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AI coding assistants · October 2026 Edition

GitHub Copilot vs Amazon Q Developer

Fourteen of fourteen models named GitHub Copilot first on the direct prompt; zero named Amazon Q Developer. GitHub Copilot was named by fourteen of the fourteen models and Amazon Q Developer by thirteen and GitHub Copilot carries 73 labels and Amazon Q Developer 38, so the shares are not directly comparable.

GitHub Copilot

endorsed leader

Named in two categories this edition.

Amazon Q Developer

accepted challenger

Named in two categories this edition.

First-choice share75%4%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.

Negative rate11%16%Negative labels as a share of the product's labels, 0 to 100.

Rank in category#1#4A position in a field of 9; printed, not drawn.

Labels7338A count; the two differ.

The two percentage rows are drawn on one 0 to 100 track, GitHub Copilot reading right to left. Rank and label count are printed, not drawn.Cursor was named alongside these two in eleven of the fourteen direct answers. GitHub Copilot vs Windsurf · GitHub Copilot vs Cursor · GitHub Copilot 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.

GitHub CopilotFirst choices, of fourteen modelsAmazon Q Developer

Direct140

Paraphrase140

Comparative70

Budget-constrained721 against GitHub Copilot

Scale-constrained30

Negative107 against GitHub Copilot · 5 against Amazon Q Developer

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.

Across every category in the October 2026 Edition, GitHub Copilot and Amazon Q Developer were named in the same answer 108 times, of the 234 answers naming GitHub Copilot and the 112 naming Amazon Q Developer. In those answers Amazon Q Developer took the first choice three times and GitHub Copilot sixty-six.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where GitHub Copilot and Amazon Q Developer stood in it.

Model	Direct	Paraphrase	Comparative	Budget-constrained	Scale-constrained	Negative
Claude Haiku 4.5
GPT-5.4 mini
Gemini 3.5 Flash
Perplexity Sonar
Grok 4.1 Fast
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

GitHub Copilot

Amazon Q Developer

first choice

named as an alternative

argued againstblank: not namedEach cell is one answer, GitHub Copilot on the left and Amazon Q Developer on the right.

The direct prompt

The plain question, one answer per model, grouped by where GitHub Copilot and Amazon Q Developer stood in it.

GitHub Copilot first, Amazon Q Developer an alternative

14 of 14 modelsAmazon Q Developer was named in the answer but not as the choice, or not at all.

Claude Haiku 4.5GitHub Copilot alternatives: JetBrains AI Assistant

GPT-5.4 miniGitHub Copilot alternatives: Cursor, JetBrains AI Assistant

Gemini 3.5 FlashGitHub Copilot alternatives: Augment Code, Cursor, Devin

Perplexity SonarGitHub Copilot alternatives: Augment Code, Claude Code, Cursor, Kilo Code

Grok 4.1 FastGitHub Copilot alternatives: Amazon Q Developer, Cursor, Gemini Code Assist, Tabnine

Mistral SmallGitHub Copilot

DeepSeek V4 FlashGitHub Copilot alternatives: Cursor, Windsurf

Llama 4 MaverickGitHub Copilot alternatives: Claude Code

Qwen 3.7 FlashCursor, GitHub Copilot alternatives: Claude Code

Kimi K2GitHub Copilot alternatives: Amazon Q Developer, Cursor, Tabnine

GLM 4.7 FlashXGitHub Copilot alternatives: Cursor, Tabnine, Windsurf

MiniMax M2.5GitHub Copilot alternatives: Cursor, Windsurf

GPT-6 LunaGitHub Copilot alternatives: Cursor, Gemini Code Assist

Muse Glimmer 30BGitHub Copilot alternatives: Amazon Q Developer, Augment 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

GitHub Copilot leads by sixty-three points.

GitHub Copilot#1 of 7

Amazon Q Developer#6 of 7

The full small business standing →

Mid-marketThe figures above

GitHub Copilot leads by seventy-one points.

GitHub Copilot#1 of 9

Amazon Q Developer#4 of 9

The full mid-market standing →

Enterprise

GitHub Copilot leads by seventy-five points.

GitHub Copilot#1 of 9

Amazon Q Developer#3 of 9

The full enterprise standing →

What the models said about GitHub Copilot

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.

“GitHub says it may use Copilot interaction data, including prompts, suggestions, and code snippets, to train and improve models for individual subscribers.” GPT-5.4 mini · negative prompt · soft negative

“GitHub may use individual subscribers’ interaction data—including prompts, outputs, code snippets, and context—to train models unless they opt out.” GPT-6 Luna · negative prompt · soft negative

“GitHub Copilot Free / Pro / Pro+ tiers. Training data opt-out required, deep IDE and repository integration increases exposure” Muse Glimmer 30B · negative prompt · soft negative

“The Safe & Scalable Choice: GitHub Copilot ... Go with GitHub Copilot if your priority is security, SSO compliance, and zero friction” Qwen 3.7 Flash · direct prompt · first choice

“Prefer autocomplete and chat plugins (like GitHub Copilot or Gemini Code Assist) for low-risk, low-autonomy use cases.” Mistral Small · negative prompt · first choice

“GitHub Copilot if you want the most established solution with strong security compliance and already use GitHub” MiniMax M2.5 · direct prompt · first choice

What the models said about Amazon Q Developer

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.

“Lower Priority/Avoid if Possible: Amazon Q Developer ... End-of-support looming ... migrate from Amazon soon” Grok 4.1 Fast · negative prompt · hard negative

“extracted 2,702 valid credentials from GitHub Copilot and 129 from Amazon CodeWhisperer using clever prompts” Muse Glimmer 30B · negative prompt · soft negative

“Ranked second lowest at 72.38 in the privacy scorecard. Also flagged in credential extraction research.” Muse Glimmer 30B · negative prompt · soft negative

“"For budget-conscious teams, Continue.dev and Amazon Q Developer offer excellent value."” Muse Glimmer 30B · budget prompt · first choice

“I'd start with Amazon CodeWhisperer or Tabnine since they're free” MiniMax M2.5 · budget prompt · first choice

“| Amazon Q Developer (ex-CodeWhisperer) | Yes (generous for individuals/AWS) | $19/mo Pro | AWS-heavy teams” Grok 4.1 Fast · budget prompt · alternative

Also compared

GitHub Copilot vs Windsurf GitHub Copilot vs Cursor GitHub Copilot vs Tabnine Windsurf vs Amazon Q Developer Cursor vs Amazon Q Developer Amazon Q Developer vs Tabnine All ten head-to-head pages and the full AI coding standing

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.