Index › IT › Developer platform › AI coding › Tabnine vs Cline
AI coding assistants · November 2026 Edition

Tabnine vs Cline

Zero of fourteen models named Tabnine first on the direct prompt; zero named Cline. Tabnine was named by twelve of the fourteen models and Cline by eight and Tabnine carries 39 labels and Cline 11, so the shares are not directly comparable.

Tabnine

accepted challenger

Named in one category this edition.

Cline

accepted challenger

Named in one category this edition.

First-choice share2%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate3%9%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#3#6A position in a field of 11; printed, not drawn.
Labels3911A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Tabnine reading right to left. Rank and label count are printed, not drawn.GitHub Copilot was named alongside these two in thirteen of the fourteen direct answers. GitHub Copilot vs Tabnine · GitHub Copilot vs Cline · Windsurf vs Tabnine

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

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
TabnineFirst choices, of fourteen modelsCline
Direct00
Paraphrase00
Comparative00
Budget-constrained11
Scale-constrained00
Negative201 against Tabnine · 1 against Cline
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where Tabnine and Cline stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
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 Cline first choice named as an alternative argued againstblank: not namedEach cell is one answer, Tabnine on the left and Cline on the right.

The direct prompt

The plain question, one answer per model, grouped by where Tabnine and Cline stood in it.

Neither was the first choice, one was named

5 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Gemini 3.5 FlashGitHub Copilot alternatives: Cursor, Tabnine, Windsurf
Perplexity SonarGitHub Copilot alternatives: Claude Code, Cursor, Tabnine
Kimi K2GitHub Copilot alternatives: Amazon Q Developer, Tabnine
GLM 4.7 FlashXGitHub Copilot alternatives: Tabnine, Windsurf
Muse Glimmer 30BGitHub Copilot alternatives: Amazon Q Developer, Cursor, Tabnine

Neither was named

9 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5Claude, GitHub Copilot
GPT-5.4 miniGitHub Copilot Business or Enterprise alternatives: Amazon Q Developer, Cursor
Grok 4.20GitHub Copilot alternatives: Amazon Q Developer, Anthropic, Claude Code, Cursor, Gemini Code Assist
Mistral SmallGitHub Copilot alternatives: Claude Code, Cursor, Superblocks
DeepSeek V4 FlashGitHub Copilot alternatives: Amazon Q Developer, Claude Code, Cursor, Sourcegraph Cody
Llama 4 MaverickGitHub Copilot alternatives: Claude Code, Cursor, Windsurf
Qwen 3.7 FlashGitHub Copilot alternatives: Anthropic, Augment Code, Claude Code, Cursor
MiniMax M2.5GitHub Copilot alternatives: Amazon Q Developer, Claude Code, Cursor, Sourcegraph Cody
GPT-6 LunaGitHub Copilot alternatives: Claude Code, Cursor

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

The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
Cline leads by two points.
Cline2%#4 of 9
Tabnine0%#6 of 9
The full small business standing →
Mid-marketThe figures above
Level: the same share of first choices.
Tabnine2%#3 of 11
Cline2%#6 of 11
The full mid-market standing →
Enterprise
Cline is not named for this buyer.
Tabnine0%#4 of 9
Cline—not named
The full enterprise standing →

What 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 Cline

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 cautious about these types of tools in sensitive environments... Tools like Cline” GLM 4.7 FlashX · negative prompt · soft negative
“Start by installing Cline (in VS Code) or Aider (in terminal)” Gemini 3.5 Flash · budget prompt · first choice
“Lowest-cost flexible option: Cline or Aider — The tools are free, but the company pays separately for model usage” Perplexity Sonar · budget prompt · alternative
“Winners: Cline / Aider + Open Source Models ... You install tools like Cline or Aider. These run locally.” Qwen 3.7 Flash · budget prompt · alternative
Also compared

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.