PBenchBrowse 284 families

PBench

Can the model plan, use tools, and finish a multi-step task?

Version / measure
Domain
Agents
Ranked models
11
Reported values
11
Reports
1 · lab
Direction
Higher is better
Status
Version not specified

Rankings

Best reported score per model across effort settings

11 models
#ModelBest settingScore
1Veo 3Googlereportedcost n/a1 value · Jun 15 · source0.526
2KlingExternal baselinereportedcost n/a1 value · Jun 15 · source0.521
3WowExternal baselinereportedcost n/a1 value · Jun 15 · source0.517
4LVPExternal baselinereportedcost n/a1 value · Jun 15 · source0.515
5Wan2.6External baselinereportedcost n/a1 value · Jun 15 · source0.514
6LTX-2External baselinereportedcost n/a1 value · Jun 15 · source0.506
7VidarExternal baselinereportedcost n/a1 value · Jun 15 · source0.501
8GigaWorldExternal baselinereportedcost n/a1 value · Jun 15 · source0.495
9Sora2OpenAIreportedcost n/a1 value · Jun 15 · source0.487
10CosmosNVIDIAreportedcost n/a1 value · Jun 15 · source0.470

Effort curve

Score vs. reported cost. Lines connect complete sweeps only.

0 values
No cost-linked effort sweep for this version.
Definition and comparison boundarypublic methodology

Can the model plan, use tools, and finish a multi-step task?

Agentic execution across tools, environments, or long-running workflows. Higher is better. This benchmark reports points rather than percent correct.

Harness, tools, prompts, attempt count, and benchmark version can materially change the result. Compare within one reporting context.

PBench source-defined quality, consistency, motion, and domain metrics.

Method / source ↗