I Tested Usage Limits for Claude, Codex, and Cursor

Comparative Usage Analysis of Coding AI Tools
📌 A head-to-head performance test was conducted comparing Claude Code (Anthropic), Codex (OpenAI), and Cursor (Grok 4.6) using a complex Laravel/PHP task to evaluate subscription usage limits.
🚀 Claude Code proved to be the most efficient in terms of speed, completing the task in 6 minutes while consuming only 1% of its weekly usage limit.
⏱️ Codex was significantly slower, taking 21 minutes to finish the task, which consumed 3% of its weekly usage limit.
💻 Cursor (using Grok 4.6) consumed 3% of its monthly usage limit for the same prompt, highlighting the high cost of its "high effort" reasoning mode.

API Cost Projections
📊 Estimating costs based on official API pricing reveals significant discrepancies between the subscription usage and the actual value of tokens consumed:
💰 If billed via API, the prompt would have cost $3.25 for Claude Code, $2.79 for Codex, and approximately $6.23 to $12.46 for Cursor (depending on the speed/model configuration).
🔍 High-level reasoning models like Grok 4.6 demonstrate that while powerful, their token consumption can be "astronomically" higher when compared to standard model tiers.

Platform Optimization and Variability
🔄 Subscription value is highly volatile due to frequent internal optimizations, rate limit resets, and temporary promotional discounts provided by AI companies.
🛠️ Factors such as sub-agent calls, automated browser testing, and architectural "overthinking" significantly impact the consumption of usage quotas regardless of the specific prompt complexity.
📈 Users should not rely on a fixed "best" option, as companies like Anthropic and OpenAI frequently push updates that alter the balance of usage limits and model efficiency.

Key Points & Insights
➡️ Context awareness: Understanding that "High Effort" or "Medium Level" thinking settings directly correlate to increased token usage and faster depletion of subscription limits.
➡️ Evaluation metric: API price modeling remains the most reliable way to benchmark costs, as subscription tiers often mask the true computational expense of the underlying models.
➡️ Operational transparency: Performance is not solely defined by the code produced but by the hidden "sub-tasks" (like automated testing or multi-agent verification) that each harness triggers, which drastically change the final cost.

📸 Video summarized with SummaryTube.com on Sep 19, 2026, 03:03 UTC