Skip to the comparison

GPT 6 Luna vs Haiku 5.5

Haiku 5.5 leads on quality in 2 of 4 pairings; the rest show no clear difference. 4 pairings; 24 merged changes in Stet-AI/stet. Compare the individual calls below to choose which setup to investigate.

What this comparison is

  • 1 real repositoryStet-AI/stet, private; not public benchmark scores
  • 24 merged changeseach run end to end by the coding agent from the repo as it was before the change
  • Graded against the merged changeby the repo’s own tests and by judges comparing each patch with it
  • Quality, cost and timea smaller sample than public benchmarks, but real agent work on a real codebase

Recorded setup pairings

Each row is a separate recorded comparison. Scores are not pooled or averaged across effort settings. W/D/L means wins, draws and losses; undecided judgments remain undecided.

Stet score: A setup’s average chance of beating another setup on a task, from 0 to 100; 50 is even with the field.

Setup pairingRecorded quality call and recordQuality evidenceCost per taskAgent time per task
GPT 6 Luna max vs Haiku 5.5 maxGPT 6 Luna max · max reasoningHaiku 5.5 max · max reasoning23/24 tasks observed by both setups.No clear quality difference: Haiku 5.5 max won 3, GPT 6 Luna max won 2, 17 tied, 2 undecided; GPT 6 Luna max leads on cost: 2.1× cheaper; Haiku 5.5 max leads on agent time: a little faster.Head to head, Haiku 5.5 max won 3, tied 17 and lost 2 against GPT 6 Luna max, with 2 undecided.No clear difference
2–3 head to head, 17 tiedGPT 6 Luna max: 46.0 (95% range 40.4–51.6)Haiku 5.5 max: 48.2 (95% range 38.8–58.1)

GPT 6 Luna max: $0.37

Haiku 5.5 max: $0.78

GPT 6 Luna max: 59.7 min

Haiku 5.5 max: 58.4 min

GPT 6 Luna low vs Haiku 5.5 maxGPT 6 Luna low · low reasoningHaiku 5.5 max · max reasoning23/24 tasks observed by both setups.Haiku 5.5 max leads on quality, 5–1 head to head with 16 tied and 2 undecided (directional at this sample size); GPT 6 Luna low leads on cost and agent time: 14.6× cheaper and 8.5× faster.Head to head, Haiku 5.5 max won 5, tied 16 and lost 1 against GPT 6 Luna low, with 2 undecided.Directional lead at this sample size
5–1 head to head, 16 tiedGPT 6 Luna low: 37.8 (95% range 27.8–47.7)Haiku 5.5 max: 48.2 (95% range 38.8–58.1)

GPT 6 Luna low: $0.05

Haiku 5.5 max: $0.78

GPT 6 Luna low: 6.9 min

Haiku 5.5 max: 58.4 min

GPT 6 Luna max vs Haiku 5.5 lowGPT 6 Luna max · max reasoningHaiku 5.5 low · low reasoning24/24 tasks observed by both setups.No clear quality difference: GPT 6 Luna max won 2, Haiku 5.5 low won 1, 20 tied, 1 undecided; Haiku 5.5 low leads on cost and agent time: 5.7× cheaper and 6.0× faster.Head to head, GPT 6 Luna max won 2, tied 20 and lost 1 against Haiku 5.5 low, with 1 undecided.No clear difference
2–1 head to head, 20 tiedGPT 6 Luna max: 46.0 (95% range 40.4–51.6)Haiku 5.5 low: 44.7 (95% range 39.5–49.1)

GPT 6 Luna max: $0.37

Haiku 5.5 low: $0.06

GPT 6 Luna max: 59.7 min

Haiku 5.5 low: 9.9 min

GPT 6 Luna low vs Haiku 5.5 lowGPT 6 Luna low · low reasoningHaiku 5.5 low · low reasoning24/24 tasks observed by both setups.Haiku 5.5 low leads on quality, 6–1 head to head with 16 tied and 1 undecided (directional at this sample size); GPT 6 Luna low leads on cost and agent time: 1.2× cheaper and 1.4× faster.Head to head, Haiku 5.5 low won 6, tied 16 and lost 1 against GPT 6 Luna low, with 1 undecided.Directional lead at this sample size
6–1 head to head, 16 tiedGPT 6 Luna low: 37.8 (95% range 27.8–47.7)Haiku 5.5 low: 44.7 (95% range 39.5–49.1)

GPT 6 Luna low: $0.05

Haiku 5.5 low: $0.06

GPT 6 Luna low: 6.9 min

Haiku 5.5 low: 9.9 min

Questions about these results

Which is better for coding, GPT 6 Luna or Haiku 5.5?
Haiku 5.5 leads on quality in 2 of 4 pairings; the rest show no clear difference. 4 pairings; 24 merged changes in Stet-AI/stet. Compare the individual calls below to choose which setup to investigate.
Where do these coding results come from?
Both coding agents worked the same 24 merged changes from the private Stet-AI/stet repository, each in the real repo as it was before the change. The repo’s tests and judges graded every patch against the merged change; no public benchmark scores are used.
Are reasoning settings combined into one model score?
No. Each effort pairing keeps its own recorded quality call, head-to-head record, cost and agent time. Incompatible scores are never averaged.