Coding models and token-saving skills, measured

I run a quick suite and a deep suite of coding tasks myself in Claude Code, Codex CLI or Cursor CLI. Every number comes from one public file.

Runs published
1,477
Models
13
Latest run
16 October 2026

Deep suite: score against estimated cost

Speed-up (C++), one of six tasks, four models. Higher is better, and left is cheaper.

$0.01$0.02$0.05$0.10$0.20$0.50$1$2$51x1.4x2x2.8x4xEstimated cost per run (USD), log scaleTimes faster the library ran (log scale)GPT-6.1 SolOpus 5.5Haiku 5.5GPT-6 Luna
  • Claude models in Claude Code
  • GPT models in Codex CLI
Show as a table
ModelScoreCost per runOutput tokensMinutesFrontier
GPT-6 Luna0.07$0.014 to $0.0162,9481.2on it
Claude Haiku 5.51.01$0.14 to $0.72107,03614.4on it
GPT-6.1 Sol1.08$0.71 to $0.7627,65311.0on it
Claude Opus 5.51.66$4.0089,10420.1on it

Source: Daily Driver Bench at dailydriver.brenhq.com, updated 16 October 2026. Deep suite v3 has six tasks and 48 runs, all at medium effort. Each dot is a model's mean on the chart's scale, and an invalid run counts as 0. 11 of the 48 runs hit the time limit and were graded on the code at that point, nine of them Claude Opus 5.5's. A dot sits at the middle of its estimated cost range.