ToolathlonBrowse 284 families

Toolathlon

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

Version / measure
Domain
Agents
Ranked models
11
Reported values
12
Reports
2 · lab
Direction
Higher is better
Status
Version not specified

Rankings

Best reported score per model across effort settings

11 models
#ModelBest settingScore
1Claude Fable 5Anthropicreportedcost n/a1 value · Jul 9 · source61.7%
2Claude Mythos 5Anthropicreportedcost n/a1 value · Jul 9 · source61.7%
3Claude Mythos PreviewAnthropicreportedcost n/a1 value · Jul 9 · source61.1%
4Claude Opus 4.8Anthropicreportedcost n/a1 value · Jul 9 · source59.9%
5GPT-5.6 SolOpenAIreportedcost n/a1 value · Jul 9 · source58.0%
6Gemini 3.5 FlashGooglereportedcost n/a1 value · May 19 · source56.5%
7GPT-5.5OpenAIreportedcost n/a2 values · Jul 9 · source55.6%
8GPT-5.6 LunaOpenAIreportedcost n/a1 value · Jul 9 · source53.4%
9GPT-5.6 TerraOpenAIreportedcost n/a1 value · Jul 9 · source53.1%
10Gemini 3 FlashGooglereportedcost n/a1 value · May 19 · source49.4%

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. The value is the percentage reported in this lab's table.

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

Method / source ↗
Toolathlon · Benchmaxxer