Amazon Q Developer vs Tabnine
Zero of fourteen models named Amazon Q Developer first on the direct prompt; zero named Tabnine. Amazon Q Developer was named by thirteen of the fourteen models and Tabnine by eleven and Amazon Q Developer carries 38 labels and Tabnine 33, so the shares are not directly comparable.
Named in two categories this edition.
Named in two 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
Amazon Q DeveloperFirst choices, of fourteen modelsTabnine
Across every category in the October 2026 Edition, Amazon Q Developer and Tabnine were named in the same answer seventy-two times, of the 112 answers naming Amazon Q Developer and the 119 naming Tabnine. In those answers Tabnine took the first choice eight times and Amazon Q Developer 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 4.1 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 | | | | | |
Amazon Q Developer Tabnine first choice named as an alternative argued againstblank: not namedEach cell is one answer, Amazon Q Developer on the left and Tabnine on the right.The direct prompt
Neither was the first choice, one was named
4 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
10 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%#4 of 7Amazon Q Developer0%#6 of 7Amazon Q Developer4%#4 of 9Tabnine2%#5 of 9Tabnine9%#2 of 9Amazon Q Developer7%#3 of 9What 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
What the models said about Tabnine
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.
“on-prem / local models such as Tabnine are repeatedly called out as the privacy champion because your code never leaves your infrastructure” Muse Glimmer 30B · negative prompt · first choice
“Prefer privacy-first tools like Tabnine (which can run locally/air-gapped and is trained only on permissively licensed code)” DeepSeek V4 Flash · negative prompt · first choice
“I'd start with Amazon CodeWhisperer or Tabnine since they're free” MiniMax M2.5 · budget 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.