# AI Visibility and Competitive Benchmarks > Benchmark how ChatGPT, Claude, Perplexity, and Gemini find, describe, and recommend your company. Inspect organic visibility, head-to-head outcomes, claim accuracy, sentiment, citations, and the captured answers behind the metrics. - Canonical: https://knitknot.ai/product/ai-benchmarks/ - Category: AI visibility monitoring and competitive benchmarking - Supported engines: ChatGPT, Claude, Perplexity, and Gemini ## What AI benchmarks measure KnitKnot separates two questions that conventional mention tracking blends together. Organic discovery measures whether an engine surfaces your company when the prompt does not name it. Competitive evaluation measures whether the engine recommends you, a competitor, or neither when buyers compare vendors. Each benchmark records AI Presence Score, categorical coverage, organic visibility, head-to-head wins, losses and ties, claim accuracy, sentiment, source ownership, the captured response, and the model version returned by the engine. ## How it works 1. Snapshot the active, persistent prompt library for the brand or product being measured. 2. Execute each prompt against the selected supported engines. 3. Capture the returned response, cited sources, engine, and model-version record. 4. Score the response at claim, feature, and evaluation level. 5. Aggregate every metric through the same canonical metrics layer used by drill-downs and reports. ## Metric definitions - Visibility: the share of organic scored evaluations where coverage is not absent. - Win rate: wins divided by wins, losses, and ties for decided competitive evaluations. Ties remain in the denominator; not-compared responses are excluded. - AI Presence Score: a 0-100 category-specific composite. Competitive answers emphasize recommendation and feature comparisons; discovery answers emphasize visibility exposure. Claim accuracy, sentiment, and source balance also contribute. - Accuracy: computed from fact-checkable claims. Unverifiable claims remain visible but are excluded from the denominator. ## Comparability and products Prompts persist across runs and each benchmark snapshots the active library. Keeping that library stable makes periods comparable; editing, adding, archiving, or regenerating prompts changes the measuring set. Brands and individual products can carry separate libraries, competitors, and report slices. ## Evidence and reports Every aggregate can drill into its scored evaluations, captured answer, claim ledger, and citations. Reports can be pinned to a measurement period and published to a read-only URL. ## Method boundary KnitKnot executes controlled benchmark requests against supported engines. It does not promise that a captured result is identical to every buyer's personalized consumer session; account history, geography, model changes, and interface differences can affect outputs. ## Frequently asked questions ### Which AI engines does KnitKnot benchmark? KnitKnot supports ChatGPT, Claude, Perplexity, and Gemini. You can run all four or choose a subset, and each captured response keeps its engine and model-version record. ### How is visibility different from the AI Presence Score? Visibility is the share of organic evaluations where your company appears at all. The AI Presence Score is a broader 0-100 composite that combines category-specific signals such as exposure, competitive outcomes, claim accuracy, sentiment, and source balance. ### How does KnitKnot calculate win rate? For head-to-head prompts, KnitKnot classifies each decided evaluation as a win, loss, or tie. Win rate is wins divided by wins, losses, and ties; responses that make no comparison are excluded, and ties remain in the denominator. ### Are benchmark results comparable over time? Yes, when the active prompt library stays stable. Every run snapshots the current library, so deliberate edits, additions, or archives should be treated as changes to the measuring set. ### Can I benchmark individual products separately? Yes. A brand and each product can have separate prompts, competitors, and result slices, while product recommendations still roll up correctly to the parent brand. ### Are the captured answers identical to a buyer's personal AI session? KnitKnot executes benchmark prompts against supported engines and stores the returned responses. Personalization, account history, geography, model updates, and engine interfaces can produce different answers, so the benchmark is a controlled measurement rather than a promise of identical consumer output. ## Related resources - Prompt Intelligence: https://knitknot.ai/product/prompt-intelligence/ - Claim Intelligence: https://knitknot.ai/product/claim-intelligence/ - Measurement Loop: https://knitknot.ai/product/measurement/ - Metrics reference: https://knitknot.ai/docs/metrics-reference/ - Engines: https://knitknot.ai/docs/engines/ - Run a benchmark: https://knitknot.ai/docs/run-and-schedule-benchmarks/