# Which AI coding agent is best for your team? > Compare AI coding agents by completed work, workflow fit, model flexibility, and verification. Use dated benchmark evidence and current vendor documentation to define a fair trial. URL: https://factory.com/articles/best-coding-agent-for-your-team Published: 2026-09-28 Updated: 2026-10-29 When you try an AI coding agent, the aim is to hand over a piece of engineering work without supervising every step. Choosing one for a team takes a closer look. A convincing demonstration can still leave you unsure how much setup, guidance, and review the tool will need in your repositories. An AI coding agent combines a model with tools for working on code and checking the result. A sensible comparison starts with a task you would actually give it, such as fixing a build or making a well-defined feature change. That keeps the choice grounded in what your team needs help with, and makes it easier to see which parts still need your attention. ## Define what the coding agent must complete Factory supports bounded tasks through ordinary Droid sessions and scripted work through [Droid Exec](https://docs.factory.com/droid-exec/overview). For larger efforts, [Missions](https://docs.factory.com/missions/overview) add planning, milestones, worker coordination, and validation. Use the workflow that fits the task rather than imposing the most elaborate orchestration on a small edit. That range is useful when a team wants the agent to carry work through implementation and verification. A patch is only part of the output. Reviewers also need the relevant checks, an explanation of unresolved failures, and a clear record of what the agent changed. Treat those as documented workflow capabilities, not comparative performance results. Competing products should not be reduced to autocomplete or assumed to lack terminal, cloud, or review functions. Compare the workflow each vendor actually documents today. Begin the trial with work the team already understands. A recurring build failure, a well-specified compatibility fix, or a maintenance change with reliable tests gives reviewers a concrete acceptance decision. Starting with an ambiguous feature makes it harder to distinguish a weak agent from a task nobody defined clearly. ## Compare coding agents without outdated product labels Cursor's [CLI documentation](https://cursor.com/docs/cli/using) describes running Agent from the command line with rules, MCP support, and command approval. Treating Cursor as an editor-only product would therefore exclude a documented part of its workflow. The [Claude Code overview](https://code.claude.com/docs/en/overview) lists terminal, IDE extensions, desktop, and web surfaces. A comparison that calls it terminal-only would similarly misrepresent the product. Evaluate the surface a team would actually use, including its access and deployment requirements. OpenAI documents a [Codex CLI](https://developers.openai.com/codex/cli), including moving between terminal work and cloud tasks. The relevant comparison is the supported execution path, not an assumed division between tools that can edit code and tools that can act. These vendor references were checked on September 28, 2026. They establish available workflows, not relative quality. Interface preferences, repository integrations, deployment requirements, and model policy can all change a team's shortlist. Record those requirements before scoring candidates instead of inferring product quality from surface coverage. Avoid giving one product a carefully prepared repository while another receives a broken setup. Use the same starting revision, relevant instructions, and task boundary. Product-specific configuration can be appropriate, but it should be documented rather than hidden inside an apparently identical comparison. ## Use benchmark results to select the first tasks Factory's October 2026 [Droid 1.0 research post](https://factory.com/news/frontier-harness) adds a same-model comparison with several of these products. On Terminal-Bench 4, run October 23 to 28, 2026, Factory reports Droid scoring 3.0 points higher than Cursor and 7.6 points higher than Claude Code with Claude Opus 5.5, and 3.5 points higher than Codex with GPT-6.1 Sol. These are Factory's results for the harness versions in its methodology, not a ranking for your repositories. Factory's [September 2025 Terminal-Bench report](https://factory.com/news/terminal-bench) reported Droid leading its Core v0.1.1 comparison at 58.75% task resolution with Opus 4.1 without thinking. The report evaluated terminal work under defined environments, tests, and time limits. This is a historical result, not a current ranking of the products listed above. The practical implication is that an evaluation should include the surrounding engineering work. An agent may need to diagnose an environment, repair a dependency, start a service, or discover the correct validation command before its code can be accepted. A test that scores only the generated function leaves those responsibilities out. Select tasks by the role they would play after adoption. For implementation, inspect behavior and regression coverage. For debugging, preserve the original failure and confirm the change addresses its cause. For review, judge findings against independently established defects instead of counting comments. Do not merge unrelated benchmark percentages into an overall product score. Task resolution, review F1, and model cost describe different experiments. A team can use all of them as evidence, but combining them requires a defensible evaluation design rather than a convenient average. Keep examples that expose limitations in the trial. A tool that works well on straightforward changes may still struggle when setup is incomplete or the expected behavior is encoded in an unfamiliar interface. Those cases help define where human involvement remains necessary. ## Choose model flexibility without losing accountability The model is a material part of a coding agent's behavior, but the product around it supplies tools, context, and execution controls. Record both. A result labeled only with the product name becomes difficult to interpret when its model selection changes. Factory's [custom-model support](https://docs.factory.com/model-independence/byok) allows provider keys, open-model endpoints, and local models in the CLI and desktop app. Availability differs by surface, so do not assume a custom configuration also appears in the hosted web or mobile product. Use that flexibility to test a defined requirement. A team may need an approved endpoint, a different cost profile, or evidence on a particular language. Changing models without preserving the task conditions makes it harder to tell whether the new configuration actually helps. For automatic model selection, record that the run used routing instead of attributing its outcome to a model the evaluator did not observe. Compare the complete routed workflow with the alternative configuration. Review the resulting changes and total cost, not just the advertised price of a possible model. Keep accountability with the engineering process. Repository policy, approval requirements, and independent checks should survive a model switch. A different reasoning setting or a higher benchmark score does not authorize the agent to broaden its access. ## Measure the work engineers still have to do An agent evaluation can look successful while moving effort from implementation into supervision. Record the interventions that changed the outcome: supplying missing context, fixing the environment, rewriting a patch, or explaining why a test failed. Separate elapsed time from engineer attention. A long unattended test run may be acceptable, while a shorter session that requires continuous steering may interrupt other work. The comparison should reflect the way the team intends to operate the product. Factory's [Agent Readiness](https://docs.factory.com/agent-readiness/overview) evaluates repository conditions that support autonomous work. Use those findings to understand setup and validation gaps, not to excuse them from the trial. If improving the repository is part of adoption, include that investment in the decision. Look closely at the handoff. A useful result names the changed behavior, points to the checks that ran, and discloses what remains uncertain. A large patch accompanied by a confident summary still requires review of scope and correctness. Retain abandoned attempts as well as accepted changes. Removing the failures after the fact can make a tool appear cheaper or more reliable than the workflow engineers would actually experience. ## Pick the coding agent for a defined rollout Choose a first task class rather than granting blanket authority over the backlog. The team should be able to name the configurations and tasks where its trial supports adoption, including the level of human intervention required. Write down the limits beside the decision. A successful maintenance trial might justify recurring dependency work while leaving architecture changes or sensitive migrations under a different review process. This is a more useful outcome than a universal winner with no operational boundary. Keep the trial cases and configuration so the team can repeat them after product or model changes. Add new failure cases while preserving enough of the original set to explain changes in the result. A better score on an easier task set should not be presented as product progress. A defensible coding-agent choice is one the team can operate repeatedly. Factory's benchmark results are relevant evidence about its tested configurations, while vendor documentation establishes current capabilities. Neither alone replaces a comparison of accepted work and the effort required to obtain it. ## Further reading - [How to evaluate coding agents on your codebase](https://factory.com/articles/evaluating-coding-agents-on-your-codebase) - [Factory Droid vs Cursor](https://factory.com/articles/factory-vs-cursor) - [Coding agent productivity metrics that matter](https://factory.com/articles/coding-agent-productivity-metrics)