Globe of History vs SWE-2: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Globe of History and SWE-2 — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Globe of History
Globe of History
Interactive 3D globe visualizing 6,000 years of historical events including battles, inventions and philosophers.
Key features
- Interactive 3D Globe: Renders historical events as clickable markers on a manipulable three-dimensional globe for geographic context and spatial exploration.
- Extensive Historical Dataset: Presents thousands of events spanning roughly 6,000 years, covering categories such as battles, inventions, and philosophers.
- Timeline Navigation: Allows users to move through time to view events active in specific years, centuries, or eras to observe chronological change and patterns.
- Categorical Filtering: Enables filtering of events by type (e.g., battles, inventions, notable people) so users can focus on specific themes or domains.
- Event Detail Views: Provides descriptive information for individual events including date, location, and contextual notes to support learning and research.
- Search and Discovery: Supports searching for places, events, or historical figures to quickly locate points of interest on the globe.
- Interactive 3D globe visualization
- Dataset covering ~6,000 years of historical events
- Categorized events (battles, inventions, philosophers, etc.)
- Web-based access via official website
- Open Graph metadata for sharing
Best for
- Classroom Teaching: Instructors use the globe to illustrate historical timelines and spatial relationships between events, making lessons more visual and interactive.
- Historical Research: Researchers locate and compare event distributions across regions and time periods to identify trends or clusters relevant to studies.
- Curriculum Development: Educators and textbook authors source event examples and visualizations for lesson plans and educational materials.
- Public Engagement: Museums or public history projects incorporate the globe as an interactive kiosk or reference for visitors exploring historical narratives.
- Personal Exploration: History enthusiasts browse events by era or location to discover lesser-known incidents, inventions, and figures tied to places they care about.
- Contextual Reporting: Journalists and writers quickly reference historical events tied to a geographic area to add historical context to stories.
- Classroom teaching and history lessons
- Independent learning and exploration of historical timelines
- Research reference for historical event locations and categories
- Public outreach or museum displays to visualize historical data
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.