Benchmarks
AI-assisted development, finally reliable.
Cursor, Copilot, and Claude Code search your codebase like a basic word processor — they scan for matching text and miss most of the places that actually need to change. Momental gives your AI the full picture before anything is touched — in milliseconds.
Benchmark 1 · Results
How many places does AI find when you change a feature?
When you change how a feature works — its name, its parameters, its output type — every place in the codebase that uses it needs updating too. Without Momental, AI tools find those places by scanning for matching text. With Momental, AI knows the full map of relationships. We ran 20 tasks. These are the results on rename tasks — the scenario where missed updates cause the most broken builds.
- Change
claimFiles()signature — add requiredttlSecondsparam - Rename
getBlastRadius()return type fieldriskLevelfrom string to literal union - Add required
includeExternalparam to the impact analysis tool - Modify
searchSymbols()to return paginated results - Change
saveRecord()to accept newenvironmentparameter
Live Example · Real Codebase
Same question. Completely different answer.
Task: “Add a required parameter to a billing function — find every place in the codebase that calls it.” Run on the same production codebase as Benchmark 1. 1,130 source files.
Search: "recordAsync(" across all files - 1 line is the function definition itself — requires manual triage to remove
- No grouping by caller — the billing service appears 9 times in separate lines with no context
- Different implementations of the same function are mixed together — no way to tell which is which
- No risk level, no test suggestions
Look up “recordAsync” in code map - Each result is a named function — zero manual triage
- Risk level: critical — billing path, 441 files affected downstream
- Exact tests to run: 3 specific test files identified automatically
- A second implementation in the worker is flagged separately — won’t be missed
Measured live · April 2026 · Momental’s production codebase · 1,130 source files. Standard search: full repository text scan, no path scoping. Momental: two API calls, 185ms + 237ms = 422ms total.
External Validation · 379k lines
Works on any codebase from day one.
We ran the same test on the Microsoft TypeScript compiler — 379,000 lines of code, cold start, never previously indexed. Same result.
20 of 20 tasks measured · April 2026 · 13m 16s total · Microsoft/TypeScript · 601 source files · 379k lines. Standard search: full repository, cold start, no path scoping. Momental: ~275ms lookup + ~227ms callers = ~502ms total.
Benchmark 2 · Results
Does AI make better decisions when it knows your team’s history?
When your team has already decided how to build something — which database pattern to use, how to handle billing, which internal tools to use — your AI should know that. Without Momental, it gives generic advice based on public documentation. With Momental, it recalls your team’s actual past decisions. We asked 5 real architectural questions. An independent AI graded each answer from 1 to 10.
The biggest gain is on questions where the right answer depends on your team’s specific rules. The AI knows the general best practice — but not that your team decided to always use the internal billing-aware wrapper instead of calling the vendor API directly. Momental surfaces that past decision. With it, the AI scores 9.2/10. Without it, 6.0/10.
Methodology
How we score every task
Every task runs twice — once with standard search tools, once with Momental. We measure five things on every run. No cherry-picking. Same codebase, same questions.
Complete
Did the AI find every place in the codebase that needs to change? The correct answer is computed fresh each run, not hardcoded.
Works
Does the AI’s proposed change actually work? Verified by an independent AI judge since running a full compiler on every task would be too slow.
Speed
How long from receiving the task to a working answer. Momental adds a small lookup cost upfront but eliminates the back-and-forth caused by missed callers.
Data volume
How much information the AI processes. Momental sends a focused summary of exactly the relevant code instead of dumping dozens of files.
Quality
An independent AI grades the answer 1–10: is it correct, does it follow good practices, did it miss anything, does it introduce any new bugs?
Fairness rules
- Same AI model for all runs
- Same task descriptions — only the context injected differs
- Correct answer verified fresh each run, not hardcoded
- All raw scores published with results
- Codebase: the Momental monorepo (TypeScript, ~300k lines, real production code) + microsoft/TypeScript (379k lines, public OSS)
All numbers from a 20-task benchmark run completed April 2026 on one production codebase (Momental’s own monorepo). External validation on microsoft/TypeScript (379k lines, public OSS) completed April 2026 — see the TypeScript section above. Runner source code is available on request.
Under the hood
What Momental gives the AI
At the start of each session, Momental gives the AI five ways to understand your codebase — as a connected map of relationships, not just a list of files.
Find any function Look up any function, class, or feature by name. Returns exactly where it’s defined across the whole codebase.
See all connections A complete picture: everything that calls this function, everything it calls, and any linked past decisions.
Measure the impact Before changing anything, understand how many files are affected, what the risk level is, and which tests to run.
Search by meaning Find related code by concept, not just keyword. Finds relevant code even when the name is completely different.
Coordinate safely Tell other AI agents which files you’re editing. Prevents conflicts when multiple agents work the same codebase at once.
What we measure
Beyond code — the full picture
Momental combines code understanding with organizational memory — your team’s past decisions, patterns, and choices. These benchmarks measure both.
Full codebase map
Every function, every caller, across the whole codebase — not just the file that’s currently open. Nothing gets missed because the AI didn’t search the right folder.
Impact scoring
A risk rating before any code is changed. Know exactly which tests to run and which teams to notify — before anything breaks.
Team memory
Your team’s past decisions surface in context — not just what the code does, but why it was built that way and what the agreed rules are.
Multi-agent coordination
Live file claiming and conflict detection. Multiple agents working the same repo without stepping on each other.
Your AI deserves better context.
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