Inconclusive · Study2views
acceptance row5 skills 1790524032999
ora Agent Smoke (5 tasks) · 1 task · 1 harness · 1 model
Ora Agent Smoke delivered flawless execution on this task.
Ranked shootout, no baseline arm: cells ranked by the composite score — rubric quality and goal achievement, then efficiency — claude-code / claude-haiku-4-5-20251001 leads.
Abstract
The treatment with ora Agent Smoke achieved a median cost of $0.01 (mean $0.01), consumed 34.5k tokens (mean 34.5k), and completed in 16.5s (mean 16.5s). Quality was perfect at 100/100 on the rubric. The benchmark ran a single harness/model cell (claude-code / claude-haiku-4-5-20251001) in shootout mode with no baseline arm for comparison. The treatment was decided the winner on quality grounds.
The result
claude-code
- 1claude-code / claude-haiku-4-5-20251001100.0best
Overall score per cell, 0–100 points (not a pass rate): 30% goal + 30% quality + 25% efficiency (cost · tokens · duration, vs the board's best arm). Absent components renormalize. Best at the top — the board's best setup is tagged.
Leaderboard
Cells ranked by the composite score — outcome (the lenses that graded) × efficiency — absolute values, no baseline arm. Tokens, cost and duration are per-cell medians, the efficiency the score folds.
| # | Harness | Model | Score | Pass rate | Quality | Evals | Goal | Tokens | Cost | Duration | Runs |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | claude-code | claude-haiku-4-5-20251001 | 100.0 | 100% | 100 | − | 100% | 34.5k | $0.01 | 16.5s | 1/1 |
Pass rate
100%over 1 graded run| Cell | Pass rate | Tasks | Runs | Ran as | Matcher |
|---|---|---|---|---|---|
| claude-code/claude-haiku-4-5-20251001 | 100% | 1 | 1 | with my setup | normalized |
Each run's answer was extracted from its response and matched against the benchmark's own key — no model in the loop. Two-stage: the mean over repeats per task, then the mean over tasks, with the interval taken over tasks.
Quality × efficiency clusters — normalized across the study
Every graded run, standardized across the study — each task ran once, so there is no field per task to compare against: → right = fewer tokens than the study's average run, ↑ up = higher quality index (70% rubric pass rate + 15% intent + 15% outcome) than average. Every run scored alike on quality here, so the vertical axis separates nothing — read the map left to right. The field pools every harness and model, so a setup's left–right position largely reflects its own token habits. Dots are runs; each harness logo is a harness × model × arm centroid — hover it for the model and averages. Up-right wins.
claude-code
Every metric, per harness × model
Benchmark pass rate
- claude-code / claude-haiku-4-5-20251001100%
Task completion
- claude-code / claude-haiku-4-5-20251001100%
Goal achievement
- claude-code / claude-haiku-4-5-20251001100%
Rubric quality /100
- claude-code / claude-haiku-4-5-20251001100
Cost per run
- claude-code / claude-haiku-4-5-20251001$0.01
Tokens per run
- claude-code / claude-haiku-4-5-2025100134.5k
Duration per run
- claude-code / claude-haiku-4-5-2025100116.5s
Rubric criteria passed
- claude-code / claude-haiku-4-5-20251001100%
Your setup
The publisher has not described this setup — what each group's tools are, who built them, and how the tasks were chosen.
Distributions
Every completed run is one dot — the spread the averages hide. Click a dot to replay that run's journey.
