Integration Benchmark

Which AI models can reliably automate enterprise integrations? Real-world results for building production-grade system connections.

Date: 3rd of August 2025

Why This Benchmark Exists

Can AI reliably build production integrations?

Enterprises waste billions on system migrations because brittle glue code breaks when systems change. AI vendors claim they can automate this work - we tested whether they actually can.

This benchmark measures how well different AI models handle real enterprise integration tasks - the kind your teams spend months building during migrations and system upgrades.

Current benchmarks test if models can write poetry or solve riddles. We test if they can connect your CRM to your billing system, migrate data between platforms, and maintain integrations when APIs change. That's what matters for enterprise transformation.

Results

Best AI Models for Integrations

Success rate at building production-grade system integrations automatically:

Rank LLM Success Rate

[1] superglue is an enterprise integration platform designed specifically for automated system migrations, not a general-purpose AI model

API Rankings

Best Integration-Ready Enterprise APIs

Which enterprise systems can AI integrate automatically without manual configuration?

Prompts we tested:

Slack: "Find user ID by email, then send direct message"
JIRA: "Get sprint issues, calculate completion %, identify blocked/high-priority items"
Notion: "Query database, find duplicate emails, return count and list"
Rank API Score superglue claude-4-sonnet claude-4-opus gpt-4.1 o4-mini gemini-2.5-flash
Insights

Key Findings

  • 84% vs 50-62%: Purpose-built integration platforms outperform general AI models by 30+ percentage points
  • Only 6 enterprise systems work reliably: Well-documented APIs with clear schemas enable automated integration
  • Complex migrations fail: Most AI models can't reliably chain system calls needed for real enterprise workflows
Best Practices

What Makes Systems Integration-Ready

  • Clear endpoints: /users/123 not /v2/entities?type=user&id=123
  • Standard auth: OAuth, Bearer tokens, API keys in headers
  • Real error messages: "User not found" not "Error 1047"
  • Consistent responses: Same structure every time
  • No custom query languages or weird filters
Methodology

How We Tested

TL;DR: We tested 21 enterprise systems across 6 different AI models.

Out of 630 integration attempts (21 systems × 6 platforms × 5 attempts each):

  • 23% failed completely - AI couldn't build even basic system connections automatically
  • Only 6 systems integrated 100% reliably across all AI platforms tested
  • Legacy API patterns block automation: Custom query languages and proprietary schemas require extensive manual configuration
  • Specialized platforms outperform by 30+ points: Purpose-built integration platforms handle enterprise complexity better than general AI

Note: superglue is an enterprise integration platform designed specifically for automated system migrations, not a general-purpose AI model. We included it to demonstrate the performance gap between specialized integration tools and general language models for enterprise use cases.

All evaluation code is open source. Check out the full benchmark implementation on GitHub to run your own tests or contribute new enterprise systems.