A practical comparison
Cursor vs Codex vs Claude Code
Compare workflows, agent orchestration, token efficiency and trade-offs.
27 criteria Three tools 19 official sources
Official sourcesSwipe the table to compare tools. Criteria stay pinned.
| Feature / criterion | Cursor Cursor official site | Codex Codex official site | Claude Code Claude Code official site |
|---|---|---|---|
Workflow & coding capabilities | |||
| Main experience | Editor with agents Code search, editing and terminal tools in a dedicated workspace. [1] | OpenAI coding agent Local and cloud work, with access varying by plan and interface. [9] | Anthropic coding agent Reads projects, edits files and runs commands. [13] |
| Where you work | Editor, CLI & cloud agents Subagent workflows are documented across all three. [2] | Desktop, CLI, IDE & cloud Check the access included in your plan. [9] | Terminal, IDE, desktop & web VS Code and JetBrains integrations are documented. [13] |
| Model choice | Multiple model providers Model selection changes usage and cost. [4] | OpenAI models Available models depend on account access; subagent models can be configured. [10] | Claude models Custom subagents can use a different model from the main session. [14] |
| Editing & running tests | File edits and shell commands Can run project tests; passing tests still needs independent review. [1] | Workspace edits and commands Execution is constrained by the sandbox and approval policy. [11] | Multi-file edits and commands Can use project tools and tests with the configured permissions. [13] |
Orchestration | |||
Orchestration means splitting a job between agents, coordinating their work and bringing the results together. | |||
| Parallel agents | Parallel subagents Workers use separate context windows and return results to the parent. [2] | Parallel subagents The parent delegates work, waits for results and consolidates findings. [10] | Subagents and agent teams Teams coordinate separate Claude sessions; this feature is experimental. [15] |
| How delegation starts | Automatic or requested Custom workers can specify prompts, tools and models. [2] | Request it explicitly Project or skill instructions can also request it. Eligible Ultra settings allow proactive delegation. [10] | Automatic or requested Worker descriptions help Claude decide when to delegate. [14] |
| Task coordination | Project coordinator Projects delegate tasks and maintain shared files across their agents. [3] | Parent-led coordination Supports follow-up instructions, waiting and agent-thread management. [10] | Lead plus teammate messaging Agent teams support shared tasks and direct communication between teammates. [15] |
| Coordination trade-offs | Startup and token overhead Separate workers gather context independently; small tasks can be slower. [2] | More tokens; possible edit conflicts Independent read-heavy tasks are a practical starting point. [10] | Experimental teams, off by default Known limits include resumption, coordination and shutdown behavior. [15] |
| Human approvals | Run modes and tool approvals Terminal commands ask by default; workspace edits generally do not. [7] | Sandbox plus approval policy Technical access and when to ask are separate controls. [11] | Permission modes and rules Allow, ask and deny rules control tools; managed settings can enforce policy. [18] |
Token efficiency | |||
A token is a unit of text processed by a model. Fewer tokens, lower prices and faster completion are different measurements. Include retries, subagents and human review when comparing value. | |||
| Keeping context focused | Isolate work in subagents Explore, Bash and Browser workers keep intermediate output out of the main conversation. [2] | Return focused summaries Delegate noisy exploration or logs so the main agent retains the decisions. [10] | Subagents and compaction Separate research from the main session; compact long conversations. [14][16] |
| Caching & reused context | Model-specific cache rates Published pricing separates input, cache reads/writes and output where applicable. [4] | Matching-prefix caching in the API Cache hits are conditional. API savings are not a promise of extra subscription tasks. [12] | Cache reporting in /usage Recent CLI versions show main-session cache statistics; these exclude subagents. [16] |
| Usage visibility | Usage pools and billing dashboard Included and on-demand usage are shown separately. [4][6] | Plan usage dashboard Check allowance and reset times; prompt length alone does not predict usage. [9] | Session and plan breakdowns /usage reports token statistics and plan usage. Its API cost estimate is not your subscription bill. [16] |
| Tokens per completed task | Not measured No controlled comparison using the same repository and tasks. | Not measured No controlled comparison using the same repository and tasks. | Not measured No controlled comparison using the same repository and tasks. |
| Cost per accepted result | Not measured No comparable total covering retries, agent work and review. | Not measured No comparable total covering retries, agent work and review. | Not measured No comparable total covering retries, agent work and review. |
Quality & reliability | |||
Documented tools can help check work; their existence does not establish a higher success rate. | |||
| Review & recovery tools | Agent checkpoints Restore agent file changes; checkpoints are separate from Git history. [1] | Controlled execution Sandboxing limits access. It does not verify that generated code is correct. [11] | Visual diff review The desktop interface supports reviewing changes and running multiple sessions. [13] |
| Code quality & correctness | Not measured No independent pass rate or code-quality score assigned. | Not measured No independent pass rate or code-quality score assigned. | Not measured No independent pass rate or code-quality score assigned. |
| Retries & completion rate | Not measured No measured failure, retry or recovery rate assigned. | Not measured No measured failure, retry or recovery rate assigned. | Not measured No measured failure, retry or recovery rate assigned. |
| Human review effort | Not measured No measured correction time or review-time ranking. | Not measured No measured correction time or review-time ranking. | Not measured No measured correction time or review-time ranking. |
Pricing & usage limits | |||
Listed individual web subscription prices in USD per month, checked 30 September 2026. Taxes, region, checkout and annual billing can differ. Similar prices do not imply equal usage. | |||
