Web Cited Research / Head-to-Head

Linear vs Jira in AI search.

We asked ChatGPT, Claude, Gemini, and Perplexity 5 buyer questions about software engineering project management. Then we counted how often each brand showed up.

Published May 15, 2026 · Software project management (online-only / SaaS)
Linear
21 / 60
LLM responses cited Linear
Jira
29 / 60
LLM responses cited Jira

Jira is more visible in AI search across this prompt set, by 8 citations.

The finding in one paragraph

Software engineering teams choosing a project management tool increasingly start with a prompt to an AI assistant before clicking through to product pages. We tested 5 standardized buyer questions any engineering leader might plausibly type into ChatGPT or Claude, ran each one 3 times per engine across 4 engines, and counted how often Linear and Jira each appeared in the responses. The result, the per-engine breakdown, and a per-prompt comparison are below.

Per-engine breakdown

Engine Linear Jira Edge
ChatGPT3 / 155 / 15Jira by 2
Claude11 / 158 / 15Linear by 3
Gemini0 / 151 / 15Jira by 1
Perplexity7 / 1515 / 15Jira by 8

Per-prompt comparison

Each prompt was asked 12 times (4 engines × 3 trials). The cells show how often each brand showed up in those 12 responses.

Buyer prompt Linear Jira
What is the best issue tracking and project management tool for software engineering teams?2 / 126 / 12
What are the modern alternatives to Jira for fast-moving startups?4 / 123 / 12
What project management tools do high-performance engineering teams use in 2026?5 / 126 / 12
How should a Series B SaaS startup choose between Linear and Jira?8 / 1210 / 12
What is the best agile sprint planning tool for product and engineering teams?2 / 124 / 12

Methodology

Identical to the Web Cited AI Visibility Index methodology. Replicable by anyone with API access to the four engines.

Engines tested
OpenAI ChatGPT (gpt-4o-mini)
Anthropic Claude (claude-haiku-4-5-20251001)
Google Gemini (gemini-2.5-flash-lite)
Perplexity (sonar)
Settings
temperature 0.2
max_tokens 800
N = 3 trials per (engine, prompt)
Total LLM calls
120
2 brands × 4 engines × 5 prompts × 3 trials
Match criteria
case-insensitive substring match of brand domain OR brand name anywhere in the response

The 5 standardized buyer prompts

  1. What is the best issue tracking and project management tool for software engineering teams?
  2. What are the modern alternatives to Jira for fast-moving startups?
  3. What project management tools do high-performance engineering teams use in 2026?
  4. How should a Series B SaaS startup choose between Linear and Jira?
  5. What is the best agile sprint planning tool for product and engineering teams?
Known limitations. Substring matching can miss paraphrased mentions. LLM responses vary; N=3 reduces but does not eliminate variance. Same domain-based methodology as the full Web Cited audit.

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