Rubric quality per run
Token composition — average per run
Efficiency frontier
Statistics
| Metric | Arm | n | Mean | Median (pooled) | Min | Max | Std dev |
|---|---|---|---|---|---|---|---|
| Rubric quality | treat | 1/1 | 100 | 100 | 100 | 100 | − |
| Cost | treat | 1/1 | $0.01 | $0.01 | $0.01 | $0.01 | − |
| Duration | treat | 1/1 | 16.5s | 16.5s | 16.5s | 16.5s | − |
| Total tokens | treat | 1/1 | 34.5k | 34.5k | 34.5k | 34.5k | − |
| Output tokens | treat | 1/1 | 284 | 284 | 284 | 284 | − |
| Cache-read tokens | treat | 1/1 | 31.3k | 31.3k | 31.3k | 31.3k | − |
| Cache-write tokens | treat | 1/1 | 3k | 3k | 3k | 3k | − |
| Turns | treat | 1/1 | 2 | 2 | 2 | 2 | − |
n = runs carrying the fact / completed runs in the arm; every statistic runs over present facts only — a missing fact is never counted as 0. Std dev is the sample form (n−1), withheld below n = 2. Every figure here POOLS all runs. The typical-run figures in the abstract, the head-to-head decision and the per-setup panels take each task's median first and then the median across tasks, so a task that ran more often never outweighs one that ran once; the two medians can differ. A promise study's headline (“cut average tokens”) compares per-arm means.
What each setup did
Derived from each run's recorded tool calls — not from a model's description of the run — and aggregated per setup, so a behavior seen across several runs is stated once with its rate. Open a finding to see the runs behind it, each linked to its journey at the step where it happened.
claude-code · anthropic · claude-haiku-4-5-20251001 · Your setup1 finding
- 1 of 1 run · 1 completed
Task text withheld
Task text withheld — this benchmark is guarded and its tasks are not republished here.
Tasks
Task text withheld — this benchmark is guarded and its tasks are not republished here.
Runs
Every run is inspectable — open one to replay the agent's journey step by step, with the analyst's read underneath.
Every run, filterable… of 1Show runsHide runs
| Harness | Model | Task | Completed | Quality | Tokens | Cost | Duration |
|---|
Loading runs…
Methodology
What each metric means
- Completed
- The run finished and the harness returned a response. It does NOT mean the answer was correct — a completed run can score zero on quality.
- Denominator: Terminal runs, excluding those killed by our own infrastructure.
- Goal achievement
- The run fully achieved the task's goal: the judge panel passed every pre-registered rubric criterion (a rubric score of 100).
- Denominator: Graded completed runs.
- Rubric quality
- The share of pre-registered acceptance criteria the judge panel passed, as a 0-100 score. Partial credit is possible.
- Denominator: The criteria count frozen before any run — never the judge's returned count.
- Quality index
- A composite used only in the quality x efficiency plot, built from whichever grader scored this study's runs — the judge's rubric, the pre-registered checks or the benchmark's own verdict — plus whether the run's own outcome was success. The map's caption names the grader and its weights.
- Denominator: Graded runs — runs the study's grader scored.
- Pairwise verdict
- A blind, position-debiased comparison of the two arms' final answers. It sees answer text only — cost and latency are measured separately.
- Denominator: Task-cell pairs where both arms produced a response.
- Infra-excluded
- A run killed by our own infrastructure. It is a missing measurement, never a loss for the arm, and is excluded from every rate denominator.
- Denominator: Reported as a count beside every affected panel.
- Single arm
- Every task runs once per harness × model cell — a shootout with no baseline arm. The readout is absolute (quality, success, tokens, cost) and the cells are ranked into a leaderboard.
- Pre-registered rubrics
- Each task's acceptance criteria are written at task generation, before any run exists, so grading can never be shaped by the results.
- Judge panel
- A panel of independent judges (claude-opus-5, gpt-5.5, claude-fable-5), each at provider-default sampling (claude-opus-5, gpt-5.5 and claude-fable-5 do not accept a temperature setting), scores every successful response against its rubric; a criterion passes only when a strict majority of the panel passes it. Each judge sees only the task, the rubric, and the response — never which arm produced it, never token counts.
- One run per task
- Every task ran once per cell, in one phrasing. Run-to-run variation is therefore not measured: a single task's difference can be one lucky or unlucky attempt. Every row of the reference table treats the TASKS as the unit: its intervals resample the tasks and its p-values come from a paired test over them, so they describe how much the result depends on which tasks were drawn — not how it would change if the same tasks were run again. With few tasks no difference can reach significance (five tasks cannot go below p = 1/16).
- Sample size
- 1 cell × 1 task × 1 arm = 1 runs.