AI Architect tops SWE-Bench Pro

A benchmark based evaluation of how deep codebase context improves coding agent success on large, complex, real world codebases.

Evaluated on SWE-Bench Pro. Conducted by The Context Lab

TASK SUCCESS RATE

43.6%

Claude Sonnet 4.5
Without context

60.8%

With codebase context

LARGE CODEBASES

3.8x

COMPLEX TASKS

4.5x

Even advanced coding agents resolve fewer than 45% of tasks when changes span large codebases and require coordinated, multi-file updates. These long-horizon scenarios expose a gap in system-level reasoning that most coding agents lack today.

This evaluation examines whether structured codebase context can close that gap. Conducted by Bito on SWE-Bench Pro, it compares identical agent runs with and without Bito’s AI Architect MCP enabled, isolating the impact of system-level codebase context on real-world software engineering tasks.

Performance gains increase sharply with complexity

Large repositories, multi-file changes, and long-horizon tasks see the biggest lift—where agents must reason across dependencies, not just edit isolated code.

3.8x

Large codebases

Biggest gains on repositories with 1.5M+ lines of code.

4.5x

Multi-file changes

Tasks spanning 10+ files see sharply higher  success.

Critical issues

Performance, security, and cross-component issues see the strongest lift.

Success rate increases with code change complexity

Task resolution by file-change complexity

As tasks span more files, standalone models drop off sharply, while AI Architect continues to resolve complex changes.

Higher success. More efficiency. Same or lower cost.

Faster task completion

~20% faster

Average task duration drops ~377s → ~300s.

Fewer tool calls

~25% fewer

Less exploration and navigation per task.

No added cost

Cost-neutral

AI costs remain flat despite higher success rates.

Efficiency scales with complexity

Time + cost benefits increase with large, multi-file tasks.

On a complex refactoring task in the webClients repository (~720 MB), the underlying architecture spans thousands of files, with fragmented calendar logic across utilities, recurrence rules, alarms, encryption, and mail integrations.  
With deep codebase context, the agent successfully completed the refactor, delivering 58K+ lines of changes across 412 files and passing all tests. The baseline Claude Sonnet 4.5 agent failed to complete the task.

System-level codebase context becomes decisive on large, multi-file refactors where local reasoning fails.

With Bito’s AI Architect

Claude Sonnet 4.5 (baseline)

EFFICIENCY GAINS

27% faster

50% fewer tool calls

44% lower cost

How AI Architect works

Models generate code. Systems require reasoning.
AI Architect delivers structured codebase context at runtime via MCP, so coding agents understand dependencies and system impact, unlocking measurably higher success on complex engineering tasks.

Evaluated on SWE-Bench Pro.  
In collaboration with The Context Lab*

No code storage or model training. End-to-end data encryption. Enterprise-ready.

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*Note: This evaluation was conducted by The Context Lab, an independent 3rd party that performs agent evaluations in a tightly controlled measurement environment.