Reported in recent model launches

These model reports include this benchmark. Their harnesses, task subsets, scoring policies, and resource budgets can differ; follow each report’s evaluation notes before joining the numbers.

Kimi · Kimi K3.1 Pro →

What it measures

Multi-tool tasks examine how an agent discovers, selects, and coordinates capabilities exposed through MCP.

How to read the result

Separate choosing the right tool from successfully completing the end-to-end task. Inspect recovery from ambiguous or failed tool results.

Limits and comparison traps

Kimi reports a specific public subset, turn budget, and judge. Results using other subsets or judging models need separate comparison cohorts.

Published evaluation

The linked publication documents the task and scoring procedure. Model results retain the source’s evaluation setting, native score units and available uncertainty.

Where this fits in an evaluation suite

A transcript that says “done” is weaker evidence than the verified final state. Track policy violations, recoveries, and invalid attempts alongside success.

Start with a capability hypothesis, then choose a second test with a different failure mode. Keep the checkpoint, reasoning effort, agent tools, sampling policy, and scoring procedure fixed when measuring a training intervention. Record failures and missing outputs in the denominator.

A reproducible comparison

Pin the benchmark release, task split, model configuration, tools, sampling budget, and evaluation harness described by the primary source.

Publish the exact run configuration, item coverage, score units, and available uncertainty. If a source supplies only an aggregate or a single run, preserve that limit. A new model name or a rounded score alone does not establish a capability improvement.

PRIMARY EVIDENCE & FURTHER READING

  1. Scale Labs — evaluation methodology and results ↗

    Scale Labs / SEAL · 2026

  2. Introducing Kimi K3.1 Pro ↗

    Moonshot AI · 2026

Reviewed 2026-10-26. Explanation and interpretation by RLTasks. Benchmark ownership remains with its original authors.