Catenary vs OpenAI Evals: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Catenary and OpenAI Evals — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Catenary
Catenary
Local-first spatial IDE that orchestrates Claude Code, Codex, Cursor, and other coding agents on an infinite canvas with visual context wires.
Key features
- Infinite Canvas: Terminals, Monaco editors, browsers, and git worktrees float on one pannable, zoomable surface so every agent stays visible at once.
- Context Wires: Drag a directed wire between panels to pass context to another agent, set to relay automatically or act as a standing permission.
- One-Click Worktrees: The New Task button creates an isolated branch, working directory, and agent, colour-coded across sidebar, dock, and canvas.
- Monaco Diffs: VS Code's editor inside the canvas with git-aware file tree and side-by-side diffs of everything an agent touched.
- Maestro Mode: One agent recruits, briefs, and wires a team of up to ten helpers, with an editable approval card before every action.
- Multi-Project Parallelism: Run several projects at once, each with its own canvas and multiple isolated branches, with state preserved on switch.
- Local-First Privacy: No account, no telemetry, and zero bytes of source code, prompts, or keys sent anywhere; only two outbound hosts total.
- Bring Your Own Keys: Agent CLIs talk directly to Anthropic, OpenAI, or Google with your own keys, or to Ollama and LM Studio on localhost.
Best for
- A developer runs three coding agents on separate branches simultaneously and watches all of them without losing track of any.
- An engineer delegates a specific subtask from one agent to another by dragging a wire instead of copy-pasting context between windows.
- A team working under strict data policies needs an agent IDE that provably never uploads source code.
- A solo builder ships several experiments in parallel isolated worktrees without polluting the main working tree.
- A reviewer wants side-by-side diffs of agent-authored changes before deciding what to keep.
- A user orchestrates a self-organizing squad of agents while keeping human approval on every structural change.
OpenAI Evals
OpenAI
Open-source framework and registry for creating, running, and comparing evaluations of large language models and LLM systems.
Key features
- Registry of Benchmarks: A curated, open registry of existing evals and benchmarks for common LLM tasks, enabling quick comparison across models and tasks.
- Custom & Private Evals: Author and run custom evals using your own datasets and grading logic; private evals let teams evaluate proprietary workflows without exposing data publicly.
- Grader Framework: Build rubric-driven automated graders, model-based graders, or human-in-the-loop grading pipelines to produce consistent, repeatable scoring.
- CLI/SDK & API Integration: Python-first SDK and CLI that integrate with the OpenAI API, support threaded execution, detailed logs, and programmatic control for batch runs.
- Continuous Evaluation (CE): Integrate evals into development workflows to run on changes, detect regressions, and track performance over time across model versions.
- Detailed Reporting & Metrics: Produces sample-level logs, aggregated counts and metrics, and final reports that summarize correctness, rubric scores, and other custom metrics.
- Extensibility & Reproducibility: Templates and examples in the repository make it straightforward to extend eval types (e.g., classification, generation, instruction following) and reproduce results.
- License & Contribution Controls: Public contributions are MIT-licensed with clear expectations about contributor rights and OpenAI’s reserved rights to use contributed data for product improvements.
- Open-source registry of prebuilt evaluation suites (benchmarks) for LLMs
- Author and run custom evals and private evals using your own data
- Integration with OpenAI API and Evals API / dashboard for running and tracking evals
- Support for structured outputs and JSON schema-based graders
- Automated grader / LLM-as-judge capabilities to estimate human judgments
- CLI and Python-based tooling; examples and Jupyter notebook demos
- Threaded and batched execution for running large eval sets locally
- Support for continuous evaluation (CE) workflows and comparison across runs
- MIT-licensed contributions with requirement to have rights for uploaded data
- Logging and reporting features with summary counts and final reports
Best for
- Benchmarking Models: Run the registry or custom evals to compare multiple model families or model versions on shared task suites and metrics.
- Prompt Optimization: Use dataset-driven evals to measure the effect of prompt edits and automatically iterate toward higher-quality prompts.
- Continuous QA for Deployments: Integrate evals into CI/CD to run continuous evaluation that catches regressions when changing prompts, models, or system components.
- Private Workflow Validation: Create private evals using internal data to validate an LLM’s behavior on organization-specific tasks without sharing sensitive data publicly.
- Automated Grading & Labeling: Build automated graders and rubric pipelines to approximate expert judgments, triage outputs for human review, and scale label generation.
- Research & Method Development: Use the open registry and tooling to prototype new evaluation methodologies, reproducible benchmarks, and shareable tasks with the community.
- Comparative Performance Analysis: Track and report differences in accuracy, rubric scores, and failure modes across model releases for decision-making and model selection.
- Benchmarking and comparing LLM models on task-specific datasets
- Building private evaluation suites that reflect production workflows without exposing data
- Automated grading and preference estimation to approximate human ratings
- Continuous evaluation in CI to detect regressions and nondeterministic behavior
- Measuring model performance on real-world occupation or task benchmarks (e.g., GDPval)
- Developing and validating model improvements prior to deployment