Cursor vs Aider
Zero of fourteen models named Cursor first on the direct prompt; zero named Aider. Cursor was named by fourteen of the fourteen models and Aider by nine and Cursor carries 63 labels and Aider 14, so the shares are not directly comparable.
Named in three categories this edition.
Named in one category this edition.
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the AI coding assistants page.
By framing
CursorFirst choices, of fourteen modelsAider
Across every category in the November 2026 Edition, Cursor and Aider were named in the same answer seventeen times, of the 184 answers naming Cursor and the 20 naming Aider. In those answers Aider took the first choice one time and Cursor four.
Every model, every framing
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | | | ||||
| GPT-5.4 mini | | | | | ||
| Gemini 3.5 Flash | | | | | ||
| Perplexity Sonar | | | | | | |
| Grok 4.20 | | | | | | |
| Mistral Small | | | ||||
| DeepSeek V4 Flash | | | | | | |
| Llama 4 Maverick | | | | |||
| Qwen 3.7 Flash | | | | | ||
| Kimi K2 | | | | | | |
| GLM 4.7 FlashX | | | | | ||
| MiniMax M2.5 | | | | | ||
| GPT-6 Luna | | | | | ||
| Muse Glimmer 30B | | | | |
Cursor Aider first choice named as an alternative argued againstblank: not namedEach cell is one answer, Cursor on the left and Aider on the right.The direct prompt
Neither was the first choice, one was named
11 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
3 of 14 modelsThe answer made no first choice from these two in this category.Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.
By buyer segment
Cursor2%#4 of 11Aider2%#5 of 11What the models said about Cursor
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“Verdict: Use with extreme caution, especially without enterprise controls and Privacy Mode explicitly enabled.” Kimi K2 · negative prompt · hard negative
“Cursor's pricing page indicates model usage can be billed separately at list price for third-party models, which can make spend less predictable.” GPT-5.4 mini · budget prompt · soft negative
“Be particularly cautious with: Autonomous agent modes in tools such as Claude Code, Cursor, Codex, Aider, Devin, and Copilot Workspace” Perplexity Sonar · negative prompt · soft negative
“Pick Cursor or Windsurf if: You want the most seamless experience for multi-file refactoring and rapid feature generation” Gemini 3.5 Flash · comparative prompt · first choice
“If you want the most powerful AI experience and don't mind switching editors → Go with Cursor.” DeepSeek V4 Flash · comparative prompt · first choice
“Cursor is currently the dominant choice due to its seamless balance of power and ease of use” Qwen 3.7 Flash · comparative prompt · first choice
What the models said about Aider
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“Be particularly cautious with: Autonomous agent modes in tools such as Claude Code, Cursor, Codex, Aider, Devin, and Copilot Workspace” Perplexity Sonar · negative prompt · soft negative
“Start by installing Cline (in VS Code) or Aider (in terminal)” Gemini 3.5 Flash · budget prompt · first choice
“A free, open-source tool that is also vendor-independent and allows you to bring your own API key.” Llama 4 Maverick · budget prompt · alternative
“Best for: Terminal-centric developers who want lightweight, transparent AI assistance” DeepSeek V4 Flash · comparative prompt · alternative
Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.