| Free access | Hobby: free, limited usage Includes access to Agent, Chat and Tab with the Auto model. [5] | Free access is listed Lightweight desktop access is subject to rollout. [9] | Not in the Free plan The pricing table lists Claude Code from Pro upward. [17] |
| Individual paid plans | Pro $20 · Pro+ $60 · Ultra $200 India-only Start: ₹649/month including tax; narrower usage pool. [5][4] | Go $8 · Plus $20 · Pro from $100 Go has limited desktop access. Plus broadens access; Pro tiers are $100, $200 and $500. [9] | Pro $20 · Max from $100 Monthly billing. Max has multiple usage tiers. [17] |
| Included usage | Separate model pools Pro, Pro+ and Ultra split Cursor Models from Other Models. Start covers Cursor Models only. [4] | Task- and model-dependent Local and cloud work share usage. Check your dashboard rather than assume a fixed task count. [9] | Session and weekly limits Paid plan capacity depends on the tier and workload. [17] |
| Extra usage & controls | Optional on-demand usage Individuals enable it explicitly. Team defaults differ; spend-limit enforcement can lag. [6] | Credits or separate API billing Eligible subscriptions can extend usage with credits; API usage is separately priced. [9] | Usage credits and API billing Track extra spend and configure limits where available. [16] |
Privacy & teams | |||
| Code and data controls | Privacy Mode Available on free and paid accounts; Cursor says it does not train on your data when enabled. [8] | Business data excluded by default Business plans state no training on business data by default. Check terms for your account type. [9] | Policy depends on account type Consumer training follows the improvement setting; commercial data is not used for training unless opted in. [19] |
| Team administration | Teams and Enterprise plans Enterprise adds provisioning, audit logs and advanced controls. [5] | Business and Enterprise controls Plan-level administration and sandbox policies address different parts of access. [11] | Team and Enterprise plans Central billing, administration and SSO are listed; Enterprise adds finer controls. [17] |
Strengths & weaknesses | |||
Our editorial assessment of the documented workflows above. These are reasons to shortlist a tool, not measured performance rankings. | |||
| Strengths | Integrated editing and model choice Worth shortlisting when you want agent tools close to your everyday editing workflow. [1][4] | Delegation with explicit controls Worth shortlisting for bounded tasks with parent-agent coordination and controlled execution. [10][11] | Configurable Claude workflows Worth shortlisting for custom workers and visibility into session usage. [14][16] |
| Weaknesses & trade-offs | More billing variables to follow Multiple pools, model rates and optional overages need attention as usage grows. [4][6] | Hard to predict tasks per plan Workload-dependent allowances make a simple price-per-prompt comparison misleading. [9] | Team orchestration needs care Experimental agent teams add coordination overhead and consume more tokens. [15] |
| Best-fit starting point | Hands-on developers Try it if an integrated editor and access to different model providers matter most. | Task-focused developers and teams Try it if you want to delegate bounded jobs and inspect the work before accepting it. | Developers building around Claude Try it if Claude-based custom workers and configurable tool permissions suit your workflow. |
Evidence, then a decision
How to compare fairly
Features and prices above describe published documentation. Strengths, weaknesses and buyer fit are our editorial interpretation. “Not measured” means we have no comparable test result to report.
- Use the same starting point. Try a bug fix, a small feature and a test-writing task from identical copies of a repository.
- Record the setup. Note the date, product version, model, plan, permissions, prompts, cache state and number of agents.
- Count the whole job. Include failed attempts, subagent usage, final correctness, elapsed time, billed cost and human review.
This is a suggested testing method, not a benchmark we have completed. Keep subscription allowance, API charges and review time separate; do not convert them into a single token score.
Frequently asked questions
Which tool is most token-efficient?
We have not measured a winner. Compare the same tasks from the same repository state, record models and settings, and count parent-agent work, subagents, retries and cache usage. Different model tokenizers and token prices make raw token totals an incomplete comparison.
Does using fewer tokens mean better value?
Not necessarily. A cheaper run that needs extensive correction may cost more overall. Track whether the result meets the requirements, the final bill or allowance consumed, elapsed time and human review time separately.
How does orchestration affect cost?
Parallel agents can tackle independent work at the same time, but each worker processes context and generates output. Coordination and repeated exploration add overhead. Start with one agent for small tasks, and test whether delegation actually improves the outcome on your work.
Is Claude Code only a terminal tool, or Codex only a cloud tool?
No. Both now offer multiple interfaces. The workflow table links to their current documentation. Check your plan and chosen interface because feature availability can differ.
What should a nontechnical founder choose?
First arrange someone who can review, test and maintain the output. Then try a small, clearly defined task in the interface you find easiest to use. These tools assist development; a subscription alone does not establish that an application is ready for customers.
Sources & editorial notes
Official pages checked on . Features, prices and limits can change. Source numbers in the table link directly to the relevant documentation.
- Cursor agent overview
- Cursor subagents
- Cursor Projects
- Cursor models and usage pools
- Cursor plans
- Cursor extra usage charges
- Cursor agent permissions
- Cursor privacy and security
- OpenAI plans and Codex access
- OpenAI subagent orchestration
- Codex approvals and sandboxing
- OpenAI API prompt caching
- Claude Code overview
- Claude Code subagents
- Claude Code agent teams
- Claude Code usage and costs
- Claude plans and pricing
- Claude Code permission controls
- Claude Code data policies
This guide compares products and workflows, not just underlying models. Availability can depend on interface, account, region and rollout. No hands-on performance rating or efficiency winner is claimed.