Not how it’s built
How it works
Search, retrieval, review, visualization and history are all solved at the code layer, by good tools. Among the public capabilities reviewed, these systems primarily expose code-, change-, review- or workflow-centred representations. GitMir makes a standing product-behaviour model the core product. This page states the boundary, dimension by dimension, and shows how to check it.
The layer argument explains the gap
The benchmark is the gap
The rest of this page is a careful description of where each product operates. It exists because people ask. But the argument that decides it is a measurement: one repository, fifty questions in plain English, the model and settings held constant, answers graded blind.
One question separates the two layers
Ask any tool in this market: “if I change this field, what does the customer see differently, and what happens downstream?” A product model answers this directly as structured product behaviour; code- and change-centred systems may infer parts of the same answer from their own context.
What the code layer holds
Files, symbols, references, commits, pull requests. These are units of construction — the record of how software was built. They are precise, they are cheap to compute from source, and every tool below is excellent at them.
What they do not state: which rule a function enforces, what a screen is for, how an order is allowed to behave, or which of those things money and personal data flow through.
What the product layer holds
What the product does and how it behaves — established once, checked, and traceable: every statement names the files it was read from.
Because behavior is established rather than inferred, impact is computed instead of guessed, the same answer comes back twice, and a person can review the claim by opening the file it cites.
An agent given retrieved code fragments reconstructs the product's rules on every task, privately, and differently each time — which is why review is archaeology and rework happens after the first version looked right. An agent given what the product actually does starts from something established once, checked, and reused.
Eleven products
6 dimensions
GitMir is intentionally the first row: the table compares layers, not quality. Every other product here is strong at the layer it names, and several are best in class there.
| Product | Primary layer | What it understands | Relationships | AI agent context | Change impact | Primary buyer |
|---|---|---|---|---|---|---|
| GitMir | Meaning / Product | What the product does and how it behaves | Business + technical | Established product knowledge | Product-logic level | CTO / VP Eng / Product / AI |
| Sourcegraph | Code Intelligence | Repos, files, symbols, references, history | Code graph | Strong | Code-level | Enterprise engineering |
| Augment Code | AI Context | Code, history, patterns, docs, tickets, knowledge | Cross-repo / service | Core | Code-level | Developers / AI engineering |
| GitHub Copilot | Coding Agent | Repository semantic index + instructions | Code / repo | Strong | Limited | Developers |
| Cursor | AI IDE | Semantic code index | Code semantic | Core | Limited | Developers |
| Unblocked | AI Context | Code, conversations, tickets, docs, analytics, production | Cross-source graph | Core | Code-level | Developers / AI engineering |
| Greptile | Code Review | Files, functions, classes, dependencies | Code graph | Review agents | PR / code impact | Engineering teams |
| Qodo | Review / Governance | Code + PR + requirements + SDLC context | Repo / service | Strong | Review impact | Enterprise engineering |
| CodeRabbit | Agentic change management / review | PRs, repositories, cross-repository code, history, team learnings, business and change context | Codegraph + cross-repository | Strong — shared context across review and agent workflows | Change-centred semantic impact, architecture context and blast radius | Engineering teams and engineering leadership |
| Graphite | PR Workflow | PR + codebase context | Code / PR | Limited | PR impact | Engineering teams |
| CodeSee | Visualization | Files, folders, dependencies | Visual code graph | No | Dependency-level | Developers / architects |
| GitKraken GitLens | Git Intelligence | Commits, branches, authorship, worktrees | Git history | Limited | Git/change-level | Developers |
POSITIONING AS EACH VENDOR PUBLICLY DESCRIBES IT, AUGUST 2026. THE MARKET MOVES — IF A ROW IS OUT OF DATE, WRITE TO [email protected] AND IT WILL BE CORRECTED.
What each column actually measures
A comparison whose columns are undefined is marketing. These are defined, one by one — so any row, including ours, can be argued with.
Primary layer
What the tool is fundamentally about: code, review, workflow, history — or the meaning of the product. Everything else in the row follows from this.
What it understands
What the tool holds. Files, symbols and commits are the record of how software was built; what the product does and how it behaves is a different subject entirely.
Relationships
How far a connection can be followed. Code-level tools connect callers to callees; the product layer also reaches the behaviour a change alters and the customers who meet it.
AI agent context
What an agent receives. Retrieved fragments require the model to reconstruct meaning on every request; established meaning arrives already resolved, with its sources attached.
Change impact
The altitude of the answer to “what does this break”. Code-level names files and symbols; product level names the behavior and the data a change reaches.
Primary buyer
Who signs. Developer tools are bought by developers; a product model is bought by the people accountable for product decisions and their cost.
What each product is for
Written without a straw man
Each note says what the product does well and where its layer ends. If you already own one of these, the question is not whether to replace it — it is what it cannot tell you.
