Tabnine vs Cursor
Zero of fourteen models named Tabnine first on the direct prompt; zero named Cursor. Tabnine was named by twelve of the fourteen models and Cursor by fourteen and Tabnine carries 39 labels and Cursor 63, so the shares are not directly comparable.
Named in one category this edition.
Named in three categories 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
TabnineFirst choices, of fourteen modelsCursor
Across every category in the November 2026 Edition, Tabnine and Cursor were named in the same answer seventy-nine times, of the 106 answers naming Tabnine and the 184 naming Cursor. In those answers Cursor took the first choice nine times and Tabnine five.
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 | | | | |
Tabnine Cursor first choice named as an alternative argued againstblank: not namedEach cell is one answer, Tabnine on the left and Cursor on the right.The direct prompt
Neither was the first choice, one was named
13 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
1 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
Tabnine2%#3 of 11Cursor2%#4 of 11Tabnine0%#4 of 9Cursor0%#7 of 9What the models said about Tabnine
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“cloud versions still risk data exfil. Some studies show similar vuln introduction rates.” Grok 4.20 · negative prompt · soft negative
“Explicitly commits to not storing or training on user code, even in free tiers. Supports fully air-gapped deployments.” DeepSeek V4 Flash · negative prompt · first choice
“Cursor (free tier or Pro) or Tabnine (free tier) are excellent starting points” MiniMax M2.5 · budget prompt · first choice
“Self-hosted/air-gapped options; explicit "code never trains our models" policy” Kimi K2 · negative prompt · first choice
What 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
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.