| Category | Function | Share | Rank | Negative rate | Labels | Quadrant | Since September 2026 |
|---|---|---|---|---|---|---|---|
| AI coding assistants | Developer platform | 75% | 1 of 48 | 11% | 73 | endorsed leader | ▲+8Since September 2026: 67% → 75%, +8 points. Inside the 11-point floor: within noise. Read over the models both editions asked. |
| Enterprise AI assistants | Data platform | 0% | 145 of 145 | 71% | 7 | under 10 labels · led by ChatGPT at 25% |
| Category | September 2026 | Now | Change | Reading | Rank |
|---|---|---|---|---|---|
| AI coding | 67% | 75% | ▲+8Since September 2026: 67% → 75%, +8 points. Inside the 11-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 1 of 48, unchanged |
| Enterprise AI | 0% | 0% | newNew since September 2026: not ranked then, 0% now. | New this edition | Rank 145 of 145, unchanged |
Shares here are read over the models both editions asked, so they can differ by a point or two from the standing above, which counts every model in this edition.
The floor is 11 points of share, measured: how far the models move a leader on their own when the same questions are asked twice with nothing changed. A larger change is movement; a smaller one is noise, and both are shown. Movement is read over the twelve models both editions asked; GPT-6 Luna, Muse Glimmer 30B joined this edition and are in the standing but not yet in the comparison. How the floor is measured · The editions
| Model | First choice | Alternative | Mention | Negative | Labels |
|---|---|---|---|---|---|
| Claude Haiku 4.5 | 3 | 2 | 0 | 2 | 7 |
| GPT-5.4 mini | 4 | 2 | 0 | 1 | 7 |
| Gemini 3.5 Flash | 3 | 2 | 0 | 1 | 6 |
| Perplexity Sonar | 3 | 1 | 0 | 1 | 5 |
| Grok 4.20 | 3 | 1 | 1 | 2 | 7 |
| Mistral Small | 5 | 0 | 2 | 0 | 7 |
| DeepSeek V4 Flash | 4 | 1 | 0 | 1 | 6 |
| Llama 4 Maverick | 2 | 1 | 1 | 0 | 4 |
| Qwen 3.7 Flash | 2 | 2 | 0 | 1 | 5 |
| Kimi K2 | 4 | 0 | 1 | 1 | 6 |
| GLM 4.7 FlashX | 3 | 1 | 0 | 0 | 4 |
| MiniMax M2.5 | 4 | 0 | 1 | 0 | 5 |
| GPT-6 Luna | 4 | 0 | 0 | 1 | 5 |
| Muse Glimmer 30B | 2 | 2 | 0 | 2 | 6 |
Verbatim evidence the judge attached to positive labels.
“The Safe & Scalable Choice: GitHub Copilot ... Go with GitHub Copilot if your priority is security, SSO compliance, and zero friction” Qwen 3.7 Flash · AI coding · 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 · AI coding · negative prompt · first choice
“GitHub Copilot if you want the most established solution with strong security compliance and already use GitHub” MiniMax M2.5 · AI coding · direct prompt · first choice
“1. GitHub Copilot - Best for: Daily autocomplete in any IDE, wide language support, and fast completions” Mistral Small · AI coding · comparative prompt · first choice
Verbatim evidence attached to negative labels. A warning on a product with few labels is a warning; on a product with many, it is one voice among them.
“Amazon Q Developer and GitHub Copilot are cloud-only and therefore unsuitable for strict air-gapped environments” Perplexity Sonar · Enterprise AI · negative prompt · hard negative
“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 · AI coding · 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 · AI coding · 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 · AI coding · negative prompt · soft negative
Citations exist only for the models that return a source list, five of the fourteen in this edition, so these counts come from 216 of the 234 answers that named GitHub Copilot and are not a share of its labels.
404 of the 496 domain citations in answers naming GitHub Copilot came from somebody else's page.
Pages are listed as the models cited them.
Search figures are US estimates from DataForSEO, read September 28, 2026; AI search demand is its modeled, directional estimate, not a count of queries to any assistant. The answers are this edition's. Two measurements side by side: neither is read as the cause of the other.
An email the morning each edition publishes: where this product moved, where it held, and by how much against the noise floor. One address, confirmed by a click; a stop link in every email.
Already following? Everything you follow, with a stop for each.
What GitHub Copilot's own pages state, read October 5, 2026: github.com/features/copilot, securitylab.github.com/ai-agents, github.com/manual/gh_copilot. A claimed page can correct any of them.
Claiming is free and changes nothing in the data. A claimed page shows a verified contact who is told when each edition publishes and when GitHub Copilot's standing changes by more than the noise floor; the right to propose corrections to the vendor table, meaning names the judge wrote that should or should not read as GitHub Copilot, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.
A new claim receives the current edition's vendor brief for GitHub Copilot by email, built from the raw record of the edition. It shows:
AI models recommend GitHub Copilot first in AI coding assistants this edition. Each badge says so in the buyer's words, names the edition, and links to the standing. The next edition issues a new badge; this one stays true as a record of October 2026.
Badges read on light and dark pages. Alt text carries the claim, the category, the edition and the index, so it stays a citation where the image does not load.