A benchmark for agentic professional workflows with verifiable success criteria, reporting pass rates and partial scores for model plus agent-harness rows.
BenchLM mirrors the Agents Last Exam full/overall split from the public leaderboard API. The snapshot reports pass rate and partial average score across roughly 149 professional workflow tasks for model plus agent-harness rows.
ALE-Bench is display only on BenchLM. Its rows combine a base model with an agent harness such as Codex, OpenClaw, Claude Code, Droid, Cursor CLI, or Gemini CLI, so BenchLM keeps the table separate from model-only rankings.
BenchLM mirrors the published pass rate view for ALE-Bench. codex / gpt-5-5 leads the public snapshot at 26.2% , followed by ale_claw / gpt-5-5 (24.2%) and openclaw / gpt-5-5 (22.8%). BenchLM does not use these results to rank models overall.
codex / gpt-5-5
codex
codex/gpt-5-5
ale_claw / gpt-5-5
ale_claw
ale_claw/gpt-5-5
openclaw / gpt-5-5
openclaw
openclaw/gpt-5-5
The published ALE-Bench snapshot is tightly clustered at the top: codex / gpt-5-5 sits at 26.2%, while the third row is only 3.4 points behind. The broader top-10 spread is 10.1 points, so the benchmark still separates strong models even when the leaders cluster.
27 models have been evaluated on ALE-Bench. The benchmark falls in the External benchmark mirrors category. BenchLM tracks this category separately from its weighted global scoring system, so these results are best compared on the dedicated Korean benchmark views. ALE-Bench is currently displayed for reference but excluded from the scoring formula, so it does not directly affect overall rankings.
Year
2026
Tasks
Approximately 149 ALE-V1 professional workflow tasks
Format
Pass rate and partial-credit score
Difficulty
Real-world agentic workflows
BenchLM mirrors the public Agents Last Exam full/overall leaderboard API as ALE-Bench. Rows combine base models with agent harnesses such as Codex, OpenClaw, Claude Code, Droid, Cursor CLI, and Gemini CLI, so the table remains display-only.
Version
ALE-Bench 2026
Refresh cadence
Quarterly
Staleness state
Current
Question availability
Public benchmark set
BenchLM uses freshness metadata to decide whether a benchmark should still be treated as a strong differentiator, a benchmark to watch, or a display-only reference. For the full scoring policy, see the BenchLM methodology page.
A benchmark for agentic professional workflows with verifiable success criteria, reporting pass rates and partial scores for model plus agent-harness rows.
codex / gpt-5-5 currently leads the published ALE-Bench snapshot with 26.2% pass rate. BenchLM shows this benchmark for display only and does not use it in overall rankings.
27 AI models are included in BenchLM's mirrored ALE-Bench snapshot, based on the public leaderboard captured on June 2026 API snapshot.
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