GitMir establishes what a product does once, from the repository, and serves each task only what it needs — with every statement traceable to the source it came from.
The reference implementation of code intelligence at scale: search, precise references and history across very large monorepos, now with agentic tooling on top. Its graph is symbols and their relationships, so it answers “where is this used” with authority — and leaves “which customer path breaks if this changes” to the reader.
Built around retrieval quality for large codebases: index everything the team has — code, history, docs, tickets — and give the model the most relevant slice. The context is assembled per prompt and inferred; there is nothing standing and inspectable that a person could review or a commit could be checked against.
The default coding agent for most teams, with the distribution to match. It works from a semantic index of the repository plus written instructions, which is enough for local work and weakest exactly where systems are coupled — the distant links between areas that no single file states.
The editor where AI-assisted work actually happens for a large share of developers. Its context is a semantic index of code, rebuilt per task; the product's rules and states are re-inferred every time rather than held anywhere the whole team — or the next agent — can read.
Describes itself as “the context layer for agentic software development”: one graph over every source a team already has — repositories, conversations, tickets, documentation, analytics and production — traversed at once instead of searched silo by silo. The graph is of the sources; what the product does and which rule holds which state is left for the model to infer from them on each request.
Reviews pull requests against a graph of the codebase, catching what a diff alone hides. The reasoning happens at review time and is scoped to the change; the understanding it builds is spent on that PR rather than accumulated as a product model.
Reaches further than review alone by pulling requirements and process context into the loop — the closest of this group to asking what a change is for. The connection is made per change, against tickets and specs, not against a standing model of how the product behaves.
Has expanded from AI code review into agentic change management, combining review, triage, semantic change understanding, security and broader codebase context. It overlaps with GitMir on context, planning, dependencies and change impact. The distinction is where the asset sits: CodeRabbit's intelligence is organised around a change, GitMir's around the product, and the product model exists before any particular issue or pull request.
Makes stacked changes and the flow of shipping them fast and orderly. Its subject is the pipeline, not the software's meaning — a complement to a product model, never a substitute for one.
Draws the codebase — files, folders and dependencies — so structure can be seen instead of imagined. The picture is of the code's shape; the diagram of what the product does is a different drawing.
The clearest view of history: who changed what, when, and on which branch. History explains how the code arrived at today's state; it does not state what the product does today.
GitMir vs Claude Code →GitMir vs Cursor →GitMir vs GitHub Copilot →GitMir vs Codex →GitMir vs Windsurf →GitMir vs Devin →GitMir vs Cline →GitMir vs Tabnine →GitMir vs JetBrains AI →GitMir vs Amazon Q Developer →GitMir vs Augment Code →GitMir vs Sourcegraph →GitMir vs Unblocked →GitMir vs CodeRabbit →GitMir vs Greptile →GitMir vs Qodo →What business logic is →
Do not take the first row on faith
Every claim on this page is checkable without talking to us. That is the point of publishing models instead of screenshots.
The Supabase model
459,339 lines of a production codebase, answerable. Ask it what a change breaks, how billing behaves, why something works the way it does — and read the evidence behind the answer.
Open the case study →The Codex model
OpenAI's coding agent modeled: 603,461 lines, answerable across the whole agent — 84 of 84 interpretation tasks completed with zero integrity failures.
Open the case study →The benchmark
The repository, the commit, the model, the settings, the questions and the scoring — everything needed to run the same test against whatever you use today.
Check it yourself →The five that decide it
Partly. CodeRabbit competes directly for engineering budget and overlaps with GitMir on context, planning, dependencies and change impact. Its platform is centred on managing software changes — review, triage, explainability and security. GitMir is centred one layer earlier: a persistent Product Intelligence asset that exists independently of any pull request or task and can be used by people and by several AI systems at once. The products can overlap, and can also be used together.
No — GitMir operates one layer above them and feeds them. Those tools read and write code; GitMir holds the model of what the product does and hands each of them the task-scoped slice they need. Teams run GitMir with their existing IDE, review bot and code search, not instead of them.
A code graph connects symbols: functions, files, imports, references — it answers "where is this used". GitMir holds what the product does and how it behaves, so the question it answers is "which customer experience breaks, what is affected, and what happens next". Different question, different altitude.
Generated documentation is prose that drifts. What GitMir establishes is checked, traceable back to the files it was read from, and kept current as the code changes — and what cannot be established is marked unknown rather than invented. You can open any statement and see the source behind it.
Eight models are published in full, among them Supabase Studio (459,339 lines), OpenAI Codex (603,461 lines), Cal.com (365,422 lines) and Atlas (173,995 lines). Each one is live on the site: walk the drawing, open any part of it and put your own questions to it, with the evidence behind every answer.
Editing code, running CI, gating pull requests, managing branches and stacking changes, storing history. Those are complements. GitMir does not replace an IDE, a review bot, a git client or a build system — it gives all of them a shared model of the product to work against.