WorldModelBenchBrowse 284 families

WorldModelBench

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 · source2.52
2Wan2.6External baselinereportedcost n/a1 value · Jun 15 · source2.50
3Qwen-RobotWorldQwenreportedcost n/a1 value · Jun 15 · source2.33
4Sora2OpenAIreportedcost n/a1 value · Jun 15 · source2.21
5CosmosNVIDIAreportedcost n/a1 value · Jun 15 · source2.14
6GigaWorldExternal baselinereportedcost n/a1 value · Jun 15 · source2.13
7WowExternal baselinereportedcost n/a1 value · Jun 15 · source2.05
8LVPExternal baselinereportedcost n/a1 value · Jun 15 · source2.01
9LTX-2External baselinereportedcost n/a1 value · Jun 15 · source1.97
10VidarExternal baselinereportedcost n/a1 value · Jun 15 · source1.62

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.

WorldModelBench instruction-following score on the source's 0–3 scale.

Method / source ↗