← Claude Haiku 5.5

Claude Haiku 5.5: capability breakdown

141 / 220

measured 8 of 22 rungs · effort row: high (the median of its published effort settings)

Provisional. Measured from Artificial Analysis's public page on 2026-10-12, using the High effort row: the median of the five effort rows AA publishes (max, xhigh, high, medium, low), the same rule that gives Opus 5.5, Sonnet 5.5 and GPT-6.1 Sol their High rows. AA's default page row is Max and is not used. Fitted with the 2026-10-04 provisional method (rung difficulties and slopes of the 2026-09-29 ladder, without the full refit), which reproduces the published ladder to within 0.9 points on average across 148 scored models and tends to read 0.6 points low; the 2026-10-07 rebuilt variant of that shortcut gives 139.6. Several Artificial Analysis benchmarks (MLCR, τ³-Banking, Terminal-Bench Hard and 2.1, IFBench, GPQA) are not yet published for this model, so 14 of 22 rungs are fitted. The next full pipeline run replaces it.

This page shows where the number comes from. Each rung is one public benchmark, worth up to 10 points times the model's chance-corrected score on it; the score is the sum of all 22 rungs. How the capability ladder works explains the rungs, the fit and its limits.

Rung (easiest → hardest) Points / 10 How Source date
HumanEval 9.93 fitted
MMLU 9.72 fitted
MATH-500 9.88 fitted
MMLU-Pro 9.41 fitted
AIME 2024 9.99 fitted
AIME 2025 9.80 fitted
LiveCodeBench 9.22 fitted
GPQA Diamond 9.00 fitted
IFBench 6.94 fitted
τ²-Telecom 9.21 fitted
AA-LCR 7.73 measured 2026-10-12
Terminal-Bench 2.1 7.35 fitted
SciCode 4.87 measured 2026-10-12
Terminal-Bench Hard 4.87 fitted
AutomationBench 3.37 measured 2026-10-12
AA-Briefcase (rubric pass) 4.70 measured 2026-10-12
HLE 3.41 measured 2026-10-12
MLCR 1.60 fitted
τ³-Banking 2.91 fitted
Omniscience acc. 3.48 measured 2026-10-12
CritPt 1.86 measured 2026-10-12
GDP.pdf 1.72 measured 2026-10-12
Total 140.97 = 141 / 220

Measured rows are the model's own published result on that benchmark. fitted rows are an item-response estimate: what a model with this ability would be expected to score on a benchmark it has no published result for (see the methodology).

Per-benchmark results are Artificial Analysis's (public model pages); fitted values are this site's estimates. Permission to republish requested 2026-09-05.