Can AI agents run a sprint? Six project management tools compared
Compare the documented agent capabilities of Linear, ClickUp, Notion, Asana, monday, and Stellary across one reproducible sprint benchmark.
11 min read
What this comparison looked like in April 2026
In April 2026 the four options a coding team was weighing were GPT-5.4 (OpenAI’s general-purpose flagship of the moment), Claude Opus 4.6 (Anthropic’s long-task model), Gemini 3.1 Pro (Google’s multimodal, large-context model) and Composer 2 (Cursor’s own coding model, routed inside its editor). The verdict was already “no single winner”: GPT-5.4 for demanding general work, Opus 4.6 for long, multi-step tasks, Gemini 3.1 Pro for speed and large contexts, Composer 2 when the workflow lived in Cursor.
What changed since
- Every model in the lineup has been superseded: GPT-5.5 replaced GPT-5.4, Claude Fable 5 replaced Opus 4.6 as the long-task model, Gemini 3.5 Flash took over the speed-and-context slot, and Cursor moved from a single model to an agent layer that routes across models.
- The selection criteria did not change. Difficulty of the task, cost per successful task, context size and the execution environment still decide more than benchmark rankings. That is why the current version keeps the same structure.
- Agent execution moved into the tools. In April the question was “which model”; by September it is as much “which harness”: Claude Code, Codex, Cursor and their MCP connections shape the result at least as much as the model. See what MCP changes for AI coding tools.
If you searched for one of these models
- Choosing a model for AI agents on project work: Claude Fable 5 vs Opus 5 and GPT-5.6 Sol, Terra and Luna are the current model-family guides.
- Choosing by task: best AI model for backend development and best AI model for code review and audits.
- The current head-to-head: Best AI models for coding.
