering leaders compare coding model options on published FrontierCode, DeepSWE and Terminal-Bench numbers alongside cost.