How to Use CursorBench When Picking a Model Inside Cursor

When Cursor changes a default model, developers ask for a leaderboard. CursorBench is Cursor's answer — a first-party evaluation of models inside Cursor's agent loop, not a reproducible public harness you can run at home. The AI library mirrors Cursor's full public CursorBench 3.2 table — score, cost, tokens, and steps, with a Cost / Tokens / Steps chart — on the CursorBench board. Use it to frame choices; confirm them on your repositories.

What CursorBench measures — and what it does not

The benchmark snapshot on this site documents:

• Primary axis shown: correctness on ambiguous multi-file IDE agent work drawn from real Cursor sessions.
• Publisher: Cursor (first-party), updated on the board's updatedAt field.
• Coverage note: Cursor also evaluates quality, efficiency, and interaction, and runs online traffic experiments — those axes are not fully ranked on the public board transcribed here.

The companion Cursor eval axes page explains the multi-axis system without inventing a second leaderboard. Efficiency and interaction scores stay on Cursor's side of the wall unless they publish a downloadable ranking.

That scope matters. A model that ranks highly on CursorBench is a candidate default for Cursor's agent surface. It is not automatically the best model for raw API chat, terminal agents, or batch jobs outside the IDE loop.

How to read the catalogued shortlist

The CursorBench board mirrors the full public table from cursor.com/cursorbench (see the board's coverage note for the mirror date), including reasoning tiers and Cursor-only rows such as Composer. A separate correctness-only shortlist is no longer the board UX — use the chart toggles and the single table.

Two editorial cautions ship with the data:

1. Training-data disclosures — treat a rank as directional when Cursor publishes a row-level training-data note. This site's current CursorBench 3.2 mirror has no such marked rows.
2. Mid-table gaps — treat ordering between closely spaced scores as noise until you replay tasks locally.

Link each row to its model profile for verified pricing, context, and verifiedAt before you change spend.

A practical model-pick workflow inside Cursor

Step 1 — Start from the board, not from memory. Open CursorBench and note which catalogued models cluster at the top for correctness. Cross-check the Cursor harness profile for pricing model and editor-switch requirements.

Step 2 — Match model tier to task class. Frontier models on the board may be overspecified for completion-heavy days. Budget tiers may fail on long-horizon agent tasks. The GPT-5.6 tier guide applies when you route OpenAI models inside Cursor — pin full model IDs, especially for Luna versus Sol aliases documented on the Luna profile.

Step 3 — Replay three real tasks. Small bug, cross-file feature, dependency migration. Measure review time and steering turns. The best AI for coding guide recommends this protocol for any harness; CursorBench does not replace it.

Step 4 — Check online product changes separately. Cursor's post describes online evals tied to product metrics inside Cursor. Those are not transcribed as a public leaderboard here. If a default model changes, read Cursor's announcement and rerun your repo trials.

When not to use CursorBench

• Choosing between Cursor and a terminal agent — compare harnesses on the AI harnesses index and switch guides like Cursor vs Muse Code, not CursorBench alone.
• API-only workloads — use model profiles and cross-provider comparisons on /ai-models, or boards with explicit public harnesses on /ai-models/benchmarks.
• Team procurement — mixed-editor teams may standardise on GitHub Copilot regardless of CursorBench; editor coverage beats an IDE-only eval.

Where the library fits together

Question	Start here
Which models top Cursor's agent eval?	CursorBench board
What axes exist beyond correctness?	Cursor eval axes
Is Cursor the right harness?	Cursor profile + coding task guide
OpenAI tier inside Cursor	GPT-5.6 Sol / Terra / Luna profiles

CursorBench is a useful inside-Cursor shortlist. The pick that sticks is still the one that passes your tests, stays within budget at your concurrency, and survives week-four retention — not the top row on a vendor-run board copied into a blog post.