Pass@N

Pass rate

Tasks solved / tasks in scope, up to 2 attempts (strict per-set denominator).

Formula: (tasks_passed_attempt_1 + tasks_passed_attempt_2_only) / task_set_size

Includes unattempted tasks as failures. Scope-aware; reflects active filters (set, category, difficulty). Primary ranking metric.

71.9%
Tasks pass 46/64
1st: 40 2nd: 6 Failed: 18
Avg cost / task

Avg cost / task

Average LLM cost per distinct benchmark task in USD.

Formula: SUM(cost_usd) / COUNT(DISTINCT task_id) across all the model's results in scope.

Use to compare operating cost across models with similar pass rates. Does not account for quality. Combine with $/Pass for a cost-efficiency view.

<$0.001
Latency p50

Latency p50

Median per-task wall time (LLM call + compile + test), in milliseconds.

Formula: 50th percentile of per-task duration_ms: LLM latency + compile time + test time.

Use p50 for a typical-case latency expectation. Unaffected by outlier slow tasks.

2m 22s
Avg score

Avg attempt score

Mean per-attempt score on a 0–100 point scale (partial credit). Drill-down only.

Formula: Mean of attempt scores across all results rows: SUM(score) / COUNT(*) over the results table. Each attempt earns 0–100 points based on compile + test outcomes.

Drill-down companion to pass_at_n. Rewards partial credit but not directly comparable to pass rate; use for within-model analysis.

60.9 / 100
All-runs pass rate

All-runs pass rate

Fraction of tasks the model solved in every single run (strict consistency, also written pass^n).

Formula: tasks where ALL runs produced a passing result / tasks_attempted_distinct

Measures reliability under repetition. High value means the model is unlikely to regress on a re-run, important for CI and production use. Formal name in the literature: pass^n.

65.6%
$/Pass

$/Pass

Total cost divided by number of distinct tasks passed. Lower is better.

Formula: SUM(cost_usd) / tasks_passed_distinct across all runs.

Best single cost-efficiency metric. Penalises expensive models that pass few tasks and rewards cheap models with high pass rates.

$0.0001
Latency p95

Latency p95

95th-percentile per-task wall time. Captures tail latency.

Formula: 95th percentile of per-task duration_ms across all tasks in all runs.

Use p95 to understand worst-case latency. A low p95 means the model rarely stalls, relevant for automated pipelines with timeouts.

2m 40s

Overview

OpenAI: GPT-5.4 has run on 3 occasions, attempting 64 tasks with an average score of 60.9 / 100.

Settings

Generation parameters used across this model's runs. "varies" indicates the value differed between runs.

Temperature
varies
Thinking budget
varies
Avg tokens / run (input + output)
122,617
Consistency
89.1%

History

1
2
3
3 runs · oldest 9d ago · latest 10d ago

Cost

meanp95

Failure modes

  • AL0104 132 Syntax error, ')' expected view all →
  • AL0000 87 App generation failed view all →
  • AL0111 42 Semicolon expected. Add a semicolon (;) to terminate the statement. view all →
  • AL0360 36 Text literal was not properly terminated. Use the character ' to terminate the literal. view all →
  • AL0107 35 Syntax error, identifier expected. Provide a valid name (letters, digits, and underscores only). view all →
  • AL0118 26 The name 'CreateSequentialGuid' does not exist in the current context. view all →
  • AL0198 26 Expected one of the application object keywords (table, tableextension, page, pageextension, pagecustomization, profile, profileextension, codeunit, report, reportextension, xmlport, query, controladdin, dotnet, enum, enumextension, interface, permissionset, permissionsetextension, entitlement) view all →
  • AL0185 17 Interface 'Payment Processor' is missing view all →
  • AL0126 15 No overload for method 'CreateGuid' takes 1 arguments. Candidates: built-in method 'CreateGuid()' view all →
  • AL0132 13 'FieldType' does not contain a definition for 'Enum' view all →

Shortcomings

AL concepts OpenAI: GPT-5.4 struggles with. Click a row for description, correct pattern, and observed error codes.

Shortcomings analysis queued

Queued for analysis. This section will populate once the run is processed.

Recent runs

Runs
StartedRunModelTasksScoreCostDurationStatus
9d ago886e8916-8c0…45/6459.1 / 100$0.0012h 35mcompleted
10d ago1b251048-5bd…44/6460.1 / 100$0.0012h 42mcompleted
10d ago7b2a8a3e-13f…44/6463.5 / 100$0.0012h 50mcompleted

See all 3 runs →

Methodology

Pass rate counts unattempted tasks as failures (strict denominator). Avg score is the mean of every attempt the model produced — failed first tries that triggered a retry contribute one observation each, pulling the mean down. See the about page for the full breakdown and unit conventions.