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Benchmarks · agentic

MCP Atlas (Scale AI)

How reliably a model completes realistic multi-step workflows over real Model Context Protocol servers: discovering the right tool in a noisy tool menu, calling it with correct parameters, recovering from errors and synthesising the results into an accurate final answer. Maintained by Scale AI. Score: Pass rate in percent over all 1,000 tasks; the published ± is the confidence half-width.

30 models · 30 results · data as of 2026-10-29 · labs.scale.com ↗

Leaderboard

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30 of 30 selected
Muse Spark 1.1 max

All results

30 rows
#ModelEffortMCP Atlas (Scale AI)
1Muse Spark 1.1max88.1%
2Fable 5.1max87.2%
3Opus 5xhigh85.8%
4Qwen 3.8xhigh84.5%
5GLM 5.3max84.2%
6Gemini 3.5 Flashhigh83.6%
7Fable 5max83.3%
8Kimi K3max82.3%
9Muse Spark82.2%
10Opus 4.8max82.2%
11GPT-5.6 Solmax81.8%
12Inkling Smallmax79.2%
13Opus 4.7max79.1%
14Gemini 3.1 Prohigh78.2%
15Opus 4.6max76.8%
16Inklingxhigh76.0%
17GPT-5.5xhigh75.3%
18GPT-5.4xhigh70.6%
19Gemini 3 Prohigh70.3%
20Opus 4.5high69.8%
21Sonnet 4.6max69.5%
22GPT-5.2xhigh67.6%
23Nemotron 3 Ultramax63.1%
24Gemini 3 Flashhigh62.0%
25Sonnet 4.5high59.5%
26Gemini 3.1 Flash-Litehigh57.1%
27GPT-5.4 Minixhigh56.7%
28GPT-5.1high50.1%
29o3 prohigh44.5%
30Haiku 4.5xhigh40.2%